During the Healthcare Innovation Summit held in Washington, D.C., experts participated in discussions surrounding the practicalities of effective AI governance. The panelists explored the nuances of adopting AI technology, the necessity for oversight, and the complexities of reimbursement as healthcare organizations strive to integrate AI in a responsible manner while scaling their operations.

The Outlook for AI Regulation and Data Analysis in Healthcare

A bright, sunlit hospital innovation lab: a doctor and a nurse stand before a wall of translucent screens showing patient vital-stats graphs and predictive analytics charts in soft natural tones. On a glass table in the foreground lies a sleek binder stamped “AI Governance,” alongside a stylized microchip icon. Through floor-to-ceiling windows, healing greenery and a tree-lined courtyard bathe the scene in warm, hopeful light.

The Future of AI Governance and Data Analytics in Healthcare - HCI Innovation Group

- Panelists emphasize the importance of effective AI governance for responsible healthcare adoption.  
- Discussion focuses on implications of AI oversight in healthcare organizations as they scale.  
- Reimbursement strategies related to AI implementation were also highlighted during the summit.

At the Healthcare Innovation Summit in Washington, D.C., panelists discuss what effective AI governance looks like in practice and the implications for adoption, oversight, and reimbursement as organizations scale AI responsibly.

The Future of AI Governance and Data Analytics in Healthcare

A recent analysis has highlighted growing patient apprehensions regarding the implementation of artificial intelligence (AI) tools by healthcare professionals. The findings reveal that a substantial number of patients would like to be kept informed about the deployment of AI in their medical care. Notably, over half of the surveyed patients are unaware of whether AI is currently being utilized in their healthcare interactions.

Conducted by the Pew Research Center in late June 2026, the survey encompassed nearly 5,000 adults across the United States. The results indicated that 72% of patients regard it as crucial or highly important for their healthcare providers to disclose the use of AI tools in patient care. Additionally, 16% feel that this information is somewhat important, while merely 7% indicated a lack of concern about being informed on the topic.

The survey unveiled varying levels of concern based on specific AI applications. The highest levels of worry were associated with AI involvement in making diagnostic decisions (81%), analyzing medical scans (81%), interpreting medical test results (80%), and documenting notes during appointments (72%). Furthermore, more than half of the respondents expressed the belief that they should be informed about the use of AI for auxiliary tasks such as prescription management (64%) and appointment scheduling (56%). However, a significant proportion (33%) felt that disclosure was unnecessary for scheduling activities. In various categories, around 9% to 10% of patients were uncertain if they should be informed, indicating a potential lack of awareness regarding the associated risks.

Despite a strong desire for transparency regarding AI's role in healthcare, nearly half of those surveyed (46%) reported being unaware of whether their healthcare providers employed AI solutions. Only 16% had been informed by a doctor about the utilization of AI in their treatment. Data from the Office of the National Coordinator for Health Information Technology (ONC) shows a marked increase in AI adoption, with 71% of hospitals utilizing AI tools in 2024, up from 66% in 2023. Nevertheless, a notable 33% of respondents presumed that their providers were not employing AI, suggesting a gap in transparency.

As the use of AI continues to rise, patients are increasingly seeking insight and a degree of control over its application in their healthcare. The survey revealed that 53% of participants feel they have little to no influence over AI usage, while only 16% believe they possess some degree of control. Furthermore, a compelling 63% expressed a desire for a greater say in AI-related healthcare decisions, with only 21% feeling comfortable about their current level of influence.

In light of the expanding role of AI in healthcare, it is imperative for providers to communicate clearly how these technologies are being utilized, ensuring that patient consent is obtained to foster trust. Additionally, ongoing evaluations of AI tools—such as transcription software and chatbots—are necessary to confirm their efficacy and accuracy.

Patients Desire Transparency on AI Utilization in Healthcare, According to Survey

A bright, airy doctor’s office flooded with warm natural light. A middle-aged patient and physician lean over a tablet showing an infographic titled “AI in Your Care: When & How,” complete with simple icons—a chip-shaped heart monitor, a magnifying-glass diagnostics symbol, and a data-shield for privacy. The patient’s expression is engaged as the doctor points to a timeline on the screen. In the background, a sleek monitor displays the words “AI Usage in Treatment.” high-detail, realistic photography style.

Survey Reveals Patients Want to Know When and How AI is Used in Healthcare - hipaajournal.com

- 72% of patients want to be informed about AI usage in healthcare settings.  
- 46% of patients are unaware if their providers use AI tools in care.  
- 53% feel they have little control over AI's role in their healthcare decisions.  

Survey Reveals Patients Want to Know When and How AI is Used in Healthcare

A recent survey has revealed that patients are concerned about the use of AI tools by doctors’ offices and other healthcare providers, and the vast majority of patients believe that they should be informed if their healthcare provider is using AI tools in connection with their healthcare. The survey also indicates that more than half of patients are unaware whether AI is currently being used in relation to their healthcare.

The survey was conducted on almost 5,000 U.S. adults in late June 2026 by the Pew Research Center. The survey revealed that 72% of patients believe it is extremely important or very important for their healthcare providers to disclose whether they are using AI tools in connection with healthcare, with 16% of respondents believing that it is somewhat important. Only 7% of respondents said they are not too bothered or not at all bothered about being informed about the use of AI.

Concern varied across different uses of AI, with the greatest concern expressed about AI being used to make diagnostic decisions (81%), analyze medical scans (81%), explain medical test results (80%), and take notes during a medical appointment (72%). More than half of patients believe that they should be informed about behind-the-scenes administrative services such as getting prescription refills (64%) and scheduling medical appointments (56%), although the latter had the largest percentage of patients who do not feel that disclosure is needed (33%). Across all areas of questioning, 9% or 10% of patients were not sure if they should be informed, potentially indicating they are unaware of any risks involved.

While most patients believe that they should be informed about the use of AI in healthcare, almost half of all surveyed patients (46%) said they were unaware whether their doctor’s office and other healthcare providers were using AI solutions, with only 16% of patients saying a doctor has actually told them that AI was used in their care. Adoption of AI in healthcare has grown considerably, with ONC’s figures showing that 71% of hospitals were using AI tools in 2024, up from 66% in 2023. Despite the high level of AI adoption, 33% of respondents believe their healthcare providers are not using AI tools, which suggests a lack of transparency.

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While patients want to be informed and have a say in how AI is used in healthcare, many Americans do not believe they have control over how AI is used. The survey revealed that more than half of respondents (53%) believe they either have no say or not much say in the use of AI in healthcare, with 16% believing they have some say. 63% of respondents to the survey would like more say in how AI is used, and only 21% of respondents said they are comfortable with how much say they currently have.

As AI adoption grows, it is important for healthcare providers to explain to patients how the tools are used and to obtain patient consent in order to maintain trust. It is also important for the tools, including transcription tools and chatbots, to be continuously evaluated to ensure they are fit for purpose and are generating accurate results.

Survey Reveals Patients Want to Know When and How AI is Used in Healthcare

Artificial intelligence (AI) in the healthcare sector is not a singular narrative; rather, it comprises three distinct stories that are advancing at varying paces, a detail often overlooked in mainstream discussions.

In the realm of hospitals and health systems, AI is steadily integrating into daily operations. It assists with essential administrative duties such as documentation, information summarization, and scheduling, all of which typically consume significant time for healthcare professionals. However, the integration of AI in areas like diagnosis and treatment decisions is approached with more caution.

Within the medical technology (medtech) field, the transition involves both regulatory and technical considerations. The FDA currently lists over 1,500 AI-enhanced medical devices, and regulatory bodies are developing frameworks that allow certain AI products to evolve post-launch, within specific, vetted parameters. This is crucial because AI technology is ever-evolving, while regulations for medical devices have traditionally focused on slower-shifting products.

In contrast, the pharmaceutical and biotech industries are following a significantly different trajectory. AI can significantly fast-track processes related to target identification and molecule discovery. However, it cannot bypass the lengthy phases of clinical testing, safety assessments, and regulatory evaluations that follow. While AI may expedite one segment of the development cycle, it doesn't eliminate the entire process.

Understanding this distinction is essential as the focus shifts toward demonstrating the value of AI implementations.

According to PwC's 2026 Global CEO Survey, 56% of CEOs reported that their organizations had not experienced increased revenues or reduced costs due to AI. This does not indicate failure; rather, it underscores that experimentation and value realization are not synonymous.

The pressing question has transitioned from "Can we adopt AI?"—a challenge many organizations have already met—to "Should we implement it in this context, and what quantifiable outcome can we expect?" 

Although this might seem straightforward, it's often where AI projects falter. Many companies mistakenly start with a technological solution in search of a problem; the more effective strategy is to identify the business challenge first.

Before committing to an AI project, clarity on three critical questions is vital:

1. What specific business challenge are we addressing?
2. What metric should improve if the implementation is successful?
3. Who in the organization is responsible for that metric?

If these questions cannot be clearly answered, the project may not be ready for investment.

It's essential to focus on measuring true outcomes rather than just activities. Metrics like "hours saved" or the number of active users do not equate to business value. In healthcare, we should prioritize factors such as cycle time, error rates, quality, revenue, margins, inventory management, customer or patient experiences, along with risk and compliance.

Post-launch, it's also critical to revisit the projected results after 90 and 180 days to assess their alignment with the original business objectives. If the expected metrics haven't shifted, it may be time to revise the solution or withdraw funding. Despite strict capital allocation criteria for technology investments, many organizations lack similar rigor in evaluating their effectiveness.

Currently, some of the quickest benefits from AI in healthcare stem from processes that are repeatable, data-rich, and rules-based. Areas such as supply-chain forecasting, inventory management, quality assurance in manufacturing, predictive maintenance, customer support, clinical documentation, knowledge retrieval, and compliance workflows exemplify this trend.

Furthermore, the human aspect cannot be neglected. Employees may feel anxious about AI's implications for their jobs, and reassurances like “AI will never take your role” are often viewed as unconvincing.

Typically, jobs comprise two categories: repetitive tasks and those requiring human insight. AI excels at automating the former. In healthcare, this translates to reducing the documentation burden for nurses, minimizing data sorting tasks for researchers, or lessening paperwork for quality engineers. The real opportunity lies in providing professionals more time for tasks that demand their unique skills: judgment, problem-solving, creativity, empathy, and relationship-building.

Organizations must also simplify the adoption of safe practices. If employees lack access to approved tools that streamline their work, they might resort to unofficial methods, which can elevate risks.

The skill set required will also evolve. Technology experts will need to focus on integration, governance, monitoring, and vendor management, rather than just model development. For others in the organization, honing the ability to ask the right questions of AI, recognizing potential inaccuracies in responses, and understanding when human evaluation is essential will be crucial.

Ultimately, success in the AI landscape will not belong to those with the most pilot projects or the loudest promotional strategies. It will go to organizations that effectively integrate AI as a reliable, everyday component of their operations and that can demonstrate meaningful improvements as a result of its implementation. 

Zeeshan Tariq serves as the Chief Digital & Information Officer for CooperVision.

The future of AI in healthcare: Transitioning from excitement to tangible business benefits

A bright, modern hospital office bathed in warm natural light. A doctor in a white coat and a business executive in a tailored suit stand side by side, examining a semi-transparent tablet floating before them. On the tablet, a rising bar chart interwoven with medical symbols—a heartbeat line, DNA helix—fades into delicate neural network circuit patterns. Sunlight streams through a large window, illuminating a stethoscope and patient chart resting on a sleek desk. The scene conveys AI in healthcare moving from hype to real business value.

The future of AI in healthcare: Moving from hype to business value | Viewpoint - Rochester Business Journal

- AI in healthcare is currently enhancing operations but faces cautious adoption for diagnosis and treatment.  
- Regulatory agencies are adapting to AI's rapid evolution while ensuring medical device safety and efficacy.  
- Successful AI initiatives must focus on measurable outcomes, not just technology implementation or activity metrics.  

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Artificial intelligence in healthcare is not one story. It is three different stories moving at three different speeds, and that nuance often gets lost in the headlines.

In hospitals and health systems, AI is already becoming part of everyday work. It helps with documentation, summarizing information, scheduling and other administrative tasks that consume enormous amounts of clinicians’ time. But when AI gets closer to diagnosis or treatment decisions, adoption is understandably more deliberate.

In medtech, the shift is as much regulatory as technical. The FDA‘s public list now includes more than 1,500 AI-enabled medical devices, and regulators are creating ways for certain AI-enabled products to improve after launch within predefined, reviewed boundaries. That matters because AI does not stand still, while medical-device regulation has historically been built around products that change much more slowly.

Pharma and biotech are moving at a different speed altogether. AI can dramatically accelerate parts of target identification and molecule discovery. What it cannot do is eliminate the years of clinical testing, safety work and regulatory review that follow. AI may shorten one leg of the relay race but it does not remove the rest of the race.

That distinction matters because we are entering a much more important phase of AI which is the importance of proving value.

PwC‘s 2026 Global CEO Survey found that 56% of CEOs said their companies had seen neither higher revenue nor lower costs from AI. That does not mean AI is failing. It means experimentation and value are not the same thing.

The question is no longer, “Can we use AI?” Most organizations have already proven that. The better question is, “Should we use it here, and what measurable result will be different if we do?”

That sounds simple, but it is where many AI initiatives go wrong. Too often, companies start with a technology and go looking for a problem. The better approach is the opposite.

Before I invest in an AI initiative, I want three questions answered in plain English:

What business problem are we solving?

What number should move if this works?

Who in the business owns that number?

If we cannot answer those questions, the initiative is probably not ready to be funded.

It’s important to be able to measure outcomes, not activity. “Hours saved,” number of users and prompts submitted can be useful indicators, but they are not business value. In healthcare, we should care more about cycle time, error rates, quality, revenue, margin, inventory, customer or patient experience, risk and compliance.

I also believe in going back 90 and 180 days after launch and comparing the results with the original business case. If the number did not move, it’s time to fix the solution or stop funding it. Too many organizations are disciplined about approving technology investments and surprisingly undisciplined about determining whether those investments actually worked.

Today, some of the fastest returns in healthcare are coming from work that is repeatable, data-heavy and rules-based: supply-chain forecasting, inventory optimization, manufacturing quality, predictive maintenance, customer service, clinical documentation, knowledge retrieval, and quality and regulatory workflows.

There is also a human dimension that leaders cannot ignore. Employees are understandably worried about what AI means for their jobs, and telling people, “AI will never replace you,” is not a credible answer.

Most jobs contain two kinds of work: repetitive work and work requiring human judgment. AI is increasingly good at the first. In healthcare, that can mean giving a nurse less documentation, a scientist less data sorting, or a quality engineer less paperwork. The opportunity is to give people more time for the work that actually requires them: judgment, problem-solving, empathy, creativity and relationships.

Organizations also need to make the safe path the easy path. If employees do not have approved tools that help them get their work done, some will inevitably find their own. That creates more risk, not less.

The skills we need will change too. Technology professionals will increasingly need to be good at integration, governance, monitoring and vendor management – not simply building models. For everyone else, the critical skill is judgment: knowing how to ask AI the right question, recognizing when the answer may be wrong, and understanding when human review is required.

In the end, the winners in AI will not be the organizations with the most pilots or the loudest strategy. They will be the ones that make AI a practical, trusted part of how the business works – and can prove that something important got better because of it.

Zeeshan Tariq is Chief Digital & Information Officer for CooperVision.

The future of AI in healthcare: Moving from hype to business value

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.

The Outlook for AI Regulation and Data Analysis in Healthcare

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