Epic has announced an expansion of its suite of artificial intelligence tools aimed at enhancing support for outpatient clinicians. The newly introduced feature, known as Ergo Visit, aggregates patient data from Epic’s electronic health records along with other relevant sources to provide a summarized view for healthcare providers, as revealed during the company's annual meeting in Wisconsin on Tuesday. Ochsner Health, based in Louisiana, is the first organization to implement this tool, with five other physicians outside the system currently testing it.

Garrett Adams, Epic's senior vice president of research and development, explained that the vision behind Ergo Visit was to reimagine the outpatient visit, as if they were starting from scratch with today’s technological advancements.

Ergo Visit represents the latest addition to Epic's expanding range of AI functionalities integrated into its electronic health record system. This suite also includes features like a clinical notetaker, a revenue cycle automation tool, and a patient-centric AI assistant.

According to Epic, Ergo Visit is designed to enable clinicians to focus more on their interactions with patients instead of being preoccupied with record-keeping. The tool activates before the appointment, leveraging Epic’s AI capabilities to highlight pertinent topics for discussion, such as the patient's reason for the visit and any upcoming vaccinations. Following the consultation, it can assist with drafting notes and identifying key issues for the clinician’s attention.

The tool's real-world application began on August 3, with four clinicians at Ochsner Health utilizing its features. Dr. Stephen Lambert, a primary care physician at the facility, likened the tool's support to a GPS system, which, while users can navigate familiar paths, is capable of guiding them more efficiently.

Currently, Epic’s AI tools support approximately 1.4 million clinicians within its electronic health record system each month. Despite Epic's stronghold on the EHR market, holding 42% of the acute care segment for 2024, competing companies are also adopting AI innovations to enhance their offerings. Notably, Oracle Health recently launched its AI-enabled patient portal in the U.S., fully integrated with its EHR system.

During its annual meeting, in addition to introducing Ergo Visit, Epic shared updates on various products, including Agent Factory—a platform for creating custom AI agents—and Curiosity, a predictive tool designed to deliver insights related to hospital readmissions and patient outcomes.

Epic aims to enhance outpatient visits through the introduction of a new AI tool.

A bright sunlit outpatient clinic reception area: a mid-career adult patient speaks with a nurse at a sleek desk, while a large wall-mounted screen shows the Epic logo and an AI-powered appointment schedule—color-coded cards smoothly flowing across the display. Warm natural light pours through floor-to-ceiling windows, casting soft shadows on minimalist medical charts and data-visualization icons integrated into the scene.

Epic targets outpatient visits with new AI tool - Healthcare Dive

- Epic launches Ergo Visit to summarize patient information for outpatient clinicians effectively.  
- Ochsner Health is the first to implement Ergo Visit, enhancing clinician-patient interactions.  
- 1.4 million clinicians use Epic's AI tools monthly, improving efficiency in healthcare delivery.

Dive Brief:

Epic is broadening its belt of artificial tools, launching a new feature meant to assist outpatient clinicians.

The tool, called Ergo Visit, compiles patient information from Epic’s electronic health record and other areas to summarize data for clinicians, executives said during the company’s annual meeting in Wisconsin on Tuesday. Louisiana-based health system Ochsner Health is the first to implement the tool, Epic said, though it’s also being piloted by five additional physicians outside the system.

“Our goal was to redesign the office visit as we would build it if we were starting from scratch, with all of the technology we have available today,” Garrett Adams, senior vice president of research and development at Epic, said in a statement.

Dive Insight:

The outpatient visit tool is the latest in a growing suite of AI capabilities Epic has integrated into its EHR in recent years, which include a clinical notetaker, a revenue cycle automation product and a patient-facing AI assistant.

Epic says that Ergo Visit should help clinicians stay present during conversations with patients, rather than having to constantly look through records. The tool starts working before the visit begins, Epic said. It uses Epic’s existing AI capabilities to identify relevant topics for the clinician, such as the reason for the visit given during scheduling or vaccinations that are due. After the visit, it can then draft notes and identify issues for the clinician to review.

Four clinicians at Ochsner Health started using the tool on Aug. 3, Epic said.

"It's like the way a GPS can direct you around traffic. You know how to get there, but this tool can help you get there the fastest," Dr. Stephen Lambert, a primary care physician at Ochsner, said in a statement.

Overall, 1.4 million clinicians are now assisted by AI within Epic’s EHR every month, according to the company.

Although Epic continues to dominate the EHR sphere, accounting for 42% of the 2024 acute care EHR market, other EHR companies are turning to AI to give them an edge. Last week, Oracle Health announced that its AI-backed patient portal is now generally available in the U.S., and is fully integrated with Oracle’s EHR.

In addition to the launch of Ergo Visit, Epic gave several product updates during its annual meeting, including new details about Agent Factory, an integrated platform for creating custom AI agents, as well as Curiosity, a predictive tool that garners insights on things like hospital readmission rates and patient outcomes.

Epic targets outpatient visits with new AI tool

A recent survey conducted by the Pew Research Center reveals a significant demand for transparency regarding the use of artificial intelligence (AI) in healthcare. Over 70% of nearly 5,000 American adults surveyed emphasized the necessity for healthcare providers to disclose when AI technology is utilized in medical decisions, such as interpreting medical scans, diagnosing health conditions, and clarifying lab results.

Patient awareness extends beyond core functions; a majority of respondents also indicated a desire to be informed when AI is employed for tasks like note-taking during appointments, managing prescription refills, or scheduling visits.

Trust plays a critical role in shaping public perception of AI in healthcare, according to Andrew Crawford, senior counsel of data and privacy at the Center for Democracy and Technology. While the information that physicians input into AI systems—be it for scribing or diagnostics—is subject to the privacy regulations of the Health Insurance Portability and Accountability Act (HIPAA), it’s crucial that patients comprehend these safeguards and feel confident that their data isn’t being exploited for advertising purposes or sold to third-party data brokers.

Crawford stressed the importance of providers communicating the tools they utilize, particularly innovative technologies like AI, so patients can make informed choices about their care. Nearly half of those surveyed expressed uncertainty about whether AI is utilized in their healthcare settings.

To foster trust, patients should feel empowered to inquire about the role of AI in their treatment, its functioning, the extent of the physician's involvement, and the options available should they prefer to avoid AI applications in their care.

An ongoing class action lawsuit against Sutter Health and Memorial Care underscores patient concerns regarding AI’s role in recording sensitive patient-provider interactions. The lawsuit alleges that medical personnel used AI tools to document confidential communications without gaining prior consent, potentially breaching state and federal laws.

Abridge AI, the clinical documentation service at the heart of these legal challenges, is currently employed by numerous health systems across the country. For instance, Kaiser Permanente implemented it in 40 hospitals and over 600 medical offices in 2024, while the Mayo Clinic incorporated it for roughly 2,000 clinicians serving over 1 million patients in early 2025.

Kaiser Permanente asserts that their usage of the tool mandates patient consent, and that all AI-generated notes will undergo human review before being added to medical records.

There are also concerns about the accuracy of AI systems, as Crawford noted. AI scribes can enable physicians to focus more intently on patient conversations, but inaccuracies in transcription could jeopardize patient safety through misdiagnoses or incorrect prescriptions. Crawford urged that clinicians using these technologies, even for administrative tasks, must ensure their accuracy and undergo thorough testing and evaluation, both prior to and following their deployment, to confirm they serve the intended purpose effectively.

Poll Reveals Patients Desire Transparency About AI in Healthcare

A bright, sunlit clinic waiting room: an adult patient sits at a small table reading a consent form boldly titled “AI Diagnostics Disclosure,” with a subtle graphic sidebar of circuit-like lines. A friendly doctor stands beside them, holding a tablet and offering a reassuring smile. On the wall behind, a medical chart’s heartbeat line gently morphs into a faint circuit pattern. Warm, natural light floods the scene, conveying transparency and trust.

Survey: Patients Want Disclosure on AI Use in Healthcare - bankinfosecurity.com

- Over 70% of Americans want clear disclosure of AI usage in healthcare settings.  
- Many patients are unaware of AI's role in their medical experiences and decisions.  
- Trust in AI's accuracy is crucial, impacting patient comfort and potential health outcomes.  

Artificial Intelligence & Machine Learning , Data Privacy , Data Security

Survey: Patients Want Disclosure on AI Use in Healthcare

More Than 70% Say AI Use in Any Medical Scenario Should Be Disclosed

Image: Shutterstock

Most Americans want transparency about how doctors use artificial intelligence in their health care, but many feel they have little say in how AI is brought into vulnerable moments of their lives, shows a June survey of almost 5,000 U.S. adults.

See Also: A Darkening Landscape: AI, Friend and Foe of Cyber Resilience

More than 70% of Pew Research Center survey participants said it is extremely important for healthcare providers to disclose AI use in diagnostic decisions, such as analyzing medical scans, diagnosing conditions and explaining lab results.

Even outside core medical functions, the majority of participants said they want to be told when AI is used to take notes during an appointment, order prescription refills from a pharmacy or schedule medical visits.

Trust, above all, shapes Americans' views on AI in healthcare, said Andrew Crawford, senior counsel of data and privacy at the Center for Democracy and Technology.

Patient information that doctors enter into AI systems such as an AI scribe or a diagnostic tool is covered by the Health Insurance Portability and Accountability Act privacy rule, but patients need to understand those protections and trust that their data won't be used for advertising or sold to data brokers, Crawford said.

"So knowing what tools providers are deploying, especially new tools like AI, is really important. And then patients can make sure that they're comfortable with those choices," Crawford said.

Nearly half of people surveyed said they are not sure if AI is used in hospitals or doctors' offices at all.

"People should have more confidence about when and where their healthcare providers are deploying AI tools," Crawford said. Then, patients can build trust by asking what role AI plays, how it works, what role the doctor retains and what alternatives exist if they choose not to involve AI.

An ongoing class action lawsuit against Sutter Health and Memorial Care highlighted patients' discomfort with AI tools recording and transcribing conversations containing sensitive personal information without clear notice or consent.

Plaintiffs alleged that medical staff at the defendants' facilities used AI to record confidential physician-patient communications without asking for permission, in violation of state and federal law (see: Lawsuit: AI Illegally Recorded Doctor-Patient Encounters).

Abridge AI, the real-time clinical documentation platform at the center of the lawsuits, has been adopted by numerous health systems nationwide. Kaiser Permanente deployed it across 40 hospitals and more than 600 medical offices in 2024 and Mayo Clinic rolled it out to about 2,000 clinicians serving over 1 million patients in early 2025.

Kaiser Permanente said the tool requires patient consent, and AI-typed notes will be reviewed by a human before being entered into medical records.

Patients may be concerned about AI's accuracy, Crawford said. Having an AI scribe can help doctors focus more on the present conversation but if the tool transcribes incorrectly, it could cause potential harm through an incorrect diagnosis or prescription.

"Doctors deploying these tools, even the administrative tools, have to make sure that the tools are fit for purpose, are accurate, and that they are testing them and evaluating them not only before they deploy them, but once they're deployed as well, to make sure that they're functioning as the doctor wants them to," Crawford said.

Survey: Patients Want Disclosure on AI Use in Healthcare

This special collection focuses on enhancing and supporting research related to Sustainable Development Goal 3 (SDG3) and Sustainable Development Goal 10 (SDG10), specifically within the field of healthcare and artificial intelligence (AI).

While the concept of human-centered AI in healthcare has gained traction, concrete evidence regarding its practical outcomes is still lacking. This collection seeks to explore the dimensions of human-AI collaboration in real-world clinical settings, encompassing the perspectives of clinicians, patients, clinical interactions, and broader health systems. By taking a comprehensive sociotechnical approach, we examine how AI influences clinical work, organizations, and healthcare systems, questioning whether it enhances, disrupts, or fundamentally alters these elements. The concept of co-intelligence is a central theme, emphasizing the vision of AI as an intellectual collaborator that aids clinical judgment rather than replaces it. We prioritize original research that is based on empirical data and contributes methodologically, theoretically, or in terms of design, rather than simply presenting descriptive accounts.

Areas of interest include, but are not limited to, the following dimensions, ranging from individual (micro) and care delivery (meso) to societal impacts (macro):

1. The impact of human-AI collaboration on clinician performance, behavior, decision-making processes, and clinical pathways, focusing on how co-intelligence is facilitated through human-in-the-loop workflows where clinicians supervise AI involvement.

2. Human factors that influence clinician-AI interactions, such as automation bias, user-friendliness, and AI’s cognitive impact on clinical decision-making. This also includes examining the long-term effects of AI on clinical expertise and professional development, such as skill acquisition for trainees and skill retention among experienced practitioners.

3. Issues related to professional identity, moral challenges, accountability, and the wellbeing of clinicians working in AI-enhanced environments.

4. Patient experiences with AI in healthcare settings, including interactions with AI-powered decision-making tools and pathways, evaluations of patient-facing AI applications, and the collaborative design of these systems with clinicians and patients. An exploration of how the doctor-patient relationship is evolving in the context of AI is also essential.

5. The design of clinical workflows to optimize AI-assisted diagnosis and care management, clarifying the roles of various clinical professionals in delivering AI-enabled healthcare.

6. Real-world examples of human-AI collaboration, taking into account regulatory, ethical, health equity, economic, and governance perspectives for responsible AI deployment, particularly in diverse and resource-limited environments.

7. Strategies for assessing co-intelligence and its broader impacts on health systems, as well as methods to evaluate and integrate these collaborations into existing healthcare frameworks.

8. Engaging participatory and co-design methodologies throughout the AI lifecycle, enabling the involvement of clinicians, patients, and affected communities in the design, evaluation, and application of clinical AI systems.

We are not seeking research focused solely on model accuracy and will not consider:

- Studies predominantly emphasizing model performance or clinician comparisons.
- Simulated case studies where AI-assisted decisions are made in isolation, as these results may not translate to real-world practice.
- Usability assessments that fail to provide theoretical, evidential, or design-related insights.
- Broad surveys of clinician or patient attitudes towards AI without an exploration of human-AI interaction.
- Assertions regarding real-world evaluations of AI's impact on health behaviors that do not systematically analyze the interaction with AI.

We encourage submissions that present multifaceted perspectives, including those from human-computer interaction, information systems, regulatory science, and critical data studies, specifically focused on the foundational aspects of human-AI collaboration. We are particularly interested in research that measures this cooperative dynamic, identifies optimal methods for capturing human-AI interactions, and investigates secondary metrics reflecting the societal implications of AI in clinical contexts.

We welcome a variety of research approaches, including quantitative, qualitative, and mixed-methods studies, with examples such as:

- Quantitative assessments of human-AI collaboration in practice, including stepped-wedge and pragmatic trials, hybrid effectiveness-implementation designs, and interrupted time series evaluations surrounding AI deployment and removal.
- Behavior and cognitive studies focusing on real-world contexts, where tasks involve measurable outcomes beyond mere accuracy, including cognitive load assessments and longitudinal analysis of skill growth post-AI withdrawal.
- Qualitative work encompassing ethnographic studies, video-based interaction analyses, and user-centered co-design research that includes interviews and iterative qualitative investigations across different stages of AI implementation.

Healthcare Collaboration Between Humans and AI: Practical Evidence

A bright, naturally lit modern hospital consultation room: a doctor in a crisp white coat leans over a sleek touchscreen monitor displaying high-resolution medical scans alongside colorful AI-generated charts and risk-assessment overlays. She points thoughtfully at a highlighted anomaly, while an adult patient on the examination table looks on with relief. Subtle digital data visualizations float on the screen—no holograms, just clear evidence of human expertise amplified by intelligent analytics.

Human-AI Collaboration in Healthcare: Evidence in Practice - Nature

- The collection emphasizes human-AI collaboration's impact on clinical practice and decision-making.
- Topics include clinician performance, patient experience, and the ethics of AI in healthcare.
- Research should focus on empirical evidence and methodological contributions beyond AI model accuracy.

Collection

This Collection supports and amplifies research related to SDG3 and SDG10.

Human-centered artificial intelligence (AI) is now a widely stated principle in healthcare, but evidence for what it delivers in practice remains scarce. This special collection asks how human-AI collaboration should be conceptualised, evaluated, and governed in real-world clinical care, from the individual clinician and patient to the clinical encounter and the wider health system. We take a deliberately broad, sociotechnical view of collaboration, spanning the ways AI reshapes clinical work, organisations and systems, and whether it augments, disrupts, or reconfigures them. Within this, we give attention to co-intelligence and human-AI hybrids: the aspiration that AI operates as an intellectual partner augmenting clinical judgement rather than replacing it. For this Collection, we prioritise empirical work grounded in real world evidence - studies that make a methodological, theoretical, or design contribution, rather than simple description alone.

Topics of interest include but are not limited to the micro (clinicians, patients), meso (care delivery), and macro (society and systems), or cut across these themes:

How human-AI collaboration shapes clinician performance, behaviour, decision-making and the clinical pathway, including evaluating how co-intelligence is structured through human-in-the-loop workflows in which humans oversee AI

Human factors in clinician-AI collaboration, including automation bias, usability and the cognitive effects of AI on clinical reasoning and decision making; longitudinal effects of AI on clinical expertise and professional practice (e.g. skill acquisition in trainees, skill maintenance and drift among established clinicians, performance measured following AI withdrawal)

Professional identity, moral distress, accountability, and clinician wellbeing in AI-mediated practice

Patient experience of and interaction with AI-mediated care and decision-making, including patient experience of AI-mediated pathways, patient interaction with LLMs, evaluation of patient-facing AI tools, and co-design of clinical AI systems with clinicians and patients; evaluating how the doctor-patient relationship is changing.

Clinical workflow design for AI-supported diagnosis and management, clinical organisation, and the roles of different clinical staff in AI-mediated care delivery

Discussion of relevant real-world implementation of human-AI collaboration from a regulatory, health equity, ethics, medicolegal, health economic, health policy and governance approaches for responsible deployment across diverse and resource-constrained settings

Methods for evaluating co-intelligence and secondary impacts of how co-intelligence affects the wider health system/ecosystem, and how health systems should evaluate and integrate this

Participatory and co-design approaches across the AI lifecycle, involving clinicians, patients, and affected communities in the design, evaluation, and deployment of clinical AI systems.

We are not looking for model accuracy in isolation and will not consider:

Studies where the principal contribution is model performance or benchmarking against clinicians

Single-shot vignette or simulated case studies where clinicians make one AI-assisted decision and the resulting reliance estimate is unlikely to generalize to practice

Usability evaluations that don't offer a theoretical, evidential, or design contribution

General surveys of patient or clinician attitudes toward AI

Claims of real-world evaluation of AI-influenced changes in health behaviour or decision making that do not discuss or measure the human-AI interaction itself

We invite translational, multi-stakeholder perspectives; including work in human–computer interaction, information systems, regulatory science, and critical data studies, that address the foundational evidence of human-AI co-collaboration. We are particularly interested in research that evaluates and measures this synergy, identifies methods to optimally capture human-AI interaction, and explores secondary or societal metrics that reflect the effects of AI on humans in clinical practice.

We welcome a mix of quantitative, qualitative, and mixed-methods research; see examples below:

Quantitative evaluation of human-AI collaboration in deployment: stepped-wedge and pragmatic trials; hybrid effectiveness–implementation designs; interrupted time series around go-live and withdrawal; difference-in-differences across deployed and non-deployed sites; regression discontinuity at AI-determined thresholds

Behavioural and cognitive studies: reader and human subjects studies in real-world evaluation, tasks carrying consequence, time pressure, case mix, or workflow context, with at least one reported outcome beyond an accuracy delta; cognitive load measurement; eye-tracking and process tracing; longitudinal skill trajectories assessed with the AI withdrawn

Qualitative and interpretive research: ethnography and organisational workplace studies; video-based interaction analysis and conversation analysis of consultations in which AI is present; HCI user and co-design studies, including interviews, contextual inquiry; longitudinal qualitative research across the deployment trajectory

Human-AI Collaboration in Healthcare: Evidence in Practice

A newly proposed bill mandates that healthcare decision-making be firmly under the authority of doctors or nurses, even in situations where artificial intelligence (AI) plays a role in guiding care. Additionally, the legislation prohibits healthcare providers from utilizing AI for tasks that are legally required to be performed by licensed professionals.

This bill was backed by the California Nurses Association, the same organization whose members recently protested at Kaiser hospitals due to concerns regarding the implementation of AI technology.

Opposition to the bill came from various hospital organizations, health plans, and technology firms. They argued that the bill's broad definition of AI could encompass tools that are already regulated and commonly used in the healthcare sector.

Last week, the Legislature approved the bill, which will become law in January, pending the signature of Governor Newsom.

This legislation may impose restrictions on AI in healthcare.

A dimly lit legislative chamber with warm tungsten light casting long shadows over a heavy wooden desk. On the desk, a polished gavel lies next to a rolled-up bill titled “AI in Healthcare Act.” A sleek metallic robotic arm clutching a stethoscope is ensnared in bright red tape. Behind the desk, a single U.S. flag stands solemnly, its fabric catching the faint glow.

This bill could put limits on AI in healthcare - KALW

- The bill mandates doctors or nurses to make final decisions on patient care.  
- California Nurses Association sponsored the bill amid protests against AI use in hospitals.  
- Hospital groups oppose the bill, arguing its AI definition could hinder existing technologies.  

The bill would require a doctor or nurse to have the final say in a patient's care, even when an AI system helps guide it. It would also bar health providers from using AI to do work the law says must be done by a licensed professional.

The bill was sponsored by the California Nurses Association, the same union whose members picketed Kaiser hospitals this week over the use of AI.

Hospital groups, health plans, and tech companies opposed the bill. They say its definition of AI is too broad, and could sweep in tools already regulated and widely used.

The Legislature passed the bill last week. The law takes effect in January if Governor Newsom signs it.

This bill could put limits on AI in healthcare

The healthcare sector stands on the brink of a significant transformation brought about by advancements in artificial intelligence (AI). While the pace of progress has been impressive, many stakeholders argue that the anticipated returns on investment remain elusive. Although healthcare organizations are increasingly investing in AI to enhance care delivery efficiency, only a handful have reported the desired financial or operational improvements. The disconnect between investment and actual impact is not rooted in technology itself; rather, it stems from the foundational architecture of healthcare organizations. How these entities navigate this challenge will determine whether AI alleviates the burdens faced by clinicians or simply adds another layer of expense and complexity to already strained systems.

AI has proven effective in specific areas such as documentation, coding support, and streamlining administrative tasks. However, isolated enhancements often fail to drive comprehensive transformation across the enterprise when they are merely added onto outdated and inefficient workflows without a fundamental redesign of care delivery practices.

The challenge of layering additional tools

The instinct to build upon existing frameworks in healthcare is understandable. Decision-makers often enhance Electronic Health Record (EHR) workflows with new tools. Unfortunately, many contemporary digital solutions complicate legacy processes. This layering strategy is akin to attempting to fit advanced software into a system designed decades ago, resulting in increased burdens rather than easing them. When AI-powered software is introduced atop antiquated workflows, it merely shifts challenges onto the end-users, typically clinicians. Additional administrative duties detract from the time clinicians can dedicate to patients, and merely automating ineffective workflows results in a faster, pricier version of the same issues.

For real change to occur, healthcare leaders must shift away from merely adding AI tools on top of outdated systems and instead rethink their workflows entirely.

Consequences of maintaining the status quo

There’s no specific tipping point that signals the over-layering of legacy systems; rather, the costs accumulate gradually. This manifests in more platforms and point solutions, each aiming to automate healthcare in fragmented ways. Beyond escalating expenses, several indicators signal a deteriorating system:

1. Clinical attrition: New AI tools are supposed to ease the workload for clinicians, yet one study indicated that fewer than half of these tools have improved their productivity in the last two years. Tools that create more challenges than solutions can lead to clinician frustration and even attrition from the profession.

2. Decline in capacity amid increasing demand: As the population ages, the need for care continues to climb. If AI tools complicate existing processes, clinicians will find even less time for patient interactions.

3. Patient dissatisfaction: At the heart of healthcare is the patient experience. Overworked clinicians can compromise patient care quality, leading to a decline in patient satisfaction.

Rethinking the role of AI in healthcare

In medical practice, addressing superficial symptoms without tackling root causes is deemed ineffective. Despite this, healthcare organizations often resort to quick software fixes to mask workflow issues. Leaders in healthcare must accurately diagnose the underlying problems and reassess which IT metrics truly convey success. Simply measuring adoption rates may obscure the reality; the real value will emerge from reducing friction in core workflows. Metrics that matter most include the elimination of redundant tasks, reclaimed clinical capacity, patient engagement, and clinician satisfaction, which should guide the evaluation of AI implementations.

Healthcare finds itself at a pivotal moment. The future will belong not to those who amass the most technology, but to those who harness it to reduce workloads. The current advancements in AI present a formidable opportunity for the sector to innovate, reconfigure, and renew itself.

Organizations that undertake the comprehensive redesign of care delivery workflows—rather than merely stacking on new technology—will unlock the true potential of AI. The time to rebuild infrastructures for improved care is now, rather than simply bolting on solutions.

Richard Atkin, CEO of Greenway Health, has a wealth of experience spanning 25 years in software leadership. He is dedicated to ensuring that the organizations he guides prioritize customer-centric strategies. Atkin’s background includes roles in the defense sector and healthcare technology companies, reflecting his commitment to enhancing customer outcomes and achieving excellence in product delivery. He holds a Bachelor of Science with Honors in Physics and Engineering from Bangor University and an MBA from Imperial College, London.

The Turning Point in Healthcare: AI Can't Resolve Outdated 1990s Technology

A dimly lit, shadowy hospital records room lined with dusty 1990s CRT monitors and overflowing metal file cabinets; a sleek silver robotic arm hovers over a flickering green-on-black DOS screen, sparks flying as broken code fragments disintegrate into darkness; a single overhead bulb casts harsh shadows, underscoring the clash between cutting-edge AI and decaying legacy tech.

The Healthcare Inflection Point: AI Can’t Fix 1990s Technology - MedCity News

- Healthcare organizations are investing in AI but struggle to see expected financial outcomes.  
- Layering AI tools onto outdated workflows complicates systems instead of improving efficiency.  
- Redesigning care delivery workflows is essential for realizing AI's true potential in healthcare.  

Healthcare has reached an inflection point with artificial intelligence. Advances have been swift. But many would argue that returns on investment are less evident. Healthcare organizations are exploring AI to improve efficiency of care delivery, yet few report seeing the financial or operational outcomes they expected. The gap between investment and impact is not a technology problem. It is an architecture problem, and how healthcare organizations respond to it will determine whether AI becomes the thing that finally relieves clinician burden, or one more layer of cost and complexity stacked on top of systems that were never built to support it.

AI has already demonstrated meaningful value in areas such as documentation, coding assistance, and administrative automation. Yet isolated improvements rarely translate into enterprise-wide transformation when they are layered onto outdated, inefficient workflows instead of being integrated into redesigned care delivery.

The layering trap

It seems logical to build on top of something that is already in place. You start with a familiar foundation and work your way up. In healthcare, decision-makers do this by adding tools onto existing EHR workflows. But many of the digital tools being layered on today are adding complexity to legacy processes. The layering approach is essentially an attempt to apply 2026 technology to a 1990s operating system. When new software, powered by artificial intelligence, is added on top of these legacy workflows, it doesn’t relieve pressure points; it shifts them towards end users, and oftentimes those are clinicians. Every additional administrative task takes time away from patient care. Automating an inefficient workflow simply creates a faster, more expensive version of the same problem.

Healthcare decision-makers need to recognize that this pattern will keep repeating itself until organizations stop adding AI on top of legacy systems, and start reimagining entire workflows.

The cost of inaction

The breaking point isn’t a single calamity. There is no specific moment that announces, “You’ve layered too much on top of a legacy system.” Instead, the cost shows up gradually, in more platforms, and more point solutions, each trying to automate healthcare piece by piece. Beyond mounting expenses, the following are signs of systemic decline:

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Clinical attrition: Does a new AI tool make life easier for clinicians? One study said less than half of new AI tools over the past two years have made providers more productive. If a tool makes work more arduous, clinicians will feel the burden. If this continues, it can ultimately lead to them leaving the profession.

Capacity drop vs demand rise: Demand for care continues to rise as the population ages. If AI tools make a process more difficult, clinicians will have even less time to focus on patient care.

Patient dissatisfaction: Healthcare is ultimately about patients. If clinicians administering care are overworked and overburdened, patient experience will suffer.

The healthcare AI pivot

In medicine, treating a symptom while ignoring the underlying cause is considered poor practice. Yet, healthcare organizations are increasingly applying software band-aids to cover workflow inefficiencies. Healthcare leaders need to understand how to diagnose the underlying problem. To do that, they should reconsider which healthcare IT metrics are most important. Adoption rates don’t always tell the real story. Value will be realized through reductions in friction within core workflows. Metrics like work eliminated, reclaimed clinical capacity, patient engagement, and provider satisfaction should carry the most weight in determining the benefits from AI innovation.

Healthcare is at an inflection point. Its future doesn’t belong to those with the most technology; it belongs to those who utilize technology to eliminate workload. The current moment in artificial intelligence gives healthcare one of its most significant opportunities to reimagine, re-engineer, and reinvigorate this critical sector.

Organizations that redesign care delivery workflows – not simply layer on more technology – will be the ones that realize AI’s full promise. It’s time to rebuild, not bolt on.

Photo: Irina_Strelnikova, Getty Images

Richard Atkin With 25 years of leadership in software organizations, Richard Atkin, the CEO of Greenway Health, has remained focused on ensuring that the businesses he leads are customer-driven. He believes that a culture of clear focus and alignment with customer needs is the cornerstone of excellence in product delivery, driving improvements in customer outcomes. Richard began his career in the defense industry before transitioning into healthcare technology, where he held roles at Datex-Ohmeda (now part of GE Healthcare), Misys Hospital Systems, Sunquest, and Greenway Health. Throughout his tenure in healthcare IT leadership, Richard has driven team growth, transformation, innovation, and a commitment to customer-focused excellence. Immediately before rejoining Greenway Health as CEO in 2025, Richard served on the Greenway Board of Directors, maintaining a strong connection to Greenway’s mission, vision, and core values. Richard earned a Bachelor of Science with Honors in Physics and Engineering from Bangor University, Wales and an MBA from Imperial College, London.

This post appears through the MedCity Influencers program. Anyone can publish their perspective on business and innovation in healthcare on MedCity News through MedCity Influencers. Click here to find out how.

The Healthcare Inflection Point: AI Can’t Fix 1990s Technology

Artificial intelligence (AI) is poised to reshape the healthcare landscape by generating new value, yet there is a pressing need for nursing to play a central role in governing this transformation. This highlights a critical issue in the healthcare payment system: while nursing contributes substantial clinical and economic benefits, much of this value remains hidden in current financing models.

Nurses engage in a wide range of activities—patient assessments, condition monitoring, complication prevention, care coordination, and patient education—that significantly influence patient outcomes and healthcare resource utilization. However, in most hospital settings, nursing is funded through general facility payments, rather than being recognized as a distinct professional service. Although healthcare institutions receive funding aimed at supporting nursing care, this financing is often not allocated specifically for nursing or reinvested in the nursing workforce.

The introduction of new coding systems for AI-driven clinical analyses sheds light on this issue. If healthcare organizations can establish mechanisms for recognizing and reimbursing algorithmic assessments, nursing can rightfully question why its own evaluations and judgments are not given similar financial recognition.

While the creation of distinct billing codes for nursing services might address some concerns, it does not tackle a more critical issue: how should the healthcare system evaluate, allocate, and manage the value generated by both its workforce and technological advancements?

As AI is integrated more deeply into healthcare, nursing should prioritize four key areas: ensuring transparency, advocating for reinvestment, protecting the workforce, and asserting meaningful governance over AI technologies.

Making the costs and benefits of AI transparent is essential. It is crucial that healthcare organizations not only provide projected efficiencies but demonstrate the actual value generated by AI implementations. This entails breaking down statistics into categories such as gross revenue, net revenue, projected versus verified savings, avoided costs, and improved clinical capacity, in addition to disclosing expenses related to the acquisition, implementation, validation, monitoring, and maintenance of AI technologies.

Moreover, attention must be paid to the implications for clinical practice. For example, while an AI tool that reduces documentation time appears beneficial, if nurses are then tasked with verifying the accuracy of entries and rectifying errors, the initial time savings may diminish. Any effective assessment of AI's value must factor in not only the time saved but also the work created and transferred.

Once verified financial gains or clinical improvements arise from AI, healthcare organizations should plan to reinvest a portion of that value back into patient care. This reinvestment may include support for staffing, staff retention, ongoing education, enhanced work environments, adherence to evidence-based practices, quality improvements, and innovations led by nurses.

It is vital to differentiate between cost and investment. Nursing roles are frequently viewed solely as labor costs on an organizational balance sheet, but they also function as productive assets that help minimize complications, reduce missed opportunities in care, and boost overall patient outcomes.

AI will undoubtedly transform nursing practices, but this shouldn't automatically lead to staff reductions. Organizations should first assess existing patient needs and reallocate resources, rather than simply reducing the workforce. For instance, time saved from documentation could be redirected towards direct patient care or other valuable nursing tasks.

Crucially, nurses must not only bear responsibility for managing the consequences of technologies they did not select but should also have a say in the decision-making process surrounding AI implementations. Their involvement is essential from the selection phase through to evaluation, ensuring that the integration of these tools is aligned with clinical workflows and that the implications for workload and staffing are thoroughly considered.

Thus, the ongoing dialogue regarding AI billing codes presents a significant opportunity for nursing. While advocating for payment mechanisms that enhance the visibility of nursing services is important, the overarching goal should be to establish a healthcare payment system that accurately reflects the investment in nursing, captures the technological value, and holds stakeholders accountable for its application.

Instead of merely asking how much money AI saves, the focus should shift to whether that value is being utilized to bolster the nursing workforce, enhance clinical capacity, and improve patient care—and nurses should have the authority to guide these important discussions. 

Olga Yakusheva, a distinguished professor at Johns Hopkins School of Nursing in Baltimore, MD, emphasizes these critical issues in her advocacy for nursing’s role in the future of healthcare.

AI is generating new value in healthcare, and nursing should play a role in its governance.

Natural morning light floods a bright hospital corridor where a nurse in crisp scrubs holds a tablet displaying warm-toned AI analytics and patient charts. Nearby, a sleek medical scanner bears subtle circuit-pattern engravings that glow softly. The nurse’s focused gaze on the data embodies human oversight guiding AI’s new value in healthcare.

AI is creating new value in healthcare. Nursing should help govern it. | Newswise - Newswise

- Nursing's economic value is often hidden in healthcare financing and needs clearer visibility.  
- AI integration should prioritize nursing authority in governance and decision-making processes.  
- Reinvesting AI-generated gains into nursing resources is essential for improving patient care quality.

AI is creating new value in healthcare. Nursing should help govern it. Source Newsroom: Johns Hopkins School of Nursing



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Newswise — The debate over billing codes for artificial intelligence (AI)-enabled clinical services has exposed a longstanding problem in healthcare payment: Nursing creates substantial clinical and economic value, but much of that value remains difficult to see in the way healthcare is financed.

Nurses assess patients, recognize changes in condition, prevent complications, coordinate care, educate patients and families, and make clinical judgments that influence outcomes and healthcare utilization. In hospitals, however, nursing is generally financed through facility payments rather than identified as a separate professional service. Hospitals receive resources intended to support nursing care, but those resources aren’t separately attributable to nursing or necessarily reinvested in the nursing workforce.

The emergence of proposed coding for clinically meaningful algorithmic analyses makes that problem more visible. If healthcare can develop mechanisms to identify and potentially reimburse an algorithm’s clinical analysis, nursing is justified in asking why comparable nursing assessment and judgment remain financially obscured.

Separate nursing billing codes may be part of the solution. But they don’t address the more fundamental question: How should healthcare measure, allocate, and govern the value created by both its workforce and its technology?

As AI becomes more integrated into care, nursing should focus on four priorities: transparency, reinvestment, workforce protection, and meaningful authority over AI governance.

Make AI’s costs and benefits visible

Healthcare organizations should be required to demonstrate the value AI actually creates rather than relying on projected efficiencies.

That requires distinguishing among gross revenue, net revenue, projected savings, verified savings, avoided costs, and released clinical capacity. Organizations also need to account for what they spend to acquire, implement, validate, monitor, and maintain AI systems, including payments to technology vendors.

Equally important is understanding what happens to clinical work.

Consider an AI tool that reduces nursing documentation time. The recovered time has value, but it isn’t necessarily a financial savings. If nurses have to spend additional time verifying the accuracy of the notes and correcting omissions and inaccuracies, this additional time and cognitive burden can offset the initial time savings, and so it needs to be subtracted to determine actual time saved. After that’s done, if nurses use that time to provide additional patient care, the organization has created clinical capacity. If, instead, staffing is reduced, this may become a financial savings—but one achieved by reducing nursing resources.

Importantly, an algorithm may automate an analysis while creating downstream nursing responsibilities and cognitive workload that includes assessing its relevance, responding to alerts, explaining results to patients, correcting errors, coordinating follow-up, and monitoring outcomes. These downstream impacts must be accounted for.

Any credible calculation of AI’s value must account for work that’s eliminated, created, and transferred.

Reinvest verified gains in patient care

When AI produces verified financial gains or additional clinical capacity, healthcare organizations should determine prospectively how some of that value will be reinvested in patient care.

Investment could include staffing, retention, continuing education, safer work environments, evidence-based practice, quality improvement, and nurse-led innovation.

The distinction between cost and investment matters here.

Nursing is frequently represented on organizational balance sheets primarily as a labor expense. But nursing resources also are productive assets: They contribute to preventing complications, reducing missed care, improving outcomes, and maintaining the clinical capacity of healthcare organizations.

Technology investment shouldn’t come at the expense of the human capital necessary to make that technology clinically useful.

Don’t automatically translate efficiency into fewer nurses

AI will change nursing work. That doesn’t mean every existing task or position should remain unchanged. But automation shouldn’t automatically become a justification for reducing clinical capacity.

Before eliminating nursing positions, organizations should examine unmet patient needs and determine whether released capacity can be redirected.

Time recovered from documentation could support direct patient care. More efficient discharge processes could create additional capacity for patient education, medication reconciliation, and follow-up. Nurses whose work changes substantially could move into care coordination, chronic disease management, population health, AI oversight, or safety monitoring.

The relevant question isn’t simply, “How many nursing hours can AI eliminate?” Rather, it’s, “How can AI allow existing nursing human capital to produce greater value for patients?”

Give nurses authority, not just responsibility

Nurses frequently become responsible for managing the downstream consequences of technologies they had little role in selecting.

That model needs to change.

Nurses should have formal decision-making roles before AI systems are purchased and throughout implementation and evaluation. They should help determine how tools fit clinical workflows, whether they increase or decrease workload, who’s responsible for acting on algorithmic output, how errors and bias are handled, how patients are informed, and how technology affects staffing and skill mix.

Nurses also should have a role in determining how verified financial gains and released clinical capacity are used.

Therefore, the AI billing-code debate presents nursing with a larger opportunity. Nursing can continue pursuing payment mechanisms that make its services more visible, but billability alone shouldn’t define economic value.

The stronger goal is a healthcare payment system that makes nursing investment visible, measures technological value accurately, and creates accountability for how both are used.

If AI creates value, the question shouldn’t focus on how much money it saves. We should ask whether that value is being used to strengthen the workforce, expand clinical capacity, and improve care.

And nurses should have the authority to help answer that question.

Olga Yakusheva, PhD, MSPE, FAAN is a professor at Johns Hopkins School of Nursing, Baltimore, MD.

AI is creating new value in healthcare. Nursing should help govern it.

ALKU has recently expanded its service offerings by acquiring rockITdata, enhancing its expertise in artificial intelligence, digital transformation, data analytics, healthcare IT, and specialized federal health initiatives.

rockITdata is a consulting firm focused on providing technology solutions in AI, digital transformation, and data analytics to both enterprise and public-sector clients.

This strategic acquisition merges rockITdata’s technological expertise and project execution skills with ALKU’s established staffing platform and its access to a skilled workforce in AI and data engineering.

Despite the acquisition, ALKU intends to maintain its core focus on specialized staffing while leveraging the new capabilities to enhance the technology solutions available to its clients.

The partnership aims to facilitate comprehensive end-to-end project execution, particularly for organizations tackling intricate projects in AI, data, and federal healthcare.

Additionally, this deal will bolster ALKU’s Healthcare Information Technology capabilities and broaden its presence in specialized federal healthcare contracts.

For rockITdata, the merger presents opportunities to tap into ALKU’s extensive network of specialized AI and data engineering professionals, aiding in the response to the growing demand within its current operations and enhancing project delivery.

Both companies view this collaboration as a unique approach that integrates specialized talent with consulting and solution delivery in the tech space.

ALKU, based in Andover, Massachusetts, operates seven additional offices across the United States and primarily offers specialized consultants in IT and life sciences sectors.

rockITdata caters to clients in healthcare, life sciences, and government, utilizing proven technologies to drive digital transformation and improve operational efficiencies.

In the words of Andrew Bull, CEO of ALKU, “Our growth at ALKU revolves around unlocking potential for our clients, business, and team. The addition of rockITdata is a thrilling move for us. By combining their technical expertise and project leadership in AI, data, and federal health with ALKU's expansive reach, we can provide a more robust solution to tackle complex challenges for our clients. We’re thrilled to welcome the rockITdata team into the ALKU family.”

Daniel Thode, President of rockITdata, adds, “Our excitement lies in what ALKU is building. They have developed an outstanding platform centered around specialized talent, in-depth market understanding, and solid client relationships. By integrating solutions delivery with AI and federal health capabilities, ALKU can enhance its support for clients and meet increasingly complex demands. We believe this partnership creates a distinctive model that brings specialized expertise to drive technology transformation in both established and emerging markets.”

ALKU Purchases rockITdata to Enhance AI and Healthcare IT Operations

A bright, sunlit hospital IT server room where two senior professionals in crisp lab coats shake hands over a glass console. Behind them, a large ALKU logo etched into the wall catches the morning light. To one side, sleek server racks bear the rockITdata emblem on a brushed-steel panel. Around them, widescreen monitors glow with AI-powered medical analytics—heartbeat waveforms, DNA strands, and predictive care graphs—all under warm natural light streaming through high windows.

ALKU Acquires rockITdata To Expand AI And Healthcare IT Capabilities - Pulse 2.0

- ALKU has acquired rockITdata to enhance its AI and digital transformation capabilities.  
- The acquisition integrates rockITdata’s expertise with ALKU’s staffing and engineering talent.  
- The deal aims to improve project delivery for complex initiatives in healthcare and federal sectors.

ALKU has acquired rockITdata, expanding its capabilities across artificial intelligence, digital transformation, data analytics, healthcare IT and specialized federal health projects.

rockITdata is a technology consulting company that specializes in AI, digital transformation and data analytics for enterprise and public-sector customers.

The acquisition combines rockITdata’s technology solutions and project delivery expertise with ALKU’s specialized staffing platform and access to AI and data engineering talent.

ALKU plans to remain focused on its core specialized staffing model while using the acquisition to broaden the range of technology solutions it can provide to customers.

The combination is expected to enable more complete end-to-end project delivery, particularly for organizations pursuing complex initiatives involving AI, data and federal health.

The deal also expands ALKU’s Healthcare Information Technology capabilities and extends the company’s reach into specialized federal healthcare contracts.

For rockITdata, access to ALKU’s broader pool of specialized AI and data engineering professionals is expected to help the company address increased demand within its existing business and scale project delivery.

The companies believe the combination creates a differentiated model that integrates specialized talent with technology consulting and solutions delivery.

ALKU is headquartered in Andover, Massachusetts, and has seven additional locations across the United States. The company provides highly specialized consultants primarily across IT and life sciences.

rockITdata serves organizations across healthcare, life sciences and government, using commercially proven technologies to support digital transformation and operational improvements.

KEY QUOTES:

“At ALKU, our growth comes down to unlocking potential for our clients, our business, and our people. Bringing rockITdata on board is an exciting move for us. Combining their hands-on technical expertise and project leadership in AI, data, and federal health with ALKU’s reach gives us the ability to offer a more complete solution to solve larger and more complex problems for our clients. We are excited to welcome the rockITdata team to ALKU.”

Andrew Bull, CEO of ALKU

“What excites us most is what ALKU is building. ALKU has created an exceptional platform around specialized talent, deep end-market expertise, and strong client relationships. Bringing solutions delivery across AI and federal health capabilities will expand the ways in which ALKU can support its clients and respond to increasingly complex needs. We believe the combination creates a differentiated model for bringing specialized expertise to support technology transformation efforts across both existing and new markets.”

Daniel Thode, President of rockITdata

ALKU Acquires rockITdata To Expand AI And Healthcare IT Capabilities

EPAM Systems, Inc. is a renowned American company specializing in digital transformation and software engineering services. It offers a wide range of consulting, software development, and IT services, catering to numerous industries on a global scale.

Siemens AG, a prominent German multinational, excels in providing automation, digitalization, and electrification solutions. Its services span various sectors, including industry, infrastructure, and mobility, reaching clients worldwide.

WNS Global Services, based in India, is an established business process management and IT services provider. It delivers a multitude of solutions encompassing customer engagement, finance and accounting, and technology services to enterprises around the globe.

L&T Technology Services Limited, also situated in India, is an engineering and technology services firm that specializes in offering design, engineering, and digital transformation solutions across an array of industries.

Larsen & Toubro Infotech Limited, yet another India-based IT service and consulting organization, provides digital transformation, cloud, and enterprise solutions to clients around the world.

Sopra Steria is a global digital services company with offerings that include consulting, systems integration, and software development, operating across both the United States and international markets.

Cyient, an Indian firm, focuses on engineering and technology services, particularly in digital solutions for the aerospace, defense, and industrial sectors.

In the United Kingdom, the healthcare sector is experiencing a surge in the integration of artificial intelligence (AI) technologies, particularly in areas like diagnostic imaging, clinical decision support systems, administrative automation, and predictive analytics. This trend is evident across both National Health Service (NHS) and private healthcare providers.

The UK’s Medicines and Healthcare products Regulatory Agency (MHRA) and the National Institute for Health and Care Excellence (NICE) have laid down well-defined approval processes for AI medical devices, effectively reducing market entry obstacles and fostering innovation within the UK healthcare AI landscape.

The market for AI in healthcare in the UK is projected to grow significantly, anticipated to reach a value of $110.61 million by 2030, with a compound annual growth rate (CAGR) of 38.6% from 2025 to 2030. The estimated valuation for 2025 stands at $21.63 million, indicating substantial growth ahead.

For this study, a comprehensive methodology was employed to assess the current size of the AI healthcare market. Extensive secondary research was conducted to gather information, which was then validated with industry experts through primary research. Methodologies like top-down and bottom-up approaches were adopted to determine the total market size, followed by detailed market segmentation and triangulation to refine estimates for various segments within the AI healthcare market.

Secondary research relied upon a range of reputable sources, including databases like Dun & Bradstreet and Bloomberg Businessweek, as well as white papers, annual reports, and other official documents. This approach facilitated a thorough understanding of the competitive landscape, enabling insights into key players and market segmentation.

In the primary research phase, qualitative and quantitative data were collected from various stakeholders in the industry. This included interviews with experts and representatives from different segments of the value chain, such as technology developers and industry leaders. The insights gained were critical in identifying market dynamics, including growth drivers, challenges, and competitive strategies.

The complete market engineering involved meticulous calculations and statistical evaluations based on a mixture of approaches. By dissecting overall market trends and leveraging extensive analyses, the report provides a detailed picture of the AI in healthcare market, including future projections.

The market is characterized by the integration of AI technologies such as machine learning, natural language processing, and robotics, aimed at enhancing healthcare delivery and operational efficiency. These applications span numerous use cases, from diagnostic imaging to patient engagement and remote monitoring, enabling significant advancements in the healthcare sector.

Key stakeholders in this space include AI software vendors, service providers, independent software vendors, platform and technology providers, as well as various healthcare institutions and investors.

The primary objectives of this research include defining and forecasting the global AI healthcare market, analyzing market influencers, identifying opportunities for stakeholders, and comprehensively profiling leading market players based on their offerings and competitive strategies. The study encompasses projections for five key regions, including North America, Europe, Asia Pacific, Latin America, and the Middle East and Africa, ensuring a well-rounded understanding of the landscape. Additionally, the study benchmarks market players to evaluate their strategic positioning within the industry.

UK Healthcare Artificial Intelligence (AI) Market Size, Share, Trends, and Growth Analysis Report, 2030

A sleek modern hospital research wing bathed in warm morning sunlight, a diverse team of doctors and analysts gathered around a floating transparent display showing upward-trending charts titled “UK AI Healthcare Market 2030.” A single Union Jack flag hangs on a white wall, medical devices and patient files rest on a nearby table—bright, optimistic, and forward-looking.

UK Artificial Intelligence (AI) in Healthcare Market Size, Share,Trends, Growth Analysis Report, 2030 - MarketsandMarkets

- UK's healthcare market is rapidly adopting AI for diagnostics and clinical support systems.  
- Clear approval pathways by MHRA and NICE are fostering innovation in AI medical devices.  
- UK's AI healthcare market is projected to reach $110.61 million by 2030.  

EPAM SYSTEMS, INC.

EPAM Systems, Inc. is a leading digital transformation and software engineering services company headquartered in the United States. The company provides consulting, software development, and IT services to clients across various industries globally.

SIEMENS AG

Siemens AG is a German multinational conglomerate providing automation, digitalization, and electrification solutions across industrial, infrastructure, and mobility sectors worldwide.

WNS GLOBAL SERVICES

WNS Global Services is an India-based business process management and IT services company offering customer engagement, finance and accounting, and technology solutions to global enterprises.

L&T TECHNOLOGY SERVICES LIMITED

L&T Technology Services Limited is an India-based engineering and technology services company providing design, engineering, and digital transformation solutions across multiple industries.

LARSEN & TOUBRO INFOTECH LIMITED

Larsen & Toubro Infotech Limited is an India-based IT services and consulting company offering digital transformation, cloud, and enterprise solutions to global clients.

SOPRA STERIA

Sopra Steria is a digital services and software company providing consulting, systems integration, and software development services, with operations in the United States and globally.

CYIENT

Cyient is an India-based engineering and technology services company specializing in digital, engineering, and cloud solutions for aerospace, defense, and industrial sectors.

The UK healthcare market is seeing significant adoption of AI in diagnostic imaging, clinical decision support systems, administrative automation, and predictive analytics across NHS and private providers.

The UK's MHRA and NICE have established clear approval pathways for AI medical devices, reducing barriers to market entry and encouraging innovation in the UK healthcare AI sector.

The UK AI in healthcare market is forecast to reach $110.61 million by 2030, growing at a CAGR of 38.6% from 2025 to 2030.

The UK AI in healthcare market is valued at $21.63 million in 2025 and is expected to grow significantly over the forecast period.

RESEARCH METHODOLOGY

The study involved significant efforts to estimate the current size of the artificial intelligence (AI) in healthcare market. Exhaustive secondary research was conducted to collect information on the artificial intelligence (AI) in healthcare market. The next step was to validate these findings, assumptions, and sizing estimates with industry experts across the value chain using primary research. Different approaches, such as top-down and bottom-up, were employed to estimate the total market size. After that, the market breakup and data triangulation procedures were used to estimate the market size of the segments and subsegments of the artificial intelligence (AI) in healthcare market.

Secondary Research

This research study involved the extensive use of secondary sources, directories, and databases such as Dun & Bradstreet, Bloomberg Businessweek, and Factiva; white papers, annual reports, and Companies House documents; investor presentations; and companies' SEC filings. The market for companies offering artificial intelligence (AI) in healthcare solutions is derived from secondary data available through paid and unpaid sources, analysis of the product portfolios of major companies in the ecosystem, and ratings of companies based on performance and quality. Various sources were consulted during the secondary research process to identify and collect information for this study. The secondary sources include annual reports, press releases, company investor presentations, white papers, journals, certified publications, articles by recognized authors, directories, and databases.

Various secondary sources were consulted during the secondary research process to identify and collect information relevant to the study. These sources included annual reports, press releases, investor presentations of artificial intelligence (AI) in healthcare vendors, forums, certified publications, and whitepapers. Secondary research was used to obtain critical information on the industry’s value chain, the pool of key players, market classification, and segmentation from market- and technology-oriented perspectives.

Primary Research

In the primary research process, various sources from both the supply and demand sides were interviewed to obtain qualitative and quantitative information for this report. Primary sources are mainly industry experts from the core and related industries, as well as preferred suppliers, manufacturers, distributors, technology developers, researchers, and organizations across all segments of this industry’s value chain. In-depth interviews were conducted with various primary respondents, including key industry participants, subject-matter experts (SMEs), C-level executives of key market players, and industry consultants, among other experts, to obtain and verify critical qualitative and quantitative information and to assess prospects.

Primary research was conducted to identify segmentation types, key players, and key market dynamics, including drivers, restraints, opportunities, challenges, and strategies adopted by key players.

After the complete market engineering (calculations for market statistics, market breakdown, market size estimates, market forecasting, and data triangulation), extensive primary research was conducted to gather information and verify and validate the critical numbers derived.

In the complete market engineering process, top-down and bottom-up approaches, along with several data triangulation methods, were extensively used to perform market estimation and forecasting across the overall market segments and subsegments listed in this report. Extensive qualitative and quantitative analyses were conducted on the complete market engineering process to identify key information/insights throughout the report.

Note 1: Other designations include sales, marketing, and product managers.

Note 2: Tiers are defined based on a company’s total revenue. As of 2025: Tier 1 = >USD 1 billion, Tier 2 = USD 500 million to USD 1 billion, and Tier 3 = <USD 500 million.

To know about the assumptions considered for the study, download the pdf brochure

Market Size Estimation The market size estimates and forecasts provided in this study are derived through a mix of the bottom-up approach (revenue share analysis of leading players) and top-down approach (assessment of utilization/adoption/penetration trends, by offering, function, application, deployment, tool, end user, and region). Data Triangulation After arriving at the overall market size—using the market size estimation processes—the market was split into several segments and subsegments. To complete the overall market engineering process and obtain the exact statistics for each market segment and subsegment, data triangulation and market breakdown procedures were employed wherever applicable. The data was triangulated by examining various factors and trends on both the demand and supply sides of the artificial intelligence (AI) in healthcare market.

Market Definition The artificial intelligence (AI) in healthcare market encompasses the application of AI technologies, such as machine learning, natural language processing, computer vision, and robotics, to improve healthcare delivery, enhance operational efficiency, and provide personalized care. These solutions address a wide range of use cases, including diagnostic imaging, predictive analytics, drug discovery, patient engagement, remote monitoring, and administrative workflows, enabling healthcare providers, payers, and pharmaceutical companies to drive innovation and improve outcomes. Key Stakeholders Artificial intelligence (AI) in healthcare software vendors

Artificial intelligence (AI) in healthcare service providers

Independent software vendors (ISVs)

Platform providers

Technology providers

System integrators

Cloud service providers

Healthcare IT service providers

Hospitals and surgical centers

Diagnostic imaging centers

Academic institutes and research laboratories

Forums, alliances, and associations

Government organizations

Institutional investors and investment banks

Investors/Shareholders

Venture capitalists

Research and consulting firms Report Objectives To define, describe, and forecast the global artificial intelligence (AI) in healthcare market based on offering, function, application, deployment, tool, end user, and region

To provide detailed information regarding the factors influencing the growth of the market (such as the drivers, restraints, opportunities, and challenges)

To strategically analyze micromarkets with respect to individual growth trends, prospects, and contributions to the overall artificial intelligence (AI) in healthcare market

To analyze market opportunities for stakeholders and provide details of the competitive landscape for market leaders

To forecast the size of the artificial intelligence (AI) in healthcare market in five main regions (along with their respective key countries): North America, Europe, the Asia Pacific, Latin America, and the Middle East & Africa

To profile key players and comprehensively analyze their product portfolios, market positions, and core competencies in the market

To track and analyze competitive developments such as product & service launches; expansions; partnerships, agreements, and collaborations; and acquisitions in the artificial intelligence (AI) in healthcare market

To benchmark players within the artificial intelligence (AI) in healthcare market using the Company Evaluation Matrix framework, which analyzes market players on various parameters within the broad categories of business strategy, market share, and product offering.

UK Artificial Intelligence (AI) in Healthcare Market Size, Share,Trends, Growth Analysis Report, 2030

As skepticism about artificial intelligence continues to prevail among the public, major technology firms are increasingly turning their attention toward the healthcare sector in hopes of enhancing the technology’s reputation.

AI is increasingly integrated into healthcare practices, with a survey conducted by the American Medical Association revealing that 81% of physicians who responded utilize AI for various tasks, including staying updated on medical research, generating discharge instructions, and documenting patient visits. In a significant development, chip manufacturer NVIDIA and pharmaceutical giant Eli Lilly announced a partnership in January, pledging up to $1 billion towards establishing an AI laboratory focused on "drug discovery."

These initiatives to boost AI investment in healthcare could be part of a broader effort to cultivate public acceptance of the technology. According to a recent report by the Wall Street Journal, AI company Anthropic, known for its AI model Claude, is actively advocating for AI applications in healthcare to enhance its public image and foster confidence among potential investors.

The rapid advancement of AI technology has prompted mixed opinions, as highlighted by a recent YouGov/Economist survey indicating that 71% of Americans believe that AI development is outpacing safe regulations. Additionally, a poll from NBC News found that 57% of voters are concerned that the risks associated with AI surpass its advantages, contrasted with 34% who hold a different view. These apprehensions stem from potential copyright conflicts with training data, environmental repercussions, job displacement, and rising operational costs linked to the maintenance of data centers.

Nevertheless, Dr. Michael Sjoding, a pulmonary and critical care physician at Michigan Medicine, suggests that AI is making a positive impact in the medical field through "ambient AI scribes," which help streamline appointment summaries. He noted, “When patients visit their doctor, they might now be asked, ‘Can I record this conversation?’ using an application typically found on smartphones. This program can listen to the full dialogue during the appointment and subsequently draft medical notes for the physician's review."

Yet, a recent study from Ohio State University indicates that public support for AI utilization in healthcare has declined, dropping from a 52% approval rate in 2024 to 42% by 2026.

AI in Healthcare: Can it Enhance the Technology’s Public Image?

A bright, sunlit hospital ward bathed in warm morning light. A confident doctor stands at a sleek touchscreen console displaying translucent AI‐powered diagnostic data gently hovering above the screen. In the foreground, a patient silhouette gives a hopeful nod. On the wall behind them, a soft white heartbeat line curves into a subtle question mark, symbolizing the debate over AI’s role in healthcare.

AI In Healthcare: Will it Improve the Tech’s Public Reputation? - WJR-AM

- AI adoption in healthcare is surging, with 81% of physicians utilizing it for tasks.  
- Tech giants like NVIDIA and Eli Lilly are investing $1 billion in AI drug discovery.  
- Public approval for AI in healthcare declined from 52% in 2024 to 42% in 2026.  

As public perception on artificial intelligence remains skeptical at best, or sour at worst, giant tech companies are looking to the healthcare sector to help improve the tech’s public approval.

AI is becoming a regular part of the healthcare industry, with a recent survey from the American Medical Association reporting that 81% of phytins surveyed said they used AI “to stay on top of medical research, create discharge instructions, document medical visits and more.” Major investments are also being made between the sectors, with computer chip manufacturer NVIDIA and pharmaceutical company Eli Lilly announcing in January that they will invest up to $1 billion to create an AI lab to help with “drug discovery.”

These public pushes for AI investment into healthcare could be part of a strategy to help shore up public acceptance of the technology, with the Wall Street Journal recently reporting that AI company Anthropic, the makers of Claude, is looking to promote AI use in healthcare to improve AI’s reputation and instill confidence in potential investors.

Rapidly-growing AI tech has garnered a divisive reputation in recent years, with a recent YouGov/Economist poll finding that majority of Americans (71%) think that AI development is moving too fast. Another poll from NBC News, 57% of voters said they thought the risks of AI outweigh its benefits, compared with to the 34% who thought differently. Concerns over AI have grown due to potential copyright issues with AI training data, the environmental impact, potential job loss to AI, and the proliferation of data centers associated with increased utility bills and heavy water use.

However, Dr. Michael Sjoding, a pulmonary and critical care physician at Michigan Medicine, told WJR that one way doctors are using AI is by creating “ambient AI scribes” to help summarize appointments, calling the technology a “positive” for healthcare.

“When you go to the doctor, they might ask you now, ‘Hey, can I record this conversation?’ using a program that’s typically on our phone,” Sjoding said. “That conversation can listen to the entire medical conversation that you’re having, and that tool can then draft the medical notes for the doctor, which the doctor would then hopefully edit and finalize.”

A recent study from the Ohio State University found that public openness to AI use in healthcare fell from a 52% approval rating in 2024 to 42% in 2026.

AI In Healthcare: Will it Improve the Tech’s Public Reputation?

I recently spoke with my doctor in London, who informed me that the practice was piloting a new AI solution from Oracle. Before our consultation began, I was asked if I felt comfortable with artificial intelligence generating notes about our discussion. Shortly thereafter, during a specialist appointment at The Shard, I encountered a similar inquiry. Despite being in different environments and speaking to distinct healthcare professionals, one clear message emerged: AI is now a part of the healthcare landscape.

This development is significant because the healthcare sector is rapidly becoming a major arena for testing artificial intelligence technologies. AI is capable of various tasks, including listening to consultations, preparing clinical notes, analyzing medical scans, interpreting lab results, forecasting patient deterioration, managing waitlists, and increasingly assisting with clinical judgments. The potential for improvement in healthcare is vast. However, as AI integration accelerates, a crucial question arises: How do patients feel about these innovations?

A recent survey by the Pew Research Center offers valuable insights into this issue. Patients are not dismissing AI; rather, they are seeking transparency, understanding, and greater control over how AI is integrated into their healthcare experiences.

"Patients are not rejecting AI. They are asking to be included in the process."

Key Findings

The survey reveals that 72% of American adults view it as extremely or very important for healthcare providers to inform them when AI is being utilized in their medical care. This sentiment transcends demographics, including age, gender, education level, and race.

As AI approaches more critical clinical roles, patient expectations grow stronger. For instance, over 80% of respondents want to be informed when AI is involved in analyzing scans, making diagnoses, or interpreting lab results. Even for tasks like ambient note-taking, which is increasingly common in healthcare, 72% of patients expect to be notified.

Interpreting the Results

Interestingly, the most crucial takeaway isn't just about the need for disclosure; it’s about patient agency. Currently, 53% of survey participants feel they lack sufficient say in whether AI is used in their medical care, while 63% express a desire for more involvement. Only 21% are satisfied with their current level of control, indicating that patients are three times more likely to want a greater say than to be comfortable with the current situation.

This data does not reflect an anti-AI sentiment; instead, it signals that the adoption of technology is outpacing the establishment of a social contract for its use. Traditionally, the debate around digital transformation in healthcare has centered on the needs of healthcare institutions—focusing on improvements in efficiency and patient throughput. Now, AI introduces an important new factor: the patient, whose data and experiences are being analyzed.

The Knowledge Gap

Notably, many patients are unaware of the AI's role in their healthcare. Only 16% reported being informed that AI had been utilized in their medical care, while 37% assumed it had not been applied, and 46% were uncertain. Among those who knew that AI had been used, merely 22% said they understood its application very well. Close to a third felt they had a poor or nonexistent understanding.

While the technology itself is advanced, the surrounding patient experience often lacks clarity.

Insights for Healthcare Leaders

The National Health Service (NHS) should take heed as Britain swiftly enters this evolving landscape. AI technologies such as virtual scribes, diagnostic algorithms, and predictive models are beginning to permeate both the NHS and private healthcare sectors. When implemented effectively, these tools could revolutionize patient care, allowing doctors to spend less time on administrative tasks and more time interacting with patients. AI could help radiologists identify issues sooner and streamline hospital resources for better efficiency. Ultimately, AI has the potential to create a more individualized and proactive healthcare experience.

Nonetheless, every benefit is compromised if patients only discover the involvement of AI after the fact. Trust is foundational in healthcare, and AI enhances this necessity rather than diminishes it. While hospitals don't have to burden patients with excessive consent forms for every algorithmic decision, establishing a clear distinction between routine automation and technology that plays an integral role in care is essential.

Establishing a Patient-Centric Framework for AI:

- Ensure patients are clearly informed when AI is collecting, interpreting, or influencing medical encounters.
- Provide plain-language explanations of how the technology operates, what data it uses, and clarify areas of clinician accountability.
- Implement rigorous standards for consent and governance as AI becomes pivotal in diagnosis, treatment, and prioritization.
- Create accessible channels for patients to ask questions, challenge AI outputs, and verify whether a human clinician reviewed AI-driven decisions.
- Maintain independent clinical governance, ensure auditability, and establish strict guidelines on the secondary uses of patient data.

Navigating the Future of AI in Healthcare

Success in healthcare AI will not solely depend on deploying advanced algorithms. Rather, the organizations that thrive will be those that prioritize patient awareness, protection, and engagement. The future development of AI will hinge not just on technical capabilities but also on obtaining patient permission and fostering trust.

Patients are not calling for an end to innovation in healthcare; they want to be part of it. Recognizing this desire as a strategic advantage instead of a barrier can facilitate trust, paving the way for smoother AI integration.

With AI firmly establishing its presence, the key question remains: Will its introduction into healthcare be seen as a collaborative effort with patients or as an imposition upon them? Insights from the patient survey suggest that the public has already begun to respond, and the healthcare sector would do well to listen.

Patients' Perspectives on AI in Healthcare

A bright, sunlit hospital corridor where two adult patients engage with a sleek digital kiosk labeled “AI Health.” One patient sits in a chair, holding a tablet that displays a friendly AI avatar and medical charts, looking intrigued. The other stands nearby, conversing with a doctor in scrubs who gestures toward the kiosk. In the background, large windows flood the modern clinic with natural light, and subtle hospital signage hints at patient feedback stations. Emotion and curiosity light up their faces, capturing the story of real patients reacting to AI in healthcare.

What Patients Really Think About AI in Healthcare - distilledpost.com

- 72% of American adults want doctors to disclose when AI is used in healthcare.  
- 63% of Americans desire more control over AI's role in their medical care.  
- Only 16% of adults understand how AI is utilized in their healthcare experiences.  

I recently had a call from my doctor in London. Before we began, I was told the practice was trialling a new Oracle AI solution and asked whether I was comfortable with the consultation being written up by artificial intelligence. A few days later, I walked into a specialist appointment at the Shard and heard almost exactly the same question. Two different settings, two different clinicians, one unmistakable signal: AI has moved out of the conference hall and into the consulting room.

That matters because healthcare is becoming one of the most important proving grounds for artificial intelligence. AI can listen to consultations, draft clinical notes, analyse scans, interpret laboratory results, predict deterioration, manage waiting lists and increasingly support clinical decisions. The opportunity is enormous. But as deployment accelerates, a more fundamental question is emerging: what do patients actually think about all of this?

A new Pew Research Center survey of U.S. adults provides one of the clearest answers yet. Patients are not simply rejecting AI. What they are demanding is transparency, understanding and significantly more control over when it is used in their care.

“Patients are not saying stop AI. They are saying: bring us with you.”

The headline number

Some 72 per cent of American adults say it is extremely or very important that a doctor or healthcare professional tells them when AI is being used in their healthcare. That view cuts across age, gender, education and racial groups.

The closer AI gets to a clinical decision, the stronger the expectation becomes. Eight in ten or more say they should be told when AI is analysing medical scans, making a diagnosis or explaining laboratory results. Even ambient note-taking, one of the fastest-growing uses of generative AI in medicine, triggers the same instinct: 72 per cent want disclosure.

What the survey is really saying

The most important finding may not be disclosure at all. It is agency. Fifty-three per cent of Americans say they currently have little or no say over whether a healthcare provider uses AI in their care. Yet 63 per cent say they would like more say. Only 21 per cent are comfortable with the amount of control they currently have. Americans are therefore three times more likely to want greater input than to be satisfied with the status quo.

That is not an anti-AI finding. It is a warning that technology adoption is moving faster than the social contract around it. For years, digital transformation has largely been judged through the needs of institutions: can a system save clinician time, improve productivity, reduce administrative burden or help hospitals treat more patients? AI adds another constituency to the discussion: the patient whose voice, image, symptoms, records or behaviour are being analysed.

The patient awareness gap

Many patients do not even know when that is happening. Only 16 per cent of adults surveyed said they knew AI had been used in their healthcare. Thirty-seven per cent believed it had not been used, while 46 per cent were unsure. Among people who know AI has been used, just 22 per cent said they understood extremely or very well how it was being used. Nearly a third said they understood it not too well or not at all.

The technology may be sophisticated. The patient experience surrounding it often is not.

What patients are telling healthcare leaders

Why the NHS should pay attention

Britain is moving rapidly into the same territory. AI scribes, diagnostic algorithms, predictive models, scheduling platforms and data systems are entering NHS and private healthcare. Used well, they can be transformative. A doctor who spends less time typing may spend more time listening. An AI-supported radiologist may identify disease earlier. A system capable of predicting demand could help hospitals use theatres, beds and staff more effectively. AI could make healthcare more personalised, preventative and responsive.

But every one of those benefits becomes harder to defend if the patient discovers only afterwards that AI played a role. Healthcare runs on trust. AI does not reduce the importance of that trust; it amplifies it. That does not mean hospitals need a lengthy consent form every time an algorithm works in the background, nor should we create so much friction that useful technology becomes impossible to deploy. It does mean drawing a clearer line between routine automation and technology that records, interprets or materially influences care.

A practical patient compact for healthcare AI

• Clear disclosure when AI records, interprets or materially influences a patient encounter.

• Plain-language explanations of what the technology is doing, what data it uses and where the clinician remains accountable.

• A higher standard of consent and governance as AI moves closer to diagnosis, treatment or prioritisation.

• Visible routes for patients to ask questions, challenge outputs and understand whether a human has reviewed an AI-supported decision.

• Independent clinical governance, auditability and strong limits on secondary uses of patient data.

The next AI divide will be about permission

The organisations that succeed in healthcare AI will not necessarily be those deploying the most algorithms. They may be those that make patients feel most informed, protected and involved. The next phase of AI adoption therefore cannot be driven by capability alone. It will be determined by permission.

Patients are not asking healthcare to stop innovating. They are asking to be brought with it. That should be seen as an advantage, not an obstacle. Trust can become an adoption strategy.

AI is here to stay. The question is whether its arrival in healthcare feels like something being done with patients, or something being done to them. The patient survey suggests the public has already begun to answer. Healthcare would be wise to listen to.

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Source note

Survey data: Pew Research Center, survey of U.S. adults conducted June 22-28, 2026. Figures used in this article are drawn from the survey material supplied for editorial review.

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What Patients Really Think About AI in Healthcare

The recent Westminster Health Forum policy conference in London shed light on the ongoing evolution of AI's role in Life Sciences and Healthcare, alongside the policy and regulatory hurdles that it faces. Attendees shared a common view that although the UK boasts considerable expertise and valuable population data through its National Health Service (NHS), obstacles to AI integration among healthcare providers still prevail. These hurdles encompass issues like data accessibility, interoperability, regulations, workforce capabilities, and the challenge of large-scale implementation—challenges not unique to this sector.

In a broader context, research findings from GWI indicate that 88% of healthcare providers are currently leveraging AI technologies. Among these users, 56% utilize AI for information retrieval, and around a third employ it for professional guidance and self-directed learning, largely indicating the prominence of familiar chatbot interfaces over specialized medical systems. Yet, a mere 18% report experiencing improved time efficiency, and only 16% have noted a boost in productivity—denoting that many are still waiting for tangible benefits in a field where efficiency gains would be particularly impactful given the demands on healthcare professionals.

This highlights a critical insight: although there are numerous sophisticated AI applications for drug discovery, early disease detection, and surgical assistance— the industry remains entangled in the same consumer-tech noise that affects other fields. Expectations that chatbot technology could lead to breakthroughs in treating serious diseases like cancer and Alzheimer’s are likely overstated, although these tools may assist specialists in data evaluation when used in conjunction with more advanced systems.

The conference also emphasized the pivotal role of regulation. Speakers voiced that regulatory frameworks must function as facilitators of innovation, a principle that has echoed through government dialogues for years. However, one must question whether this approach remains effective. For instance, there’s scant evidence of AI innovation being stifled by regulation in recent years, aside from rare instances where legal actions compelled vendors to secure appropriate licensing for training data.

Interestingly, much of today’s technological advancement in the US can be traced back to policy initiatives during the Biden administration, which addressed AI safety and ethical guidelines. Yet, despite these regulations, nine of the world's ten leading companies, all within the AI domain, have seen their market caps soar beyond $1 trillion. This suggests that while guidelines may exist, they do not inherently obstruct innovation. A lack of rigorous governance can expose the public to unethical practices, emphasizing the need for more robust regulatory oversight to protect consumer interests.

Professor Alastair Denniston, Chair of the UK National Commission on the Regulation of AI in Healthcare, provided a balanced perspective on the necessity of regulation. Highlighting his experience as an ophthalmologist within the NHS, he noted significant advancements in diagnostics and patient care over the past decade, coupled with an increased ability for patients to engage meaningfully in their own healthcare decisions. Nevertheless, he cautioned against a purely optimistic bias toward innovation. He stressed that regulation exists to ensure safety, efficacy, and objectivity—safeguards aimed at reducing risks to patients.

The discussions at the conference were particularly fruitful in addressing the challenges of implementing innovative technologies within the NHS. Drawing from his involvement in Innovate UK’s Expert Missions, which sought to bridge relationships with tech hubs in the US, he illustrated how the UK healthcare system still grapples with similar hurdles present during that initiative. These include convoluted procurement processes, insufficient funding for scaling solutions, and readiness within organizations to adopt new technologies.

As Denniston proposed, effective regulation should be geared toward fostering patient-centric and practitioner-friendly practices while ensuring safety and speed in technological deployment. He highlighted the importance of a balanced perspective—while expecting improvement in safety standards, the healthcare sector should recognize that achieving 100% safety is unrealistic. The focus should lie on enhancing current practices rather than striving for unattainable ideals.

Ultimately, the conference underscored that the road to successfully integrating innovations in healthcare relies not on merely developing new technologies but on establishing access to data, building public trust—especially amid privacy concerns—implementing smarter regulations, investing in workforce training, supporting startups to scale, and facilitating smoother transitions from research to practical applications.

While progress has been hindered in part by the extended disruption of COVID-19, the continued challenge of securing adequate funding in the face of substantial investments in large consultancies calls for renewed attention toward nurturing genuine healthcare innovators.

AI in Healthcare (1/2) – emphasizing the ‘signal’ from specialist tools over the distractions of consumer technology

A bright, sunlit hospital exam room: a medical specialist in scrubs leans over a sleek touchscreen console displaying a 3D MRI scan with crisp, color-coded outlines and clear waveform graphs of vital signals. On a side table in soft focus sits a small pile of smartphones turned off—representing the noise of consumer tech—while all attention is on the glowing specialist AI tool’s clear, signal-driven data.

AI in Healthcare (1/2) – focus on the ‘signal’ of specialist tools, rather than the noise of consumer tech - diginomica

- The UK faces barriers to AI adoption in healthcare, including data accessibility and regulation.  
- While 88% of providers use AI, few report improved efficiency or productivity gains.  
- Successful AI integration requires better data access, workforce skills, and smarter regulation.

As noted on diginomica passim the value of AI in Life Sciences and Healthcare has been seen. That value and the policy and regulatory challenges that underpin that, were the subject of a recent Westminster Health Forum policy conference in London.

The event highlighted a consensus among delegates that, while the UK possesses undoubted expertise and, via its National Health Service (NHS), valuable data on Britain’s population, barriers remain to AI adoption among care providers. These include data accessibility, interoperability, regulation, commercialization, workforce skills, and implementation at scale – a familiar scenario in other professional sectors, of course.

For a big-picture context for these discussions, see August 2026 findings from research company GWI - provided to me as raw spreadsheet data. According to that survey, 88% of Healthcare providers are already using AI: 56% to find information, with roughly one-third using it for professional advice and self-learning, which suggests the AIs in question are mainly the familiar chatbots, rather than specialist medical systems.

However, only 18% of users report greater time efficiency from these (implicitly) consumer tools, and just 16% an increase in productivity: another familiar scenario. The promised instant wins continue to elude most users – and in a sector where you would hope they would be obvious, given how stressed and time-poor Healthcare workers are.

All of which tells us something useful, namely that while there are many excellent, high-end, professional applications of AI in drug discovery, cancer screening, early detection and diagnosis – plus keyhole surgery and lab automation in robotics – the sector is adrift on the same sea of consumer-tech noise as the rest of us. Cures for cancer, heart disease, Alzheimer’s, MND, and other diseases are unlikely to surface from the wave of chatbot hype, though natural-language prompts may help specialists interrogate their data alongside more dedicated tools.

Put simply, industry hype that conflates the adoption of consumer chatbots with the promise of specialist AI tools in cancer research is unhelpful, mis-leading, and disingenuous.

Regulation matters

At the Westminster policy conference, speakers emphasized that regulation should always act as an enabler of innovation, which has been the prevailing message from Whitehall for many years, indeed, it is practically a government meme. But is that still the right approach? After all, can you name a single example of AI innovation being impeded by regulation over the last five years, other than a handful of vendors being forced, by legal action, to pay for or license training data?

Consider this - arguably, much of today’s US tech innovation has roots in the Biden era, when AI safety, codes of ethical conduct, and the AI Bill of Rights were firmly on the table. None of that stopped nine of the top ten most valuable companies on Earth – all of them in AI – from amassing market capitalizations of over $1 trillion.

The message could not be clearer - rules, codes of conduct, safety summits, and early attempts at regulation were no impediment to innovation, while sweeping aside commitments to safety and attempting to stop regulation in its tracks leaves the public exposed to unethical practice. That suggests that it is time for regulators to put their teeth back in - vendors clearly don’t need help in innovating, they do need someone to keep them in line and protect the rest of us.

That aside, Professor Alastair Denniston is Chair of the UK National Commission on the Regulation of AI in Healthcare. He offered a more moderate, but sanguine view:

Why do we innovate? Why do we regulate? And how do we make regulation work for AI? Well, I'm a doctor in the NHS. I'm an ophthalmologist, but I'm old enough to recognize that the practice I can do today is way better than I could have done ten years ago. I have better diagnostic tests, better treatments with fewer side effects, and the patients I see have greater ability to integrate, control, and manage their own healthcare. And they also have better sources of information, to understand, question and challenge me on things. So why do we regulate? Shouldn’t we just take the brakes off and let it all happen? Well, the reason we regulate is because sometimes we're a bit rubbish. And other times we have an optimism bias. […] And if we rolled this out across the NHS, it would be a disaster.

Arguably, the world has indeed rolled out an optimism bias with AI, and the results – at best – are underwhelming. Denniston continued:

So, regulation is partly checking that things are safe, that they are effective, that they do what they claim to do, and that my personal bias or enthusiasm for a particular product or service is not just a personal view, but an objective assessment. Ultimately, regulation is that set of rules, standards, and guidance. And their enforcement comes top down to reduce public risk. It’s about changing behavior.

In other words, the world needs objective assessments, not more marcomms, social posts, and PR.

Innovation challenges

But the conference was at its most useful when it highlighted the challenges of translating real innovation into NHS practice.

I know from first-hand experience that this has long been a problem. Back in 2018-19, as the UK sought to mitigate the effects of the 2016 Brexit referendum, I took part in Innovate UK’s Expert Missions program, in which entrepreneurs, policymakers, and academics undertook a series of tech-specific missions to deepen trading relationships with partners outside of Europe.

One such was a Digital Health mission to Texas, home of the Texas Medical Center (TMC) in Houston, and several other leading healthcare facilities. Core to that week-long visit was a series of fact-finding meetings with tech accelerators, start-ups, hospitals, and research centers to find out how the US is able to speed technologies out of labs and into clinical practise much more quickly than the UK.

The aim was for the NHS to learn from that culture and – where appropriate for a national, taxpayer-funded service – emulate those processes. But fast-forward to today, and it seems the Health Service is facing the same problems as it was back then. Among them, the Westminster conference identified fragmented procurement pathways, limited scale-up funding, and a lack of organizational readiness.

As Denniston observed, smarter regulation could enable the change, alongside better technology and implementation processes. He argued that the ideal combination would be:

Patient-centered and practitioner-enabling. It should be safe. It should be fast, and it should be trusted.

Some of that is self-explanatory, he suggested, but highlighted a couple of areas for attention:

First, it is a myth that we can achieve 100% safety in Healthcare. We don't have that now, and we can't expect it of any new technology, whether it's AI or anything else. Instead, what we're trying to achieve is improving safety. So, we need to compare with where we are now, and not where we wish to be. And the second area is speed. Speed matters because, when beneficial innovation is coming through, we want to get it to patients as quickly as possible. We want to make sure that patients benefit from innovations early, especially if those innovations are safer and of higher quality than we have today. We don't want the innovation pipeline to be slowed with friction, if that only delays the benefits.

But as the conference noted, success will depend less on developing new technologies, and more on enabling access to data, building public trust (in a culture in which privacy is assumed), establishing smarter (not supine) regulation, investing in workforce skills, helping start-ups scale to viable businesses, and creating new pathways out of research labs and into safe, standards-based adoption.

All of which the Expert Missions were designed to achieve eight years ago, but we should acknowledge the prolonged COVID hiatus in the NHS’ strategic and operational planning.

Plus, we should recognize the ongoing struggle for central government funding, at a time when billions of dollars are being poured into the pockets of systems integrators and large consultancies and not nearly enough into those of healthcare innovators.

Epic aims to enhance outpatient visits through the introduction of a new AI tool.

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