Ankit Jain, the CEO of Infinitus Systems, recently appeared on 'Power Lunch' to talk about the transformative impact of AI on the healthcare sector. He emphasized how artificial intelligence is simplifying various processes within the industry, allowing nurses and other healthcare professionals to dedicate more time to patient care. Jain also addressed the concerns and criticisms surrounding the integration of AI in healthcare, highlighting the need for balanced discussions as technology continues to evolve.

Infinitus Systems CEO discusses AI in healthcare: It can empower nurses to assist patients.

A sunlit hospital ward bathed in warm morning light: a nurse kneels by a patient’s bedside, holding their hand and smiling, while a sleek wall-mounted digital console displays organized vitals and schedules under a prominent Infinitus Systems logo—emphasizing AI freeing nurses to focus on care.

Infinitus Systems CEO on AI in healthcare: Can unlock nurses to help patients - CNBC

- Ankit Jain discusses AI's potential to streamline healthcare and enhance nurse efficiency.  
- The CEO addresses concerns regarding the backlash against AI integration in healthcare.  
- Infinitus Systems aims to leverage AI to improve patient care and outcomes.  

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Infinitus Systems CEO on AI in healthcare: Can unlock nurses to help patients

Ankit Jain, Infinitus Systems CEO, joins 'Power Lunch' to discuss how AI is streamlining healthcare, backlash around AI and much more.

Infinitus Systems CEO on AI in healthcare: Can unlock nurses to help patients

Michael Schlosser, MD, MBA, serves as the Senior Vice President and Chief Transformation Officer at HCA Healthcare, directly reporting to the CEO. In this pivotal role, he spearheads a comprehensive digital transformation across the organization, focusing on the design, development, integration, and enhancement of technologies and processes aimed at improving clinical, administrative, and operational efficiency. His overarching goal is to elevate the experiences and outcomes for leaders, staff, care teams, and patients at HCA Healthcare. Among his responsibilities, Dr. Schlosser manages the integration and optimization of electronic health record (EHR) systems, leads teams specializing in artificial intelligence (AI) and machine learning, heads the data office, and oversees the Responsible AI program. Prior to this, he held the position of Senior Vice President for Care Transformation and Innovation, where he launched early digital transformation initiatives at HCA. His background also includes a tenure as Group Chief Medical Officer, where he oversaw clinical operations at 100 hospitals, prioritizing quality and patient outcomes. A trained neurosurgeon, Dr. Schlosser completed his residency and fellowship at Johns Hopkins University and has previously served as a medical officer at the FDA. He holds a chemical engineering degree from MIT and an MBA from Vanderbilt University.

Healthcare providers are currently navigating increasing demands to enhance patient care while managing workforce challenges, escalating costs, and complex operations. AI stands out as a potential solution to these pressing issues. Despite recognition of the necessity to incorporate AI, only 13% of healthcare systems have a defined strategy for integrating AI into their clinical workflows. HCA Healthcare has taken a forward-thinking approach by formulating a comprehensive AI strategy, underpinned by a strong governance framework to ensure that AI technologies are deployed safely and effectively. The strategy prioritizes collaborative design with clinicians, meticulous validation of outcomes, and robust governance, ensuring that AI adoption is both trusted and effective. This article outlines HCA Healthcare's AI strategy alongside four initial initiatives that display the practical application of AI in clinical, operational, and administrative tasks. These examples highlight how AI can improve patient experiences, support healthcare workers, and boost operational efficiency. For instance, at one facility, an AI-driven tool streamlined scheduling across over 1,000 nursing departments, reducing scheduling time from hours to just 2 to 3 hours per cycle, thereby allowing nurse leaders more time for direct patient care. HCA Healthcare’s strategic investment in AI focuses on balancing innovation with accountability, fostering sustainable and transformative impacts that can serve as a model for other healthcare systems.

The increasing interest in AI within healthcare organizations reflects a shift towards recognizing its potential to address persistent challenges. Recent surveys indicate that a significant number of healthcare executives acknowledge AI's capacity to enhance clinical decision-making and operational efficiency; however, the lack of comprehensive strategies means that many organizations remain uncertain about where to begin their AI journeys. HCA Healthcare’s strategy emphasizes prioritizing essential functions that substantially benefit end-users and have a strategic impact. By setting realistic goals and ensuring measurable value while committing to change management and clinician collaboration, HCA Healthcare aims to facilitate successful AI adoption.

The article elaborates on HCA Healthcare’s journey in AI investment, showcasing four key completed initiatives and offering a framework for other health systems to consider while developing their AI strategies.

As one of the nation’s leading healthcare providers employing over 300,000 individuals—including 100,000 nurses—HCA Healthcare operates 189 hospitals and around 2,500 other care facilities across 19 U.S. states and the UK. The organization handles nearly 47 million patient encounters annually and has harnessed vast amounts of data to drive improvements in care provision. With a scale rarely seen in private healthcare systems, HCA Healthcare’s data capabilities enable both real-world trials involving diverse populations and traditional retrospective research, setting benchmarks in areas such as maternal care and antibiotic stewardship. Notable among its research efforts is the 2024 INSPIRE trial, which successfully identified optimal antibiotic treatments for hospitalized patients with pneumonia and urinary tract infections.

HCA Healthcare’s serious approach toward AI took shape with the establishment of the Care Transformation and Innovation (CT&I) department in 2021. Collaborating with industry leaders like Google and Palantir, the team explored generative AI applications for administrative tasks, aiming to free clinicians to devote more time to patients. Innovation hubs within its hospitals foster a spirit of creativity, serving as testing grounds for new ideas and technologies. The partnership between technology teams and clinical staff remains critical, emphasizing human involvement throughout the process.

AI has become integral to HCA Healthcare's broader investment strategy. The Department of Digital Transformation and Innovation (DT&I), comprised of 755 professionals, drives the deployment of AI solutions aimed at minimizing administrative burdens, improving patient outcomes, and optimizing operational functions.

To maximize the impact of AI investment, HCA Healthcare has adopted a portfolio management approach, applying insights similar to financial portfolio management to minimize risk while maintaining momentum across multiple projects. Clear focus areas are identified to direct resources where they can create significant value with minimal risk.

Among the ongoing AI initiatives at HCA Healthcare are several promising projects:

1. **Nurse Handoffs**: A digital assistant developed for nurses enhances the reliability of patient information exchange during shift changes, which occur roughly 24 million times annually within HCA Healthcare facilities.

2. **Digital Labor Management**: Timpani, an innovative staffing and scheduling platform, increases nurse workflow efficiency by optimizing scheduling based on clinical needs and nurse preferences, significantly reducing time spent on manual scheduling.

3. **Ambient Clinical Documentation**: This AI tool utilizes advanced speech recognition and natural language processing to streamline documentation, effectively reducing the clerical burden on physicians.

4. **Maternal-Fetal Care**: A clinical AI platform consolidates data from multiple sources into a user-friendly interface, improving monitoring and decision-making for maternal-fetal care teams.

5. **Intelligent Inventory Management**: AI algorithms are employed to optimize inventory tracking and management, enhancing overall operational efficiency.

6. **Hospital Throughput**: This initiative aims to streamline patient transitions through the care process, using AI to enhance discharge procedures and improve overall hospital capacity.

These initiatives embody HCA Healthcare's commitment to leveraging AI not merely for financial return but to improve safety, operational effectiveness, and caregiver satisfaction. By focusing on robust change management and iterative testing, HCA Healthcare seeks to ensure that technological innovations translate into meaningful, measurable impacts across its vast organization.

As the healthcare landscape evolves with technological advancements, HCA Healthcare's approach serves as a model for other organizations looking to responsibly harness AI. The lessons learned illustrate the significance of structured strategies in effectively deploying AI to enhance care delivery and create lasting value for patients and providers alike.

HCA Healthcare's strategy for expanding artificial intelligence.

A bright, sunlit hospital innovation hub with floor-to-ceiling windows letting in warm natural light. At the center, a physician in scrubs studies patient analytics on a sleek touchscreen console branded prominently with the HCA Healthcare logo. In the background, nurses and technicians move through a modern ward, digital charts floating on integrated displays, illustrating the company’s strategic scaling of artificial intelligence across its network.

HCA Healthcare’s strategic approach to scaling artificial intelligence - HCA Healthcare Today

- Michael Schlosser leads HCA Healthcare's digital transformation, focusing on AI-driven clinical enhancements.  
- HCA Healthcare faces pressure to improve care amidst rising operational complexities and workforce strains.  
- The organization implements a comprehensive AI strategy to optimize efficiency and improve patient outcomes.

HCA Healthcare’s strategic approach to scaling artificial intelligence

As healthcare organizations strive to scale artificial intelligence solutions, they often encounter significant hurdles that become evident during this phase rather than at the initial pilot stage. A recent survey highlights several key challenges that impede progress:

- Integration challenges with existing legacy systems (29%)
- Regulatory and compliance issues (29%)
- Concerns regarding security and privacy (29%)
- Problems related to data quality, availability, and lineage (27%)
- Shortages in talent and necessary skills (27%)

Participants in focus groups emphasized that the process of scaling AI puts pressure on infrastructure, operational processes, and overall preparedness in ways that pilot projects typically do not reveal. Many leaders noted that the primary obstacles stem less from the AI tools themselves and more from deficiencies in the associated workflows. 

One focus group participant remarked, "An effective AI solution becomes obsolete if it doesn't align with existing workflows. It’s crucial to analyze the workflow thoroughly first."

Additionally, the implementation of AI tends to highlight pre-existing weaknesses within organizations. As a result, IT teams are often redirected to address issues related to data quality, lineage, and accessibility that AI implementation brings to light.

AI in healthcare: Transitioning from broad use to operational responsibility

A bright, sunlit hospital operations room where two doctors stand beside a sleek touchscreen display showing AI-driven diagnostic graphs on one side and an “Operational Accountability Log” checklist on the other; shafts of morning light flood through large windows, highlighting the blend of human care and cutting-edge technology.

AI in healthcare: From wide adoption to operational accountability - RSM US LLP

- Healthcare organizations face challenges scaling AI due to legacy system integration issues.  
- Regulatory compliance and security concerns are significant inhibitors of AI implementation.  
- Participants emphasize the importance of aligning AI with existing workflows for successful scaling.  

Scaling AI brings operational friction into focus

Momentum often slows not at the pilot stage, but as healthcare organizations try to scale AI. According to the survey the top inhibitors are:

Integration with legacy systems (29%)

Regulatory or compliance constraints (29%)

Security and privacy concerns (29%)

Data quality, availability and lineage issues (27%)

Talent and skills gaps (27%)

Focus group participants reinforced that scaling stress tests infrastructure, processes and readiness in ways pilots never do. Leaders pointed less to the tools themselves and more to gaps in the surrounding workflows.

“Every good AI fails if it doesn’t fit the workflow,” said one participant. “You really have to take a look at the workflow first.”

AI also exposes upstream weaknesses, with IT effort shifting toward fixing the data quality, lineage and access issues that AI surfaces.

AI in healthcare: From wide adoption to operational accountability

In June 2026, Rhode Island's Governor Daniel McKee enacted three significant bills related to artificial intelligence (AI) in healthcare. These laws seek to address issues stemming from the application of AI products, companion tools, and mental health service technologies.

The three newly signed pieces of legislation include:

1. The Use of Artificial Intelligence by Healthcare Providers Notification Act (S 2570), effective June 22, 2026
2. The Regulatory Provisions—Artificial Intelligence Companion Models Act (S 2195), effective January 1, 2027
3. The Oversight of Artificial Intelligence Technology in Mental Health Care Act (S 2197), effective June 22, 2026

The Use of Artificial Intelligence by Healthcare Providers Notification Act stands alone, while the other two acts form part of a comprehensive healthcare legislative package that aims to ensure the responsible application of AI in the healthcare sector, contributing to its stability and sustainability.

**Details of the AI Providers Notification Act**

This act mandates that patients be informed when AI technology is utilized by healthcare providers. All licensed healthcare professionals and facilities are required to notify patients about the use of AI tools during both in-person and telehealth consultations. Additionally, after each visit, healthcare providers must verify the accuracy of the AI-generated documentation.

**Focus on AI Companion Tools**

The Regulatory Provisions—Artificial Intelligence Companion Models Act concentrates on disclosure requirements and protocols to prevent self-harm associated with AI companion tools. These systems leverage AI and emotional recognition to create a semblance of human interaction, remembering previous user exchanges and engaging them with targeted emotional inquiries.

Certain systems—primarily for customer service, efficiency enhancement, or internal operations—are exempted from the definition of AI companions. Under this law, an "Operator" encompasses any entity involved in running or providing an AI companion tool. Operators are required to include safeguards for addressing issues of self-harm or suicidal thoughts and must immediately refer users to crisis services if such expressions occur.

Operators will also need to give clear notifications to users at the onset of interaction and subsequently every three hours, reminding them that they are not conversing with a human being. Starting July 1, 2027, these operators must submit annual reports to the Rhode Island Office of Attorney General detailing the activation of safety measures, among other metrics. The Attorney General is empowered to pursue enforcement actions against noncompliant operators, with penalties of up to $15,000 per day.

**Regulation of AI in Mental Health Care**

The Oversight of AI Technology in Mental Health Care Act places limitations on AI tools used for emotional and supplementary support within mental health services, allowing only licensed professionals, such as psychiatrists, to use them. Consent from the patient is essential for the use of any supplementary AI in therapeutic settings, and AI must not be employed for making treatment decisions.

Additionally, therapy services via AI cannot be publicly marketed unless they are administered by licensed professionals. Licensed healthcare providers can engage AI for administrative purposes but must oversee its use judiciously. If they opt to incorporate AI for emotional simulations, they must initially inform the patient about the AI's use and obtain written consent.

Should patients independently choose to use predictive AI tools, healthcare providers are encouraged to discuss the risks and limitations associated with such software, while remaining responsible for the confidentiality and safety of the patient.

This act reinforces patient confidentiality standards as outlined in the Confidentiality of Health Care Communications and Information Act, and the Executive Office of Health and Human Services holds the authority to investigate any potential breaches.

Certain exceptions apply to the act, including religious counseling, peer support, and public self-help materials that do not claim to provide therapeutic services. Additionally, any AI tools endorsed by the FDA or authorized federal agencies for healthcare use are exempt, as are AI systems evaluated by a healthcare facility’s institutional review board for research purposes.

**Looking Ahead**

As the integration of AI in healthcare continues to evolve, these new regulations introduce compliance demands for various stakeholders in Rhode Island, encompassing licensed healthcare providers, facilities, and AI service operators.

Rhode Island enacts three bills on AI in healthcare

Sunlit scene outside Rhode Island’s State House at dawn, three oversized parchment-style bills standing upright in front of the steps—each printed with subtle circuit‐board patterns and medical symbols (caduceus, heart monitor lines), a stethoscope draped over one scroll. The Rhode Island flag waves gently beside them. Warm natural light bathes the building’s white columns, suggesting legislative progress and hope in AI‐powered healthcare.

Rhode Island joins the wave of states regulating AI in healthcare and AI companion tools - Nixon Peabody

- Governor Daniel McKee signed three AI healthcare bills addressing AI’s impact on patient care.  
- The laws require notification to patients about AI usage during healthcare visits and procedures.  
- AI tools in mental health are restricted to licensed professionals with patient consent and oversight.  

In June 2026, Governor Daniel McKee signed three new artificial intelligence (AI) healthcare bills into law. These acts aim to address concerns arising from the use of AI products, AI companion tools, and AI tools used in the provision of mental health services.

The Use of Artificial Intelligence by Healthcare Providers Notification Act (S 2570) (effective June 22, 2026)

The Regulatory Provisions—Artificial Intelligence Companion Models Act (S 2195) (effective January 1, 2027)

The Oversight of Artificial Intelligence Technology in Mental Health Care Act (S 2197) (effective June 22, 2026)

The Use of Artificial Intelligence by Healthcare Providers Notification Act was enacted as a standalone law. Rhode Island’s General Assembly announced The Regulatory Provisions—Artificial Intelligence Companion Models Act and The Oversight of Artificial Intelligence Technology in Mental Health Care Act as part of a 12-piece healthcare legislation package designed to support the stability and sustainability of the healthcare system by enforcing the safe implementation of AI.

Overview of the use of AI by Healthcare Providers Notification Act

The Use of AI by Healthcare Providers Notification Act ensures patients are properly notified of the use of AI by healthcare providers during their visit. All licensed healthcare providers and healthcare facilities must notify patients of AI technology being used for the purpose of documenting in-person or telehealth visits. At the conclusion of the visit, the healthcare provider or facility must review the documentation for accuracy.

Overview of AI companion tools for emotional recognition and human-like relationships

The Regulatory Provisions—Artificial Intelligence Companion Models Act focuses on disclosure and self-harm prevention protocols for AI companion tools. These are systems that use artificial intelligence, generative AI, and/or emotional recognition algorithms to simulate a sustained human-like relationship with users by retaining information from prior interactions, asking unprompted, emotion-based questions, and sustaining an ongoing dialogue about matters personal to the user.

Systems that are used solely for customer service, systems primarily designed and marketed for efficiency improvements or research/technical assistance, and systems used solely for internal business purposes or employee productivity are exempt from the definition of “AI companion” tools.

Under the new legislation, an “Operator” is any person, partnership, business entity, or affiliate that operates or provides an AI companion tool. Operators are prohibited from providing an AI companion tool unless it contains a protocol for addressing potential self-harm, physical harm to others, or suicidal ideation. If such expressions are made, safety protocols are activated and the tool must immediately provide the user with a referral to a crisis service provider, such as a suicide hotline, crisis text line, or other appropriate crisis service.

Additionally, the operators of the AI companion tools must ensure that they provide clear and conspicuous notifications upon the initiation of use, and then every three hours thereafter, reminding the user, either verbally or in writing, that they are not communicating with a human.

Beginning on July 1, 2027, operators will be required to file annual reports with Rhode Island’s Office of Attorney General that include the number of safety protocols activated by the tool and other related metrics. The legislation provides the attorney general with the enforcement power to investigate, sue, and seek injunctions for AI companion operators’ noncompliance in the use of these AI companion tools. Violators may incur civil penalties of up to $15,000 per day.

Overview of regulatory oversight of AI use in mental health care

The Oversight of AI technology in Mental Health Care Act, restricts the use of supplementary/emotional support AI tools in the mental health setting to licensed healthcare professionals, including psychiatrists, that hold a license in Rhode Island to provide therapy or psychotherapy services, and only where the patient has provided prior written consent. The Act also prohibits its use in treatment decision-making. Licensed healthcare professionals are strictly prohibited from using AI products to (i) make independent therapeutic decisions in the patient plan of care, (ii) directly interact with patients in any form of therapeutic communication without an established relationship as defined by the Act with a licensed healthcare professional, or (iii) determine therapeutic recommendations or treatment plans.

Further, therapy or psychotherapy services, including through the use of internet-based AI may not be offered to the public unless the services are conducted by a licensed healthcare professional, which prevents companies from providing or advertising internet-based AI companion tools for the purpose of providing therapy services over the internet if such services are not provided by a licensed healthcare professional. A licensed healthcare professional may use AI to assist in providing administrative and supplementary support provided the licensed healthcare professional maintains appropriate clinical judgment and oversight of patient use of the AI tool. Notably, licensed healthcare professionals are not responsible for vendor-controlled system designs, algorithms, or outputs.

If a licensed healthcare professional uses permitted AI tools designed to simulate emotional attachment, bonding, dependency, or in the provision of supplementary support in services where the session is recorded or transcribed, they must first inform the patient or the patient’s parent/representative of the use of AI and its purpose in writing and obtain written consent.

If a patient voluntarily discloses their use of AI features on their own time that can make predictions, recommendations, or influence decisions, the licensed healthcare professional may discuss the use of the software and offer guidance as to the risks, limitations, and safety to the patient. The licensed professional retains responsibility for the confidentiality of the record and monitoring and intervening when necessary for the safety of the patient. This provision will likely be difficult to operationalize in practice, as the statute does not define what constitutes adequate monitoring of a patient’s safety in connection with their independent AI use.

The Act’s general confidentiality obligations for patient records and communications are backed by the penalties set out in the Confidentiality of Health Care Communications and Information Act, and the Executive Office of Health and Human Services has authority to investigate any actual, alleged, or suspected violation of the Act.

Religious counseling, peer support, and self-help education materials and educational resources that are available to the public and do not purport to offer therapy services are exempt from the new law. Additionally, this law exempts any AI tool reviewed and cleared by the FDA or any other federal agency tasked with approving AI algorithms for use in healthcare, as well as tools used for research approved by a healthcare facility’s institutional review board.

Going forward

As AI continues to be integrated into healthcare and commercial products, these three laws impose new compliance obligations on a range of stakeholders in Rhode Island, including licensed healthcare providers and healthcare facilities as well as operators of AI companion technology.

Rhode Island passes three AI healthcare bills

A team led by Dr. R. Andrew Taylor from UVA Health and Dr. Arwen B.L. Declan of Clemson University has introduced a new framework designed to assist hospitals in incorporating artificial intelligence (AI) in ways that prioritize both operational efficiency and patient care quality.

Recognizing the transformative potential of AI in healthcare, Taylor and Declan aimed to maintain the sector’s ethical commitments to patients and local communities while navigating the urgency for hospitals to swiftly implement AI solutions—sometimes without adequate guidelines to assess their value beyond mere financial metrics. Their newly proposed Total Mission Value framework is particularly timely, given the increasing urgency for healthcare institutions to adopt AI technologies.

At the core of their framework is a hierarchy that places patient care and the overall patient experience at the pinnacle of a structure grounded in ethical standards, all while being reinforced by economic sustainability. They emphasize that AI should enhance the capabilities of healthcare professionals rather than merely add to their burdens or replace them.

Taylor, who holds the position of vice chair of research and innovation in the Department of Emergency Medicine at the University of Virginia School of Medicine, remarked, “AI is being integrated into medicine at an unprecedented pace, yet hospitals lack a cohesive evaluation method for these technologies. Conventional assessments often focus solely on costs, as they are the most straightforward to gauge. This framework is intended to offer organizations a structured approach to evaluate the comprehensive benefits of an AI tool—taking into account its impact on patients, staff, and care quality.”

In their published paper, the authors acknowledge the dual aspects of AI’s potential. “While AI could expedite care delivery, enhance diagnostic precision, and improve cost-efficiency, it also risks introducing biases, reducing transparency, displacing the workforce, and undermining the patient-clinician relationship,” they caution.

They point out the conventional focus on cost in technology evaluations creates a gap in comprehensive decision-making. Their framework aims to integrate five ethically centered priorities: patient care, staff experience, hospital operations, financial implications, and educational and research activities.

Placing patient care at the framework's summit underscores the need for “patient-centered” practices characterized by integrity, trust, compassion, and respect. In addition, the category focusing on staff experience advocates for harnessing AI to foster team development and interdisciplinary collaboration.

Declan highlighted the importance of assessing AI tools more broadly. “With a plethora of new AI products being promoted to enhance healthcare, the challenge lies in identifying which truly deliver benefits,” she explained, emphasizing the necessity to evaluate AI's impact across clinical, operational, and financial spectrums while keeping patient welfare as the guiding principle in every evaluation.

In closing, Taylor and Declan stress the importance of keeping patient care as the healthcare sector's “central, defining mission.” 

Taylor added, “We believe that maintaining this mission at the forefront will not only facilitate robust AI adoption but will also foster the necessary trust among patients and clinicians. Ultimately, technology should enable us to improve patient care, and if we prioritize this goal, improved efficiency and cost savings are likely to follow.”

The framework has been published in the open-access journal npj Digital Medicine, making it available for public reading. Taylor has also disclosed that he received a grant from Beckman Coulter for the assessment of a clinical decision-making tool known as TriageGo and serves as an advisor for VeraHealth.

For those interested in staying updated on the latest advancements in medical research from UVA and the new Paul and Diane Manning Institute of Biotechnology, the Making of Medicine blog is a valuable resource.

Medical Professionals Establish Guidelines for AI's Future Role in Healthcare

A sunlit hospital conference room scene: four doctors in white coats stand around a large glass whiteboard, sketching a glowing neural-network brain icon at the top and jotting down bullet-point principles beneath. Warm natural light pours through tall windows overlooking a tranquil hospital courtyard, illuminating their tablets, clipboards, and thoughtful expressions as they collaborate on the future of AI in healthcare.

Doctors Develop Guiding Principles for Future of AI in Healthcare - UVA Health

- UVA and Clemson doctors created a framework for integrating AI in healthcare responsibly.  
- The framework prioritizes patient care, ethics, and economic sustainability in AI adoption.  
- It aims to evaluate AI tools' impact on clinical, operational, and financial dimensions.  

A UVA Health emergency medicine doctor and colleague at Clemson University have developed a framework to help hospitals integrate artificial intelligence to not just increase efficiency and cut costs but ensure high-quality patient care remains the top priority.

With the massive potential of artificial intelligence to transform healthcare in the coming years, UVAâs R. Andrew Taylor, MD, MHS, and Clemsonâs Arwen B.L. Declan, MD, PhD, created the new framework to ensure healthcare remains ârooted in its ethical obligationsâ to serve patients, communities and local workforces, they say in a new paper outlining their creation. Their Total Mission Value framework arrives as hospitals face mounting pressure to adopt AI quickly, often with little practical guidance on how to weigh a toolâs worth beyond its price.

Declan and Taylorâs framework puts patient care and the patient experience at the top of a pyramid built on a foundation of ethics and supported by a base of economic sustainability. It emphasizes that AI should support care providers in their mission rather than replace them or simply create more work for them.Â

âAI is being adopted in medicine at a scope and velocity we have never seen before, but hospitals havenât had a good way to weigh these decisions as a whole,â said Taylor, vice chair of research and innovation for the University of Virginia School of Medicineâs Department of Emergency Medicine. âTypical approaches tend to measure cost, because cost is the easiest thing to measure. We built this framework to give organizations a structured way to also weigh what an AI tool does for patients, for staff and for the quality of care.â

The Future of AI in Healthcare

Declan and Taylor are candid that AIâs promise cuts both ways. âAI tools could enhance care speed, diagnostic accuracy and costs efficiency while supporting population health, scientific inquiry and operational management,â they note in their paper. âHowever, they also introduce risks of bias, opacity, workforce displacement and erosion of the patient-clinician relationship that are invisible to cost-focused analyses.â

Declan and Taylor note that hospitals have lacked a structured way to weigh these choices as a wholeâthe standard approaches for evaluating a new technology tend to center on cost. Their framework is built to fill that gap, integrating five âethically groundedâ priorities: patient care, staff experience, hospital operations, economic impact, and education and research.

Patient care, which the authors place at the top of the framework, emphasizes the importance of âpatient-centeredâ care and traits such as integrity, honesty, trust, compassion and respect. The staff-experience category, meanwhile, calls for hospitals to use AI to drive workforce development, teamwork and collaboration across disciplines.

Declan emphasizes that realizing AI's full potential means resisting the urge to evaluate it narrowly.Â âHospitals are seeing a huge number of new AI tools marketed to improve healthcare. The challenge is to figure out which ones actually will,â said Declan,Â clinical assistant professor in Clemson University's School of Health Research. âThat requires weighing an AI toolâs impact across clinical, operational and financial dimensions, while keeping patient care at the center of every decision.â

Ultimately, it is vital that hospitals remember that patient care is their âcentral, defining mission,â Declan and Taylor write.

âOur hope is that keeping the mission front and center actually speeds good AI adoption rather than slowing it down, because it builds the trust that patients and clinicians need,â Taylor said. âTechnology should help us take better care of people. If we keep that as the goal, the efficiency and the savings tend to follow.â

Framework Published

Taylor and Declan haveÂ unveiled their framework in the scientific journal npj Digital Medicine. The article is open access and free to read.Â

Taylor noted that he has received a grant from Beckman Coulter to support evaluation of a clinical decision-making algorithm called TriageGo and that he is an adviser for VeraHealth.

To keep up with the latest medical research news from UVA and UVAâs new Paul and Diane Manning Institute of Biotechnology, bookmark theÂ Making of MedicineÂ blog.

Doctors Develop Guiding Principles for Future of AI in Healthcare

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Dr. Peter A. Najjar describes AI in healthcare as 'early stage' yet 'very promising'

A bright hospital corridor flooded with warm morning sunlight streaming through large windows. Dr. Peter A. Najjar, in a crisp white lab coat embroidered with his name, stands holding a sleek tablet that displays a glowing 3D heart model overlaid with dynamic AI-driven analytics charts. His face lights up with excitement as he points to the screen. In soft focus behind him, modern medical monitors and equipment hint at the cutting-edge setting—a hopeful glimpse of healthcare AI’s early days.

Dr. Peter A. Najjar says AI in healthcare is ‘early days’ but ‘very exciting’ - Fox Business

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Dr. Peter A. Najjar says AI in healthcare is ‘early days’ but ‘very exciting’

The Wisconsin Department of Natural Resources has issued an air quality warning affecting several counties, including Adams, Buffalo, Clark, Crawford, Grant, Jackson, Juneau, La Crosse, Monroe, Richland, Taylor, Trempealeau, and Vernon. The air quality index (AQI) for fine particulate matter (PM2.5) is anticipated to escalate into the Very Unhealthy (Purple) to Unhealthy (Red) categories statewide. This advisory will remain in effect until noon on Monday.

For those in sensitive groups facing a Very Unhealthy AQI, it is advised to refrain from any outdoor physical activities. Consider rescheduling such activities for when air quality improves or moving them indoors. Individuals with asthma are urged to adhere to their asthma management plans and to keep fast-acting medication readily available. Those with heart conditions should be vigilant for any symptoms such as palpitations, shortness of breath, or unusual fatigue, and should seek medical advice if these occur. The general public is encouraged to minimize outdoor physical exertion and to retreat indoors if they experience any symptoms.

In the case of an Unhealthy AQI, sensitive individuals should also contemplate moving activities indoors or postpone them until air quality improves. People with asthma should continue to follow their action plans and have quick-relief medications accessible. Anyone with heart issues should remain vigilant for adverse symptoms and contact healthcare providers if necessary. Lastly, the broader population should aim to limit the duration and intensity of outdoor activities and seek shelter if they notice any health symptoms.

The current conditions point to another wave of smoke originating from the north-northeast, expected to impact Wisconsin throughout Saturday, with effects persisting into Sunday. Most regions are predicted to experience an Unhealthy AQI, particularly in the northeast where Very Unhealthy levels may occur. Conversely, swaths of the southwestern region may only reach an Unhealthy for Sensitive Groups (Orange) AQI. It is worth noting that this smoke may not reach the southwestern parts of the state until after sunset on Saturday, resulting in statewide air quality effects expected by 6 a.m. on Sunday. For the latest updates on air quality, visit: https://airquality.wi.gov.

A leading global hospital is exploring the transformative potential of AI in health care.

A bright, sunlit hospital research wing with floor-to-ceiling windows, where a doctor in crisp white scrubs studies a large touchscreen display of 3D medical scans feeding from a sleek, AI diagnostic unit beside a patient bed—natural light pouring in to highlight cutting-edge innovation and hope in health care.

One of the world’s most prominent hospitals is testing how AI can revolutionize health care - WXOW

- Wisconsin issues Air Quality Advisory for multiple counties, effective until noon Monday.  
- PM2.5 levels are expected to reach Very Unhealthy and Unhealthy classifications statewide.  
- Residents urged to minimize outdoor activities, especially those with asthma or heart disease.

Weather Alert

The Wisconsin Department of Natural Resources has issued an Air Quality Advisory for the following counties: Adams, Buffalo, Clark, Crawford, Grant, Jackson, Juneau, La Crosse, Monroe, Richland, Taylor, Trempealeau, Vernon WHAT...The PM2.5 AQI is expected to reach the Very Unhealthy (Purple) to Unhealthy (Red) level. WHERE...Statewide. WHEN...Until noon Monday. Precautionary/Preparedness Actions: For Very Unhealthy (Purple) AQI: Sensitive groups: Avoid all physical activity outdoors. Reschedule to a time when air quality is better or move activities indoors. People with asthma: Follow your asthma action plan and keep quick-relief medicine handy. People with heart disease: Symptoms such as palpitations, shortness of breath, or unusual fatigue may indicate a serious problem. If you have any of these, contact your health care provider. Everyone else: Limit outdoor physical activity. Go indoors if you have symptoms. For Unhealthy (Red) AQI: Sensitive groups: Consider rescheduling or moving all activities inside. Go inside if you have symptoms. People with asthma: Follow your asthma action plan and keep quick-relief medicine handy. People with heart disease: Symptoms such as palpitations, shortness of breath, or unusual fatigue may indicate a serious problem. If you have any of these, contact your health care provider. Everyone else: Keep outdoor activities shorter and less intense. Go inside if you have symptoms. Additional Details: Another round of smoke will move in from the north-northeast throughout the day on Saturday before settling over the state on Sunday. Most areas are expected to observe Unhealthy (Red) AQI PM2.5, with the heaviest impacts to the northeast where Very Unhealthy (Purple) AQI PM2.5 will be possible. Some areas to the southwest may only experience Unhealthy for Sensitive Groups (Orange) AQI PM2.5. This round of smoke may not reach the southwestern half of the state before sunset Saturday, meaning the smoke will arrive overnight, with statewide impacts expected by 6 a.m. Sunday morning. For more information on current air quality, please see: https://airquality.wi.gov

One of the world’s most prominent hospitals is testing how AI can revolutionize health care

Mark Cuban responded skeptically to claims about artificial intelligence outperforming human doctors, highlighting a significant discrepancy between the optimistic portrayals of the tech industry and the actual experiences of patients. A survey conducted by the West Health-Gallup Center on Healthcare in America revealed that about 25% of U.S. adults have consulted AI for health-related inquiries.

On July 11, OpenAI CEO Sam Altman shared these survey results, which were quickly endorsed by venture capitalist Marc Andreessen, who remarked that AI is now a superior doctor compared to 99.99% of human medical professionals.

The comparison was drawn from scenarios that had previously puzzled earlier OpenAI models. The results indicated that doctors rated AI's responses as flawless between 19.8% and 23.9% of the time, while human-written answers scored a mere 10.4%.

On July 10, Karan Singhal, head of OpenAI's health research team, released findings from the latest model iteration, GPT-5.6. In blind assessments, healthcare professionals evaluated this AI’s performance on various patient-focused and clinician-focused tasks.

Cuban’s retort on the social media platform X pointed out that over 25% of a physician's time is consumed by dealing with healthcare conglomerates that complicate both the care process and cost-effectiveness for providers and patients alike. His concern isn't about the AI's performance per se, but rather the systemic issues in healthcare that persist even after an accurate diagnosis is made, particularly regarding how insurance companies utilize AI to stall claim approvals instead of facilitating timely care.

On July 12, Andreessen celebrated the AI model's higher performance in a blind test, echoing Altman's claims while also pointing out the detrimental role that intermediaries play in healthcare. He illustrated that these entities are creating AI systems intended to "delay and deny."

In response to the increasing reliance on AI, Cuban noted that many patients remain skeptical of its accuracy, with only 4% of users expressing strong trust in AI's reliability, and 11% reporting that AI provided advice they deemed unsafe. The disconnect lies in patients' apprehensions about the motivations behind AI's guidance.

Cuban’s argument essentially underscores the fact that even if AI tools can optimize certain processes, they don’t address the foundational challenges posed by insurance companies and related entities that prioritize profits over patient care. He drew attention to the financial burden placed on hospitals, which often outsource revenue cycle management, profiting from the delay in claim recovery.

He pointed to his investment in Claimable, a startup leveraging AI to draft appeal letters for patients with denied insurance claims. Claimable, which has secured $10 million in funding, boasts a success rate where approximately 75% of appeals result in a favorable outcome for the patient, illustrating the perpetual confrontation between patient advocacy through technology and insurer resistance.

Unfortunately, many patients remain unaware of this appeal process. According to the KFF, less than 1% of denied claims from Affordable Care Act marketplace plans were challenged in 2023. Of those who did contest their denials, insurers upheld 56% of the rejections, reaffirming the issues Cuban describes.

Cuban’s proposed solution centers on enhancing pricing transparency within the healthcare system. He highlighted the lack of understanding employers have regarding their healthcare expenditures, advocating for direct contracting between employers and healthcare providers. By sidestepping middlemen and negotiating terms directly, he aims to streamline the healthcare process.

To this end, Cuban launched Cost Plus Wellness, an initiative designed to divulge contracts between employers and providers, fostering an environment where others can replicate effective agreements. To date, they have made 37 contracts public, benefitting over 9,000 providers, particularly within the Dallas-Fort Worth area.

Cuban argues that a technologically advanced AI does not inherently lower healthcare costs. Even if AI excels in diagnosis, patients still face bureaucratic hurdles such as prior authorizations and formularies before receiving treatment.

He concluded his message with a stark prediction: unless the middlemen are eliminated from the healthcare equation, the system will remain fundamentally flawed.

Mark Cuban cautions that AI in healthcare will only assist insurers in 'delaying and denying' — responding to Andreessen's assertion about AI doctors.

A dark, cinematic insurance claims office at night: rows of shadowed desks with mechanical arms stamping bold red “DELAY” and “DENY” on medical claim folders. A sleek chrome robot in a lab coat holds a clipboard labeled “AI Healthcare,” standing impassively as claims pile up. In the background, a large screen bathed in warm spotlight shows Mark Cuban’s silhouette pointing defiantly, warning against the system. Deep shadows, moody spotlights, and a tense, film-noir atmosphere.

Mark Cuban warns AI healthcare will just help insurers 'delay and deny' — firing back at Andreessen's AI doctor claim - Yahoo Finance Singapore

- Mark Cuban argues AI's improved diagnosis doesn't address systemic healthcare insurance issues.  
- Survey shows only 4% of users strongly trust AI's medical accuracy, raising patient concerns.  
- Cuban advocates for direct contracting between employers and providers to bypass insurance middlemen.

This led Cuban to reply with skepticism the same day. His perspective reflects a massive gap between tech industry hype and actual patient reality. A West Health-Gallup Center on Healthcare in America survey (5)released April 15 found that one in four U.S. adults has turned to AI at some point with a health question (5).

OpenAI CEO Sam Altman (3)reposted (3) the findings on July 11. Andreessen (4)amplified it (4) the day after with a one-line verdict: "AI is already a better doctor than 99.99% of human doctors."

And these were cases picked specifically because they had stumped earlier OpenAI models. The AI came out ahead by a wide margin: Physicians rated its answers as flawless between 19.8% and 23.9% of the time, depending on the variant, compared with just 10.4% for the physician-written responses (2).

On July 10, Karan Singhal, who leads OpenAI's health research team, (2)posted results from the company's newest model family, GPT-5.6. In blinded reviews, physicians rated GPT-5.6's answers to a set of patient-facing and clinician-facing health tasks.

"25 pct or more of a doctor's time is spent dealing with conglomerates that do all they can to make the doctor's care more difficult and expensive, for both the doctor and patient," Cuban replied in a quote on (1)X (1).

Cuban's rejection wasn't against the score itself, but was aimed at what the two were celebrating, because of what happens after a good diagnosis reaches an insurance company, where the same industry is already using AI to slow claims down rather than pay them out.

On July 12, Marc Andreessen, a venture capitalist, was celebrating an AI model that scored better than real doctors on a blind test, with a post quoting Sam Altman's similar assertion.

Then he went straight for the businesses sitting between patients and their doctors, which, he warned, are building AI agents designed to "delay and deny (1)."

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Mark Cuban read a post on X claiming artificial intelligence had already become a better doctor than almost every physician in the country.

Story continues

Among those who had used one in the past 30 days, only 4% said they strongly trust its accuracy and 11% said an AI tool had given them advice they believed was unsafe. Cuban's reply landed in that gap because he knew that in the real world, patients aren't just worried about whether the AI is smart enough; they're worried about who is pulling its strings.

Read More: Are you paying too much for car insurance? Here are 3 clever ways to slash your monthly bill

Why Cuban says the benchmark misses the point

Cuban's argument starts with a number of his own: He says a quarter or more of a doctor's working day already goes to fighting insurers, pharmacy benefit managers and other intermediaries, not treating patients. Handing doctors a faster AI assistant doesn't remove that friction, he argued. It just guarantees the businesses on the other side build faster AI of their own, tools designed to slow down and reject claims rather than approve them (1).

Cuban says this is already happening. Hospitals, he says, pay outside revenue cycle management firms — companies whose whole job is chasing down the money insurers owe — as much as 10% of revenue for the service. He also likened where this is headed to the old Mad magazine cartoon where two spies keep building sharper weapons against each other and neither one ever wins (1).

Actually, there's a real-world test case sitting in Cuban's own investor portfolio. He's an investor in Claimable, a startup that uses AI to write appeal letters for patients whose insurance claims get denied. The startup (6)counts Cuban among the backers behind its $10 million in funding (6), according to Inc., and (7)Bloomberg reported (7) that roughly three out of every four people who appeal through it win a reversal.

In other words, patients are arming themselves with AI appeals, and insurers have every reason to answer with AI denials — Cuban's warning, running in miniature.

And most people never get that far. On Affordable Care Act marketplace plans, for instance, not even 1 in 100 denied claims was challenged by patients in 2023 (8) — and among those who did push back, insurers stood by 56% of the denials, according to KFF, the nonpartisan health policy research group that analyzed the federal claims data.

Bloomberg's reporting on Claimable put a face on that math. One user, a patient managing a chronic inflammatory condition, had relied on the same infusion drug for more than half his life. When his employer switched insurers, the new carrier pushed him toward a cheaper alternative, and a single missed dose during the dispute was enough to trigger a painful flare-up (7).

The medicine worked fine on its own for years. What broke down was a coverage decision made by someone with no stake in his outcome — precisely the friction Cuban is describing.

What Cuban wants employers to do instead

Cuban's fight is over pricing information. He points out in the X post that not a single company, including those run by executives arguing with him online, actually knows what it's paying for the health care it buys its own employees. They see a total bill, but the contract behind that bill stays hidden (1).

To fix this problem, he suggests direct contracting, whereby employers negotiate and pay providers directly, with no carrier. That is, you, as an employer, should avoid third-party administrators, pharmacy benefit managers or any middleman who decides which drugs a plan covers and at what price. He has put money behind the idea.

Cuban's company runs Cost Plus Wellness, a free platform (9)launched in January (9) that publishes contracts between employers and providers so other companies can copy the terms. So far, (10)37 of those contracts are public on the site, reaching more than 9,000 providers (10), according to Becker's — most of them clustered in the Dallas-Fort Worth market, where Baylor Scott & White Health has signed on.

Why a better AI doctor won't lower your bill

For anyone whose premiums climb every January, the benchmark charts miss where the money actually goes. An AI can nail the diagnosis on the first try, and the treatment still has to clear a prior authorization (the insurer's sign-off before care is approved), a formulary (its list of covered drugs) and an appeals queue before anyone gets better.

Cuban ended his post with a prediction blunter than any benchmark. Until the conglomerates are cut out of the middle, he wrote, health care in this country "will continue to be f—ed (1)."

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Article Sources

We rely only on vetted sources and credible third-party reporting. For details, see our ethics and guidelines.

X (1), (2), (3), (4); Gallup (5); Inc. (6); Bloomberg (7); KFF (8); MedCity News (9); Becker's Health IT (10)

This article originally appeared on Moneywise.com under the title: Mark Cuban warns AI healthcare will just help insurers 'delay and deny' — firing back at Andreessen's AI doctor claim

This article provides information only and should not be construed as advice. It is provided without warranty of any kind.

Mark Cuban warns AI healthcare will just help insurers 'delay and deny' — firing back at Andreessen's AI doctor claim

The ongoing conflict between healthcare providers and insurers has evolved into a sophisticated contest dominated by algorithms, where AI-driven tools for prior authorizations face off against those designed for appeals.

Ashis Barad has firsthand experience from both perspectives of this struggle. He began his professional journey as a physician at Sutter Health and later worked at Baylor Scott & White Health, where he was instrumental in advocating for advanced digital solutions. In 2022, Barad took on the role of chief digital and information officer at Allegheny Health Network and its parent organization, Highmark Health, gaining insights into how insurers develop and implement AI technologies. Two years later, he transitioned back to the provider side by joining the Hospital for Special Surgery (HSS) in the same executive role.

Barad asserts that the ongoing race to automate authorizations and appeals is ultimately a misguided approach. 

“We're engaged in a limited competition while ignoring the broader, ongoing challenge,” Barad pointed out. “If we continue to focus solely on the immediate concerns, we will persist on an inflationary trajectory. We must collaborate to determine how AI can lead us towards a more efficient, cost-effective model because that is what our patients deserve.”

Rather than funneling resources into AI for swifter authorizations and appeals, Barad envisions a future where providers and payers share data to create more individualized care protocols.

He highlighted HSS as a prime example, noting that the institution conducts over 40,000 orthopedic surgeries annually, surpassing any other hospital in the United States. HSS has successfully created a comprehensive database that links imaging results with surgical outcomes.

Such extensive real-world data is a significant gap in the information available to insurers. Barad believes that if payers and providers collaborated to develop these models, authorization processes could become more precise and tailored to individual patients, rather than relying on generic guidelines.

He has already tested this concept in discussions with payers. During a recent meeting with a prominent insurance provider, he inquired about the potential value of HSS's integrated imaging and outcomes data for their actuarial teams.

“They expressed a keen interest, saying they'd find it extremely valuable,” he shared.

Payers generally have access only to basic claims information, such as rates of infection or readmission following initial surgeries, but they lack data on outcomes for patients undergoing multiple procedures. This lack of detail is significant, especially as many HSS patients are complex cases that fall outside typical metrics.

Barad believes that a transformative opportunity exists in utilizing this richer dataset to fundamentally change authorization processes. He pointed to the practice of “gold carding,” which allows exceptional providers to bypass prior authorizations, as an area ripe for reimagining.

Currently, this practice relies on rudimentary criteria, such as flowcharts and cost limits, rather than actual patient outcomes. Barad envisions a redesigned approach personalized for each patient, integrating authorization seamlessly into care pathways rather than treating it as a separate, post-service request.

However, he warns that as long as providers and payers continue to invest in AI merely to outsmart one another, this competition adds unnecessary costs to an already strained system. He emphasizes that the road to genuine cost reduction lies in cooperation and shared data initiatives.

The conflict between providers and payers over bots is increasing costs for all.

Dimly lit industrial hall with two towering mechanical robots locked in a fierce tug-of-war over a giant, cracked dollar coin. One robot bears a glowing medical caduceus emblem, the other a bold insurance-shield icon. Sparks fly from frayed cables as warm orange light flickers off metal surfaces. In the foreground, silhouettes of worried adults clutching empty wallets watch helplessly under heavy shadows.

The ‘Bot Vs. Bot’ Dynamic Between Providers & Payers Is Driving Up Costs for Everyone - MedCity News

- The conflict between healthcare providers and payers revolves around competing AI tools and strategies.  
- Ashis Barad advocates for collaboration on data to create personalized care pathways.  
- Current autorization methods need to evolve from blanket policies to individualized patient approaches.

The war between healthcare providers and payers has increasingly become a battle of algorithms, with AI tools for prior authorizations on one side and AI-powered appeals on the other.

Ashis Barad has sat on both sides of that fight. He began his career as a physician at Sutter Health, then practiced at Baylor Scott & White Health, where he led the push for better digital tools. In 2022, Barad transitioned into the role of chief digital and information officer at Allegheny Health Network and its parent company Highmark Health, getting a taste of how payers build and deploy AI internally. Two years ago, he joined the Hospital for Special Surgery (HSS) to serve as chief digital and information officer, sitting firmly on the provider side once again.

In his eyes, providers and payers racing to out-automate each other is a losing strategy.

“We’re playing a finite game when there’s an infinite game to be played,” Barad declared. “If we play the finite game of today, we’re going to continue on the inflationary route. So we have to figure out how to work together to figure out what the deflationary path for AI is, because that’s what we owe the people, honestly.”

Instead of pouring resources into AI for faster authorizations and appeals, Barad wants to see providers and payers pool their data to build more personalized care pathways.

He pointed to HSS’ own data as an example of what’s possible. The hospital performs more than 40,000 orthopedic surgeries a year — more than double the volume of any other U.S. hospital — and has developed a structured repository connecting imaging data to surgical outcomes.

Having that kind of real-world data is something insurers lack. Barad thinks that if payers and providers built those models together, authorizations would become far more targeted at the individual level, rather than relying on blanket rules.

Barad said he has already tested the idea with payers directly. During a recent meeting with a large insurance company, he asked how valuable HSS’ linked imaging-and-outcomes data would be to their actuarial teams.

“They were like, we would salivate over it,” he said.

That’s because payers typically only have access to basic claims data — infection or readmission rates after a first surgery, for instance — not the outcomes data for patients on their third, fourth or fifth procedure. But those are the kind of complex cases that make up much of HSS’ patient population, Barad pointed out.

Barad thinks the real opportunity lies in using richer data to reshape how authorization works in the first place. He pointed to gold carding, the practice of exempting high-performing providers from prior authorization, as a concept ripe for reinvention.

Today’s version is a blunt instrument based on flowcharts and cost thresholds rather than actual outcomes, he explained. Built the right way, Barad thinks it could instead be personalized down to each patient. Authorization would be baked into a care pathway, not requested after the fact.

But as long as payers and providers keep investing in AI to outmaneuver each other, Barad warned this spending only adds a new layer of cost to a system already straining under it. He said the path to actually lowering costs runs through collaboration on data.

Photo: Malorny, Getty Images

The ‘Bot Vs. Bot’ Dynamic Between Providers & Payers Is Driving Up Costs for Everyone

The global market for AI in healthcare is projected to soar to $491 billion by 2032. However, the World Economic Forum (WEF) issues a cautionary note about potentially repeating the errors of past digital transformations. 

One significant example highlighted is the implementation of electronic health records (EHRs). While EHRs were expected to enhance coordination and elevate clinical decision-making, their introduction initially led to an increase in administrative burdens. This shift often resulted in healthcare professionals spending more time on documentation than on patient care.

The WEF suggests that rather than becoming the focal point of clinician-patient interactions, AI should function discreetly in the background. The aim should be to streamline healthcare processes, thereby enabling clinicians to dedicate more time to developing trust with their patients.

The key takeaway, according to the WEF, is not that digital technologies are inherently flawed, but rather that innovation lacking a clear focus on human-centered objectives can lead to unforeseen challenges.

Building trust between patients and healthcare providers has been a longstanding concern for the WEF, which has recognized it as essential for the effective integration of AI in the healthcare sector. Shyam Bishen, who heads the Centre for Health and Healthcare at the WEF, emphasized in a 2024 article that “mistrust is the most formidable barrier to adopting generative AI in healthcare.”

WEF: AI Could Enhance the Humanity of Healthcare

A bright, sunlit WEF conference stage with a banner reading “WEF” overhead, a doctor gently resting a hand on an adult patient’s shoulder as an AI-powered tablet beside them glows with soft, warm-toned network graphics, warm natural light illuminating their hopeful expressions.

WEF: AI Has the Potential to Make Healthcare More Human - Healthcare Digital

- The global AI healthcare market is projected to reach US$491bn by 2032.  
- WEF warns against past mistakes, citing problematic electronic health records rollout consequences.  
- Trust is crucial for AI adoption; mistrust is a barrier in healthcare settings.  

With the global AI healthcare market on track to reach US$491bn by 2032, the World Economic Forum (WEF) warns against repeating mistakes made during previous waves of digital transformation.

It cites the rollout of electronic health records (EHRs), which promised better coordination and improved clinical decision-making, but initially contributed to growing administrative workloads, often forcing doctors to spend more time documenting care than delivering it.

Instead of taking centre stage in the clinician-patient relationship, WEF argues AI must operate in the background, removing friction from healthcare delivery while allowing clinicians to spend more time building trust with patients.

The lesson, WEF argues, is not that digital technologies fail, but that innovation without a clear human-centred purpose can create unintended consequences.

Creating trust between patient and clinician

WEF has long recognised trust as a critical factor in the successful adoption of AI in healthcare.

“Mistrust is the most formidable barrier to adopting generative AI in healthcare,” Shyam Bishen, Head of the Centre for Health and Healthcare at WEF, wrote in a 2024 article.

WEF: AI Has the Potential to Make Healthcare More Human

The intersection of agentic artificial intelligence (AI) and the healthcare sector has reached a pivotal juncture. With the rapid pace of technological advancement, many healthcare organizations find it challenging to keep up. This delay is not just a matter of efficiency, as it could lead to postponed care, compromised patient privacy, and a breakdown of trust between clinicians and patients.

Leading organizations in this space are typically those that grasp how agentic AI operates, not necessarily the ones that adopt it the quickest. It’s crucial to comprehend where responsible usage of AI can falter.

Exploring the Framework

While agentic AI might seem like a singular entity, it actually functions through five interconnected layers:

1. **Power Requirements**: Similar to all cloud computing frameworks, robust electrical infrastructure is essential to power the AI agents.
  
2. **Infrastructure/Hardware**: Data centers springing up globally are the repositories for the vast amounts of data that AI agents utilize.

3. **Network Layer**: It’s vital to have advanced security measures in place to protect sensitive data.

4. **Data Layer**: The intricate chain of protocols is necessary for effective digital data transmission.

5. **Application Layer**: The various forms in which users engage with AI agents can differ significantly.

Users must understand how these layers interrelate to identify and address issues effectively. Furthermore, reassessing the potential vulnerabilities at each layer is essential for responsible utilization.

Defining Responsible Use

Discussions around responsible AI often focus on the network, data, and application layers, as these are the ones users interact with most frequently. Security breaches can occur even in well-protected organizations if the data layer isn't managed correctly. For example, if an AI learns to equate a patient's pain rating of 10 with "severe pain" without proper context, it can lead to misleading documentation and consequently harm clinician trust and organizational credibility.

Responsible usage of AI demands more than just a secure infrastructure. Three foundational principles must guide any AI program:

1. **Bias Mitigation**: The medical field has historical examples that illustrate the dangers of biased treatments and protocols. AI trained on historical data can replicate these biases on a large scale. Healthcare organizations must consistently review AI systems for biases related to age, race, gender, and other variables, treating this as a clinical necessity rather than a mere compliance task.

2. **Observability**: Transparency is crucial for all personnel interacting with AI agents. They must understand the agent's designated tasks and performance metrics to ensure it functions within established boundaries.

3. **Explainability**: Articulating the functionality and rationale behind an AI agent's actions is essential. Regular third-party audits are important safeguards that enhance accountability both internally and externally.

Effective governance in AI for healthcare requires more than just compliance with a single standard or certification. 

Adopting a comprehensive strategy is vital. Industry benchmarks such as the National Institute of Standards and Technology’s (NIST) Risk Management Framework and insights from the Open Worldwide Application Security Project (OWASP) provide valuable guidance for navigating AI risk and security. Compliance with HIPAA is crucial for every healthcare AI application. Initiatives like the Coalition for Health AI, which I am part of, aim to solidify principles of agentic AI into standardized best practices across the sector.

However, having frameworks isn’t sufficient on its own. The most effective organizations are weaving these standards into their governance models, tailoring them to their specific workflows, patient demographics, and risk assessments, rather than simply fulfilling compliance requirements. The objective is to cultivate trust that elevates AI from a potential liability to a lasting asset.

Thoughtful Implementation Over Hastiness

Healthcare providers cannot ignore the importance of agentic AI, yet they must also refrain from deploying it without adequate infrastructure for responsible use. Finding the balance between overanalyzing—leading to stagnation—and underanalyzing—resulting in reckless deployment—is crucial for strategic success.

Agentic AI serves as a bridge uniting predictive, generative, and autonomous systems. Establishing this bridge sustainably requires active human involvement to set, monitor, and enforce safety parameters over time.

Challenges in healthcare often arise not from the accuracy of AI models, but from determining accountability when errors occur. The most effective adopters create an environment of clinical and organizational trust, transforming agentic AI into a valuable tool for both healthcare teams and their patients.

Steering the Responsible Integration of Agentic AI in Healthcare

A bright, sunlit hospital ward where a female physician in a white coat stands beside a sleek robotic arm offering her a digital tablet displaying a glowing 3D heart scan. A stethoscope and patient charts lie on a nearby stainless steel tray. Warm natural light streams through large windows, casting soft shadows that emphasize human–AI collaboration and responsible oversight in healthcare.

Navigating Responsible Adoption of Agentic AI in Healthcare - HCI Innovation Group

- The healthcare industry faces challenges in adopting rapidly advancing agentic AI technology responsibly.  
- Responsible AI use requires addressing bias, observability, and explainability principles thoroughly.  
- Successful organizations integrate AI frameworks into their governance tailored to specific healthcare contexts.  

The healthcare industry’s relationship to agentic artificial intelligence has reached an inflection point. The technology is advancing faster than most organizations can adopt it — and in healthcare that gap carries consequences for administrators, clinicians, payers and patients. Unlike other industries where delayed adoption means lost efficiency, in healthcare it can mean delayed care, compromised privacy and erosion of clinical trust.

The organizations that lead aren’t necessarily the ones moving fastest, but those who possess a clear understanding of how agentic AI actually works — and where responsible use can break down.

Understanding the Layers

Agentic AI is often thought of as a single system. Functionally, however, it operates across five interdependent layers:

Power requirement: like all cloud-computing platforms, a strong grid is needed to power the computing of every AI agent

Infrastructure/hardware: the data centers that are springing up around the world are where the data every AI agent uses "lives"

Network layer: sophisticated digital safeguards must be in place to ensure private data remains private

Data layer: digital data delivery requires a complex chain of protocols

Application layer: the way a user interfaces with an AI agent can take a variety of forms

Every agentic AI user depends on all five. To troubleshoot a problem, or improve a process, users must know which layer to target. And to use these systems responsibly, we need to understand where each layer is vulnerable.

What ‘Responsible Use’ Really Means

Most conversations about responsible AI focus on the top three layers — network, data and application — because end users interact with these layers most often. Malicious actors can infiltrate an insecure network. But irresponsible use doesn’t require bad intent. A well-secured organization can still fail at the data layer through hallucinations that seem harmless until they aren’t.

Consider a concrete example: A patient rates their pain a 10 on a 10-point scale. An LLM might interpret that rating as “severe pain,” and insert that phrase into a clinical summary, but the physician never used the phrase himself. That single interpretive leap, multiplied across thousands of cases, can create documentation that misrepresents clinical reality, exposes organizations to legal risk, and erodes the trust of the clinicians the system was built to support.

Responsible use requires more than secure infrastructure. In particular, three principles must undergird any AI initiative:

Bias mitigation. The history of medicine offers hard lessons about what happens when treatments and protocols embed the biases of their era. AI systems trained on that same historical data can perpetuate those patterns at scale. Healthcare organizations deploying agentic AI must actively audit for bias across age, race, gender and other factors — not as a compliance exercise, but as a clinical imperative.

Observability. Every person whose work is touched by an AI agent needs visibility into what that agent is designed to do and how it’s performing. Without it, there’s no way to confirm the system is operating within its guardrails — and no way to catch it when it isn’t.

Explainability: The ability to articulate what an AI agent does, and why it does it, shouldn’t be optional. Third-party audits are a necessary safeguard. The capacity to explain a system’s purpose to stakeholders outside the immediate team ensures both internal and external accountability.

Responsible governance in this space — for now, at least — requires more than holding a single membership or certification.

Begin with a multi-pronged approach. Industry frameworks like the National Institute of Standards and Technology’s (NIST) Risk Management Framework and the Open Worldwide Application Security Project (OWASP) offer actionable insights for managing AI risk and security. HIPAA compliance remains non-negotiable for any healthcare AI deployment. Organizations like Coalition for Health AI (whose Agentic AI work group I belong to) are working toward codifying agentic AI principles into industry-wide best practices.

Frameworks alone aren't enough. The organizations already getting this right are integrating these standards into their own internal governance frameworks — contextualizing them to their specific workflows, patient populations and risk profiles — rather than treating compliance as a box to check. The goal is to build the kind of trust that makes AI a durable asset — not a liability.

Deliberate Adoption Beats Moving Quickly

Healthcare organizations can’t afford to ignore agentic AI, but they also can’t afford to deploy it without the infrastructure to use it responsibly. The gap between these two failure modes — overanalyzing to the point of paralysis, and underanalyzing to the point of recklessness — is where the real strategic work lives.

Agentic AI represents a bridge between predictive, generative, and autonomous systems. Building that bridge to last requires keeping a human meaningfully in the loop — the mechanism by which guardrails are set, monitored, and enforced over time.

Health systems struggle less with model accuracy than deciding who is responsible when the model is wrong. The most successful adopters build the kind of clinical and organizational trust that makes agentic AI transformational, both for their teams and for their patients.

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