Healthcare organizations are facing immense challenges. Growing patient demand, staffing shortages, shrinking profit margins, and increasingly complicated regulations are prompting these entities to reconsider their care delivery methods and operational scalability.

In parallel, the field of artificial intelligence is experiencing a significant transformation. What started as initial tests with generative AI is now advancing towards agentic AI, which is capable of executing complex, multi-step processes, integrating with various systems, and achieving quantifiable results within regulated settings.

For healthcare institutions, the focus has shifted from using disparate tools to fundamentally restructuring their operations around AI-driven and agent-enabled frameworks.

Pioneering healthcare providers are transitioning from experimental phases to full-scale implementation, moving beyond minor automation efforts to embrace comprehensive organizational change.

PwC explores Agentic AI in healthcare using AWS and Anthropic.

A sunlit modern hospital ward: a sleek autonomous AI medical assistant on wheels, its robotic arms handing a vial of medicine to a nurse while a doctor reviews dynamic health charts on a tablet. Natural morning light floods the room through large windows. Behind them, bold decals of the AWS logo, the Anthropic logo, and the PwC logo grace a glass wall, symbolizing their collaboration in agentic AI healthcare.

Agentic AI for healthcare providers with AWS and Anthropic - PwC

- Healthcare providers face increasing challenges like workforce shortages and regulatory complexities.  
- AI is evolving from experimentation to capable agentic systems that enhance operational workflows.  
- Organizations are transitioning from pilot programs to comprehensive AI-native operating models.  

Healthcare providers are under significant pressure. Rising demand, workforce shortages, margin compression, and increasing regulatory complexity are forcing organizations to rethink how care is delivered and how operations can scale.

At the same time, AI has entered a new phase. What began as experimentation with generative AI is rapidly evolving into agentic AI, which can execute multi-step workflows, operate across systems, and deliver measurable outcomes within regulated environments.

For healthcare providers, the opportunity is no longer about isolated tools; i it’s about rearchitecting the enterprise around AI-native, agent-enabled operating models.

Leading healthcare providers are beginning to make the shift from pilots to production, and from incremental automation to holistic transformation.

Agentic AI in healthcare with AWS and Anthropic: PwC

Iowa recently introduced legislation mandating that patients must give consent before artificial intelligence (AI) can be utilized to record and transcribe clinical interactions. In a separate development, Utah has passed a law asserting that AI does not fall under the category of innovation or technology enhancements within the medical practice environment.

The following outlines the newly implemented measures for 2026, organized by relevant areas of concern.

**Restricting AI in Medical Authorizations**

These recent legislative measures respond to growing apprehension about the deployment of AI systems by healthcare insurance providers, which can automatically grant or deny approval for medical treatments.

Most of the legislation in this area requires that a human medical professional be involved in any decision-making regarding authorizations. Additionally, decisions must reflect the nuances of the patient’s medical history and individual circumstances, rather than relying exclusively on aggregated data.

**Alabama**

SB 63 pertains to the role of AI in healthcare insurance authorizations. This law establishes regulations around the use of AI by insurers for prior authorization decisions. It mandates that insurers must be transparent about their use of AI and that any AI-generated decisions must be informed by the specific medical history and clinical conditions of the patient. Furthermore, any denial of coverage must involve a licensed physician or healthcare professional. This legislation, sponsored by Orr, was enacted on April 17, 2026, and is set to take effect on October 1, 2026.

**Colorado**

The bill HB 1139 addresses the regulations surrounding AI use in healthcare authorization choices. It requires insurers to ensure that medical authorization decisions account for a patient’s unique medical history and clinical circumstances, avoiding reliance on general data alone. In the event of a coverage denial, a licensed clinician or physician is obligated to review the case. Sponsored by Joseph and Lieder, this legislation was enacted on June 2, 2026, and will be effective starting January 1, 2027.

**Georgia**

Georgia’s SB 444 establishes a prohibition against making insurance coverage decisions based exclusively on AI systems or software. The bill was sponsored by Kirkpatrick and others, and it was enacted on May 5, 2026, with an effective date set for January 1, 2027.

**Illinois**

The legislation HB 3114 in Illinois restricts healthcare payers from adopting any policy or applying any algorithm or automated process that fails to consider the complete information provided by billing healthcare professionals when downcoding claims. The bill allows the use of automated systems to flag potential downcoding opportunities; however, any final determinations must be carried out or reviewed by a human. This bill, sponsored by Koehler and others, is currently awaiting the governor’s approval.

**Iowa**

Iowa's HF 2635 focuses on standards of conduct for health insurance. It permits insurers to leverage AI for the initial review of prior authorization requests, but decisions to deny, delay, or downgrade such requests cannot solely rely on AI. This law was enacted on May 13, 2026.

**Utah**

Utah's SB 319 updates preauthorization requirements for health insurance to include stipulations on AI usage. Insurers are required to publish preauthorization details on their websites and disclose AI's role in authorization reviews. This bill mandates that individuals evaluating adverse preauthorization decisions apply independent medical judgment, rather than depending solely on external recommendations. Additionally, it sets forth time limits for authorization determinations, outlines minimum durations for chronic or long-term care authorizations, and requires insurers to provide information on adverse rulings. Sponsored by Johnson and Hall, the law was enacted on March 19, 2026, and will take effect on January 1, 2027.

**Washington**

Finally, Washington’s SB 5395 enhances existing regulations to ensure transparency and accountability concerning the use of AI in prior authorization processes. The new law clarifies that only licensed physicians or health professionals may deny prior authorization requests based on medical necessity. AI is not permitted to serve as the sole basis for denying, delaying, or altering healthcare services. Sponsored by Orwall, Muzzall, and others, this law was enacted on March 23, 2026, and is effective as of June 11, 2026.

States enacted 14 new healthcare AI regulations this year — Transparency Coalition. Ensuring Transparency in AI Now.

A sunlit legislative chamber with a polished wood desk holding a neat stack of 14 legal briefs stamped “PASSED,” a wooden gavel beside a tablet displaying a stylized digital brain icon over a red medical cross, a single U.S. flag standing in the corner, and a large fabric banner overhead reading “Transparency in AI Now – Transparency Coalition,” warm natural light streaming through tall arched windows.

States have passed 14 new laws this year regulating the use of AI in healthcare - Transparency Coalition

- Iowa requires patient consent for AI use in clinical interactions and transcriptions.  
- New laws mandate human oversight in AI-related healthcare authorization decisions across states.  
- Utah specifies AI does not qualify as a technology upgrade in medical practices.

Iowa enacted a law requiring patient consent when using AI to record and transcribe clinical interactions, while Utah enacted a law that specified that AI does not qualify as an innovation or technology upgrade within a medical clinic’s scope of practice.

We have all the new 2026 measures below, categorized by area of concern.

limiting ai use in medical authorizations

These new laws address a widespread concern over the use of AI models by healthcare insurance companies to automatically approve or deny medical procedures.

Most versions of the laws adopted in this space require a human medical professional in the authorization decision loop. They also typically require a decision to be based on the patient’s medical history and circumstances, and not solely on group data.

Alabama

SB 63: Use of AI in healthcare insurance authorizations

This Act regulates the use of AI by healthcare insurers to make prior authorization determination. Insurers must disclose AI use, and AI-determined decisions must be based on the patient’s medical history and unique clinical circumstances. Denials must be made by a licensed physician or healthcare professional. Sponsor: Orr.Enacted April 17, 2026. Effective Oct. 1, 2026.

Colorado

HB 1139: Regulating AI use in healthcare authorization decisions

This Act requires healthcare insurers to base their medical authorization decisions on a patient’s medical history, individual clinical circumstances, and not solely on group data. A denial of coverage must be reviewed by a licensed clinician or physician. Sponsors: Joseph, Lieder. Enacted June 2, 2026. Effective Jan. 1, 2027.

Georgia

SB 444: Use of AI in healthcare insurance decisions

This bill prohibits decisions regarding insurance coverage of healthcare decisions from being based solely on AI systems or software tools. Sponsors: Kirkpatrick, et al. Enacted May 5, 2026. Effective Jan. 1, 2027.

Illinois

SB 3114: Restricting the use of AI in healthcare approvals

This bill prohibits a health care payor from implementing any policy or using any algorithm or other automated process, system, or tool that bypasses the evaluation of all information included by the billing health care professional to downcode a claim. SB 3114 provides that a health care payor may use an automated process to identify claims that may justify a downcoding determination, but all downcoding determinations must be made or reviewed by a natural person. Sponsors: Koehler, et al. Awaiting governor’s signature.

Iowa

HF 2635: Use of AI in healthcare authorization decisions

This Act concerns health insurance standards of conduct. Under the Act, insurers may use AI to provide initial review of a prior authorization request, but may not base their decision to deny, delay, or downgrade the request solely on AI. Enacted May 13, 2026.

Utah

SB 319: Regulating AI use of in healthcare insurance authorizations

This Act updates requirements for health insurance preauthorization decisions to include restrictions around the use of AI. Insurers must post preauthorization information on their website, and disclose the use of AI in the authorization review process. SB 319 also requires an individual reviewing an adverse preauthorization determination to use independent medical judgment and not rely solely on recommendations from any other source. The Act includes a time limit to make an authorization determination; provides minimum periods that an authorization must be valid for chronic or long-term care conditions; and requires an insurer to provide information regarding an adverse determination. Sponsors: Johnson, Hall. Enacted March 19, 2026. Effective Jan. 1, 2027.

Washington

SB 5395: AI use in healthcare prior authorization decisions

This Act expands on existing law to require transparency and accountability with the use of AI in the prior authorization determination process. The new law specifies that only a licensed physician or a licensed health professional may deny a prior authorization request based on medical necessity. Artificial intelligence shall not be the sole means used to deny, delay, or modify health care services. Sponsors: Orwall, Muzzall, et al. Enacted March 23, 2026. Effective June 11, 2026.

States have passed 14 new laws this year regulating the use of AI in healthcare — Transparency Coalition. Legislation for Transparency in AI Now.

Increasingly, patients are arriving at their medical appointments after discussing their symptoms with AI chatbots, often placing as much trust in these digital assistants as they do in their healthcare providers.

However, the issue arises when a chatbot offers incorrect guidance and a patient suffers as a result. The question of accountability remains murky. Meghan O’Connor, a health law partner at Quarles & Brady and co-chair of the firm’s AI team, highlights that this critical issue in healthcare law is still evolving.

"This represents one of the most pressing unresolved questions in the realm of healthcare law today. The distribution of liability will depend heavily on the specific circumstances and is likely to be clarified through years of legal proceedings," she explained.

O’Connor identifies three key stakeholders in the liability discussion: the AI developer, the patient, and the healthcare provider. 

AI companies typically include disclaimers stating that their outputs shouldn't be considered medical advice, but these disclaimers may not provide much protection if the chatbot is promoted in a way that leads patients to perceive its responses as diagnostic recommendations.

When it comes to patients, O’Connor believes that courts are unlikely to assign significant blame to individuals who trust a resource that appears to offer authoritative information.

Providers, however, face a more precarious situation. Once a patient mentions they've acted on misleading AI advice, a provider's failure to rectify that misinformation could lead to malpractice claims. O’Connor emphasizes that determining the standard of care involves assessing what a reasonable provider would have done when faced with identical information.

"My practical advice for healthcare providers is clear: correct any misinformation, document the conversation thoroughly, and handle AI-derived data the same way you would any other patient-reported information that contradicts your clinical judgment—by addressing it directly and noting your actions," she advised.

When a patient refers to AI-generated information during their visit, it becomes part of their medical history, just like if they had shared insights from an online search or advice from a friend, O’Connor points out.

She warns against inaction.

"Consider this analogy: if a patient mentions seeing on an online forum that drinking pickle juice can cure glaucoma, no responsible ophthalmologist would remain silent. Misinformation from AI should be treated with the same level of seriousness. The origin of the erroneous information does not diminish a provider’s obligation to their patient once it's been introduced in a healthcare context," O’Connor asserted.

Though legal frameworks have yet to catch up with the advent of AI chatbots, O’Connor’s concluding advice for providers is to engage proactively rather than ignore these discussions. Investing a little time now could significantly impact patient outcomes in the future.

What occurs when patients rely on AI instead of their physician?

A bright, sunlit doctor’s office: a middle-aged patient sits at a wooden desk holding a tablet that displays a glowing AI chat interface, turning away from a concerned physician across the table. Medical charts, a stethoscope, and gentle rays of natural light streaming through the window set a warm yet tense atmosphere.

What Happens When Patients Trust AI Over Their Doctor? - MedCity News

- Patients increasingly trust AI chatbots, complicating liability issues in healthcare scenarios.  
- Responsibility for errors likely depends on the AI developer, patient, and provider interactions.  
- Providers must engage with AI-sourced misinformation to avoid potential malpractice claims.

Patients are increasingly walking into exam rooms having already consulted an AI chatbot about their symptoms — and sometimes trusting it as much as they trust their doctor.

But when a chatbot gets it wrong and a patient gets hurt, who bears the responsibility? According to Meghan O’Connor, health law partner at Quarles & Brady and co-chair of the firm’s AI team, the answer is still being written.

“This is one of the most significant open questions in healthcare law right now, and the honest answer is that liability allocation is going to be highly fact-dependent and will likely take years of litigation to clarify,” she remarked.

O’Connor broke the liability question into three layers: the AI developer, the patient and the provider.

AI companies often lean on disclaimers stating their outputs aren’t medical advice, but those disclaimers might not hold up if a chatbot was marketed in a way that encouraged patients to treat its answers as diagnostic guidance, she explained.

As for patients, O’Connor says courts are unlikely to pin much blame on them for trusting a tool that presents itself as authoritative.

Providers have to walk a finer line, though. Once a patient discloses they’ve been relying on inaccurate AI advice, the provider’s failure to correct it could become a malpractice issue. O’Connor said this standard-of-care issue boils down to what a reasonable provider would have done with that same information.

“My practical recommendation for providers: correct misinformation, document the conversation and treat AI-sourced information the same way you’d treat any patient-reported information that conflicts with clinical judgment — address it directly and note that you did,” she stated.

Once a patient starts discussing AI-generated information during a clinical encounter, that information becomes part of the clinical picture the same way as a patient reporting web-browsing results or advice from their friend, O’Connor noted.

She said silence is rarely the safer option.

“Think of it this way: if a patient told you they read on an internet forum that drinking pickle juice would cure their glaucoma, no reasonable ophthalmologist would say nothing. AI-sourced misinformation should be treated the same way. The source of the bad information does not change the provider’s reasonable duty to the patient once it’s been raised in the context of care,” O’Connor declared.

The law hasn’t caught up to AI chatbots yet, but her bottom-line advice for providers is to engage, not ignore. It’s a small investment of time that she said could matter a great deal down the line.

Photo: Witthaya Prasongsin, Getty Images

What Happens When Patients Trust AI Over Their Doctor?

In an effort to guide the integration of artificial intelligence (AI) into healthcare, a physician at UVA Health and a colleague from Clemson University have developed a framework designed to prioritize patient care above all. R. Andrew Taylor, who serves as the vice chair of research and innovation in Emergency Medicine at the University of Virginia School of Medicine, highlighted the unprecedented pace at which AI is being implemented in medical settings. However, he pointed out that hospitals have been lacking a comprehensive approach to evaluate these technologies effectively.

“Many traditional methods focus on cost because it’s straightforward to measure. We created this framework to provide healthcare organizations with a structured method for evaluating the impact of an AI tool on patients, healthcare staff, and the overall quality of care,” Taylor stated.

The approach by Taylor and Arwen B.L. Declan, a clinical assistant professor at Clemson's School of Health Research, is designed to ensure that healthcare stays firmly rooted in ethical standards that prioritize serving patients and supporting local communities. Their framework, named Total Mission Value, comes at a time when hospitals are under increasing pressure to quickly adopt AI technologies without guidance on assessing their value beyond financial considerations.

At the core of this framework is the patient experience, supported by ethical principles and the goal of economic sustainability. The emphasis is clear: AI should enhance the work of care providers rather than replace them or add to their burdens.

The researchers acknowledge the dual-edged nature of AI technologies. While these tools can potentially enhance the speed of care, improve diagnostic accuracy, and contribute to operational efficiency and population health initiatives, they also carry the risk of bias, lack of transparency, job displacement, and could damage the patient-provider relationship — all concerns that are often overlooked when focusing solely on cost.

Declan and Taylor stress that hospitals have not had a well-defined method to evaluate these multifaceted choices. Their proposed framework incorporates five ethically grounded priorities: patient care, staff experience, hospital operations, economic impact, and education and research.

Declan points out the abundance of AI tools being marketed to healthcare facilities and the pressing need to determine which ones will genuinely improve care outcomes. “That necessitates assessing the effects of an AI tool across clinical, operational, and financial perspectives, while ensuring that patient care remains the focal point,” he explained.

Ultimately, the framework emphasizes that patient care should be the primary mission of any healthcare institution. “We believe that by keeping this mission at the forefront, the adoption of effective AI technologies can be accelerated, fostering trust between patients and clinicians,” Taylor noted. “When technology is aligned with the goal of enhancing care, improved efficiency and cost savings are likely to follow.”

UVA Health researcher outlines framework for integrating AI in healthcare.

A sunlit UVA Health research lab: a scientist in a white coat studies a transparent touchscreen displaying a glowing blueprint of healthcare AI—interlinked icons for heart rate, DNA helix, stethoscope, and a hospital silhouette. The UVA Health logo hangs prominently on the wall behind. Warm natural light streams through large windows onto the desk, scattered with papers and a tablet.

UVA Health researcher helps lay out framework for use of AI in healthcare - Augusta Free Press

- UVA and Clemson researchers developed a framework for integrating AI in healthcare effectively.  
- The framework prioritizes patient care while considering ethics, staff, and operational impacts.  
- Hospitals must evaluate AI tools comprehensively, focusing on patient experience over costs.  

This would seem to be basic common sense, but a UVA Health emergency medicine doctor and a colleague at Clemson felt the need to spell out a framework for how hospitals integrate AI into their work, and to stress the importance of making sure AI is used, primarily, to enhance patient care.

Imagine that.

“AI is being adopted in medicine at a scope and velocity we have never seen before, but hospitals haven’t had a good way to weigh these decisions as a whole,” said R. Andrew Taylor, vice chair of research and innovation for the University of Virginia School of Medicine’s Department of Emergency Medicine.

“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,” Taylor said.

Our American healthcare system focuses on cost because it is run like a business, not a public trust, but that’s just because we do everything bassackwards here in the States.

The framework on AI from Taylor and Arwen B.L. Declan, a clinical assistant professor in the School of Health Research at Clemson, is meant to ensure healthcare remains “rooted in its ethical obligations” to serve patients, communities and local workforces, they say in a new paper outlining their creation, which has been published in npj digital medicine.

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 tool’s worth beyond its price.

Declan and Taylor’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.

The researchers are candid that AI’s promise cuts both ways:

“AI tools could enhance care speed, diagnostic accuracy and costs efficiency while supporting population health, scientific inquiry and operational management,” they note in their paper. “However, they also introduce risks of bias, opacity, workforce displacement and erosion of the patient-clinician relationship that are invisible to cost-focused analyses.”

Declan and Taylor note that hospitals have lacked a structured way to weigh these choices as a whole — the standard approaches for evaluating a new technology tend to center on cost.

Their framework is built to fill that gap, integrating five “ethically grounded” priorities:

patient care

staff experience

hospital operations

economic impact

education and research

Declan emphasizes that realizing AI’s full potential means resisting the urge to evaluate it narrowly.

“Hospitals are seeing a huge number of new AI tools marketed to improve healthcare. The challenge is to figure out which ones actually will,” Declan said. “That requires weighing an AI tool’s impact across clinical, operational and financial dimensions, while keeping patient care at the center of every decision.”

Ultimately, it is vital that hospitals remember that patient care is their “central, defining mission,” Declan and Taylor write.

“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,” Taylor said. “Technology should help us take better care of people. If we keep that as the goal, the efficiency and the savings tend to follow.”

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UVA Health researcher helps lay out framework for use of AI in healthcare

Telefónica Tech, the technology division of Telefónica, has collaborated with the global healthcare technology company Harrison.ai to introduce an AI-driven diagnostic tool aimed at enhancing regional healthcare services throughout Spain. 

This innovative clinical tool assists radiologists by examining chest X-rays, representing a significant move toward integrating AI-assisted screening into existing clinical systems, as opposed to relying solely on independent diagnostic software.

Facilitating early detection of clinical conditions 

The AI solution is capable of recognizing as many as 124 clinical conditions from just one chest X-ray, including pulmonary nodules, which play a vital role in lung cancer screening initiatives. 

Early detection of these findings allows healthcare professionals to identify concerning conditions at an earlier stage, potentially preventing further complications. The tool is intended to complement radiological expertise, rather than replace the essential clinical judgment of radiologists.

Telefónica Tech and Harrison.ai Collaborate on AI X-Ray Technology

A bright, modern radiology suite flooded with warm natural light. At center, a sleek AI-powered X-ray machine bears the Telefónica Tech logo on one panel and the Harrison.ai logo on the other. A doctor in a crisp white coat studies a high-resolution 3D bone scan on a nearby touchscreen display. Clean, futuristic medical setting showing innovation and collaboration.

Telefónica Tech & Harrison.ai Partner on AI X-Ray Tool - Healthcare Digital

- Telefónica Tech partners with Harrison.ai to enhance healthcare with AI diagnostics in Spain.  
- AI tool analyzes chest X-rays, identifying 124 clinical findings for early detection.  
- Designed to support, not replace, radiologists' judgements in clinical screening processes.  

Telefónica Tech, the business unit of Telefónica, has partnered with global healthcare technology firm Harrison.ai to bring an AI-powered diagnostics tool to regional health services across Spain.

The clinical tool is designed to support radiologists by analysing chest X-rays, with the partnership signalling a shift toward AI-assisted screening within existing clinical infrastructure rather than standalone diagnostic software.

Identifying clinical findings early

The AI system can automatically identify up to 124 clinical findings from a single chest X-ray, including pulmonary nodules, a finding central to lung cancer screening programmes.

Earlier identification of such findings could help medical practitioners flag critical conditions before they progress further.

The tool is positioned as a support mechanism rather than a replacement for radiological judgement.

Telefónica Tech & Harrison.ai Partner on AI X-Ray Tool

As investors navigate markets influenced by geopolitical events, energy prices, and changing inflation indicators, many are shifting their focus toward long-term structural themes rather than being swayed by fleeting headlines. One area that stands out in this context is the application of artificial intelligence in healthcare, which promises enhancements in diagnostics, treatment strategies, and overall productivity in systems that are increasingly under financial strain. To assist in identifying companies that align with this transformative trend, the Transformative AI Healthcare Stocks screener offers a valuable resource. Below, we spotlight three companies from this screener that exemplify this theme and deserve further examination.

Pfizer is a significant player in the global biopharmaceutical realm, involved in discovering, developing, producing, and selling medications and vaccines across a variety of sectors, including internal medicine, oncology, and rare diseases.

Generating approximately $61.9 billion annually, Pfizer operates predominantly through its Global Biopharmaceuticals Business, with $37.4 billion of revenue stemming from the United States, $9.4 billion from emerging markets, and $16.4 billion from other developed territories. The company boasts a market capitalization of around $142.5 billion.

Pfizer’s involvement with AI in healthcare extends to drug discovery, clinical development, and biomolecular modeling. This company is supported by a diverse portfolio in vaccines, oncology, rare diseases, and internal medicine. Despite appearing affordable in comparison to some competitors and offering a notable dividend yield, potential income investors should be mindful that the dividends are not fully backed by earnings or free cash flow. Therefore, the sustainability of these payouts warrants consideration. Furthermore, Pfizer's late-stage pipeline shows promise in areas like oncology, hemophilia, and obesity treatments, alongside partnerships that harness AI for research and development, all while facing ongoing regulatory and patent challenges. Thus, the interplay between Pfizer’s current valuation and its long-term outlook encompasses many variables rather than a straightforward narrative.

Next, we turn to Medtronic, a renowned global medical technology firm that manufactures a range of devices, including heart pacemakers, defibrillators, surgical tools, and diabetes care products for chronic health conditions.

Medtronic amasses around $36.4 billion in annual revenue, with its Cardiovascular segment leading the way at approximately $14 billion, followed by Neuroscience at $10.3 billion, Medical Surgical at $8.8 billion, and Other contributions totalling $3.2 billion. The company's market cap stands at $105 billion.

Through its extensive lineup of cardiovascular and neuromodulation devices, Medtronic gives investors access to AI-driven surgical and monitoring technologies. In particular, it is advancing into AI-guided surgical video and robotics through its Hugo system, while also pursuing growth in less saturated international markets and integrating recent acquisitions, including SPR Therapeutics. However, challenges like margin pressures, underwhelming performance in diabetes and surgical segments, and a complicated spin-off within its diabetes division create risks. Investors weighing Medtronic's AI initiatives, restructuring efforts, and this risk profile against its current valuation and projected analyst expectations will find a broader narrative that includes more than just headline financial figures.

Lastly, Hinge Health, based in San Francisco, focuses on providing digital musculoskeletal care, targeting chronic pain and post-surgery rehabilitation through a hybrid platform that incorporates AI-powered motion tracking and wearables.

Hinge Health generates around $646.3 million in revenue from its healthcare software, emphasizing its push towards a digital musculoskeletal care model rather than conventional product sales. The company has a market capitalization of about $5.9 billion.

Positioned at the crossroads of AI, software, and healthcare, Hinge Health employs motion tracking and automation tools alongside its Enso wearable to deliver musculoskeletal and migraine treatment solutions that can be tracked for effectiveness and cost savings by employers and health plans. Analysts expect future growth in revenue and earnings, and recent positive guidance coupled with index admissions has amplified interest in the stock, despite its premium pricing and history of losses. For prospective investors, the critical question revolves around how the evolution towards digital, outcomes-focused musculoskeletal care and Hinge Health's expansion efforts will impact its growth trajectory and risk framework moving forward.

These three companies are just the beginning; the screener has revealed an additional 36 companies with compelling narratives in AI healthcare that might reshape your perspective on this evolving theme. Utilize Simply Wall St to identify and analyze key drivers, risk factors, and business models that align with your views on AI diagnostics, personalized medicine, remote care, and research, allowing you to target the most promising investment opportunities.

If any of these firms have piqued your interest, sign up for free on Simply Wall St. You can monitor share prices in relation to fair value and stay updated on significant developments. Once you've made your investment decisions, manage your portfolio effectively with our tools that prioritize critical, actionable updates. Throughout your investment journey, our platform helps filter top ideas from various investor perspectives, enabling you to spot hidden catalysts and risks early, thus enhancing your decision-making and helping you stay ahead of the market.

In a rapidly changing market environment, it's essential to seek alternatives that maintain robust income streams, even amid market noise. Consider exploring a curated selection of resilient dividend-paying stocks, scanning for undervalued businesses with solid fundamentals, and identifying potential AI drivers in the hardware and infrastructure sectors.

This discussion provided by Simply Wall St is designed for informational purposes only, relying on historical data and analyst predictions without offering personalized financial advice. It does not endorse the purchase or sale of any stock, nor does it account for individual financial situations or goals. Our analysis emphasizes long-term performance rooted in fundamental data, and may not reflect the most current developments affecting the companies discussed. Simply Wall St does not hold positions in any of the mentioned stocks.

For a thorough valuation analysis of companies like Hinge Health, our tools can help determine if they are undervalued or overvalued, presenting insights on fair value estimates, risks, dividends, insider trading, and overall financial health. Access free analysis to make informed investment decisions.

We welcome feedback on our content and encourage readers to reach out with any concerns or suggestions directly.

Pfizer Shares Headline 3 AI Healthcare Companies to Watch More Closely

Sunlit modern trading desk in a bright office, a widescreen monitor displays the Pfizer logo prominently next to a rising green stock chart, flanked by minimalist AI healthcare symbols (a circuit-patterned heart, data node icons), soft warm daylight streams through floor-to-ceiling windows, papers and a coffee cup rest casually on the desk.

Pfizer Stock Leads 3 AI Healthcare Names Worth A Closer Look - simplywall.st

- Investors focus on long-term structural changes like AI in healthcare amid geopolitical tensions.
- Pfizer, Medtronic, and Hinge Health highlight AI's transformative potential in medical diagnostics.
- Each company faces unique challenges, balancing innovation with cash flow and market pressures.

With markets fixated on geopolitics, energy prices, and shifting inflation signals, many investors are looking for themes tied to long term structural change rather than short term headlines. Artificial intelligence in healthcare sits squarely in that camp, with potential to improve diagnostics, treatment decisions, and productivity across systems facing cost pressure. The Transformative AI Healthcare Stocks screener helps filter companies exposed to this trend, while you keep an eye on macro data and central bank moves. In this article, you will see 3 stocks from the screener that bring this theme to life and merit a closer look.

Overview: Pfizer is a large global biopharmaceutical company that discovers, develops, manufactures, and sells prescription medicines and vaccines across internal medicine, oncology, inflammation and immunology, rare diseases, biosimilars, and sterile injectables.

Operations: Pfizer generates about US$61.9b in annual revenue primarily from its Global Biopharmaceuticals Business, with around US$37.4b from the United States, US$9.4b from emerging markets, and US$16.4b from other developed markets.

Market Cap: US$142.5b

Pfizer provides exposure to AI in healthcare in areas such as drug discovery, clinical development, and biomolecular modelling. It is also anchored by a broad portfolio in vaccines, oncology, rare diseases, and internal medicine. The stock appears inexpensive relative to some peers, and it offers a high dividend yield. However, the dividend is not fully covered by earnings or free cash flow, so income-focused investors may wish to weigh the appeal of the payout against the risks to its sustainability. In addition, Pfizer has an active late stage pipeline in oncology, hemophilia, and obesity treatments, as well as AI powered R&D partnerships and ongoing patent and regulatory pressures. As a result, the relationship between Pfizer’s current valuation and its long term potential involves multiple moving parts rather than a simple narrative.

Pfizer’s AI powered pipeline, high dividend yield, and complex cash flow picture make the story feel unfinished, so it is worth lining up the current share price against the .

PFE Discounted Cash Flow as at Jul 2026

Overview: Medtronic is a global medical technology company that makes devices such as heart pacemakers and defibrillators, spinal and brain implants, surgical tools, and diabetes care products used by hospitals and specialists to treat chronic conditions.

Operations: Medtronic generates about US$36.4b in annual revenue, led by Cardiovascular at roughly US$14.0b, Neuroscience at US$10.3b, Medical Surgical at US$8.8b, and Other at US$3.2b.

Market Cap: US$105.0b

Medtronic provides exposure to AI powered surgery and monitoring tools layered onto a large base of cardiovascular and neuromodulation devices, backed by nearly US$36.4b in sales and a 3.41% dividend yield. The company is pushing into AI guided surgical video, robotics with its Hugo system, and advanced neuromodulation. It is also expanding in underpenetrated international markets and integrating acquisitions such as SPR Therapeutics. At the same time, margin pressure, underperforming diabetes and surgical units, and a complex Diabetes spin off create execution risk. For investors evaluating how this mix of AI related initiatives, restructuring activity, and risk compares with Medtronic’s current valuation and analyst expectations, the full picture extends beyond headline revenue figures and P/E multiples.

Medtronic’s push into AI guided surgery, robotics, and neuromodulation could be masking a much bigger story in plain sight. Line up the current setup against the and see what might be hiding under the surface.

NYSE:MDT Earnings & Revenue Growth as at Jul 2026

Overview: Hinge Health is a San Francisco based healthcare technology company that delivers musculoskeletal care for issues like chronic pain, acute injury, and post surgery rehab through a digital platform combining AI powered motion tracking, a nerve stimulation wearable, and clinical support. It primarily works with self insured employers and health plans that want to manage musculoskeletal costs and outcomes at scale using a technology enabled model.

Operations: Hinge Health generates its roughly US$646.3m in revenue from healthcare software, reflecting its focus on a digital MSK care platform rather than traditional device sales or provider fees.

Market Cap: US$5.9b

Hinge Health sits at the intersection of AI, software and healthcare, using motion tracking, automation tools like Robin, and its Enso wearable to deliver musculoskeletal and migraine care that employers and health plans can track for cost savings and return on investment. Analysts have highlighted expectations for revenue and earnings growth, and recent guidance raises and index inclusions have drawn more attention to the stock, even as it still prices in a premium and carries a history of losses. For investors, the key consideration is how the shift toward digital, outcomes based MSK care and Hinge Health’s push into new programs and channels could influence its growth and risk profile from here.

Hinge Health’s accelerating shift from devices to a software based MSK platform is only half the story; the real question is whether the growth trajectory in the already bakes in the key risk that could change everything.

NYSE:HNGE Earnings & Revenue Growth as at Jul 2026

The three stocks in this article are only a starting point, as the full surfaced 36 more companies with equally compelling AI in healthcare narratives that could change how you think about this theme. Use Simply Wall St to identify and analyze the specific catalysts, risk profiles, and business models that fit your view on AI diagnostics, personalized medicine, remote care, and research so you can focus on the highest conviction opportunities.

Take Control of Your Investment Journey

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Seeking Fresh Alternatives Before Momentum Flies

Markets can move quickly and the best setups rarely wait. New ideas gain momentum, attract broader attention, and pricing can shift. Scan fresh stock pockets while it matters and consider acting promptly when you have completed your research.

Target income streams that could hold up even when headlines feel noisy by scanning a curated group of resilient payers with the .

Look for under the radar businesses with strong balance sheets and fundamentals using a carefully filtered before others reassess their valuations.

Identify potential AI enablers involved in the hardware and infrastructure side of the trend with a focused shortlist of .

This article by Simply Wall St is general in nature. We provide commentary based on historical data and analyst forecasts only using an unbiased methodology and our articles are not intended to be financial advice. It does not constitute a recommendation to buy or sell any stock, and does not take account of your objectives, or your financial situation. We aim to bring you long-term focused analysis driven by fundamental data. Note that our analysis may not factor in the latest price-sensitive company announcements or qualitative material. Simply Wall St has no position in any stocks mentioned.

Valuation is complex, but we're here to simplify it. Discover if Hinge Health might be undervalued or overvalued with our detailed analysis, featuring fair value estimates, potential risks, dividends, insider trades, and its financial condition. Access Free Analysis

Have feedback on this article? Concerned about the content? Get in touch with us directly. Alternatively, email editorial-team@simplywallst.com

Pfizer Stock Leads 3 AI Healthcare Names Worth A Closer Look

JLL has integrated artificial intelligence into its Advanced Technology Group Intelligence platform, part of its Accreditation Lifecycle Program aimed at ensuring healthcare facilities adhere to compliance regulations, the company revealed on Wednesday. This initiative is specifically targeted at helping healthcare organizations maintain their accreditation standards efficiently.

The introduction of AI allows managers to extract relevant data from various reports, identify incomplete sections or missing documents, and manage their findings through an organized compliance dashboard, according to JLL.

Carey Sealy, Managing Director of ATG Enterprise Services at JLL's Work Dynamics healthcare division, noted, “Healthcare systems often find themselves in a cycle of frantic, reactive preparation.” He emphasized that these new AI tools act as an early warning mechanism for their clients.

Healthcare institutions are facing significant compliance hurdles, particularly as they navigate aging infrastructure and limited resources, according to prevailing reports.

Last year, the Joint Commission, which oversees accreditation for most healthcare facilities, introduced updates to its standards known as Accreditation 360. These changes are aimed at simplifying rules relating to the management of a facility’s physical environment, among other key areas.

While the new accreditation standards are anticipated to alleviate compliance issues in the long run, organizations must first adapt to the learning curve presented, as highlighted in a recent blog by J.J. Keller Consulting Services.

When JLL first launched its Accreditation Lifecycle Program in April, it was designed to reflect the accreditation cycle mandated by the Joint Commission, which requires regular self-assessments by healthcare organizations. Noncompliance can lead to significant operational risks, according to JLL.

The recently added AI features aim to mitigate the likelihood of noncompliance by automating the procedures involved in analyzing inspection reports and associated workflows, thereby reducing the chances of human error—such as typos and overlooked details—that often occur during manual data entry.

The compliance manager's dashboard is equipped with a visual calendar and indicators to show pass or fail statuses, allowing managers to spot trends effectively.

Cheryl Carron, JLL's COO for Work Dynamics in the Americas and President of the Healthcare Division, stated that the ultimate aim is to “transform compliance management from a mere administrative task into a strategic advantage.”

JLL incorporates AI into healthcare facility compliance system

Natural morning light floods a modern hospital corridor. A professional healthcare compliance officer in business attire holds a sleek tablet displaying a digital dashboard overlaid with AI-driven checkmarks and shield icons. The JLL logo is prominently mounted on a plaque behind them, warm and inviting tones highlighting the fusion of innovation and facility assurance.

JLL adds AI to healthcare facility compliance platform - Facilities Dive

- JLL integrates AI capabilities into its healthcare accreditation compliance platform for improved efficiency.  
- New features include data extraction, documentation flagging, and a compliance management dashboard.  
- AI enhancements aim to reduce human error and streamline inspection report analysis processes.  

Listen to the article 3 min This audio is auto-generated. Please let us know if you have feedback

Dive Brief:

JLL has added artificial intelligence capabilities to its Advanced Technology Group Intelligence platform, part of its healthcare compliance-focused Accreditation Lifecycle Program, the company announced Wednesday. The program is designed to keep healthcare facilities in compliance with accreditation standards.

Managers now can use AI to extract data fields from reports, flag empty fields or missing documentation and organize findings in a compliance dashboard, JLL says.

“Healthcare systems are often trapped in reactive, frantic … preparation cycles,” Carey Sealy, managing director of ATG enterprise services at JLL’s Work Dynamics healthcare division, said in a statement. “With these new AI capabilities, we are providing our clients with an early warning system.”

Dive Insight:

Healthcare systems face compliance challenges as managers deal with aging infrastructure and resource constraints, reports show.

The Joint Commission — the nonprofit organization that manages the accreditation process that most healthcare facilities follow — released changes to its standards last year, dubbed Accreditation 360, that streamlined rules for managing the facility’s physical environment, among other things.

Although the accreditation changes are expected to reduce the compliance burden over time, managers face a learning curve that will pose a short-term challenge, J.J. Keller Consulting Services said in a blog post.

When it first rolled out its Accreditation Lifecycle Program in April, JLL said it had designed it to mirror the Joint Commission accreditation cycle, which requires healthcare organizations to conduct self-evaluations on a regular basis. Failing to meet accreditation requirements represents an enterprise risk that can impact the organization’s ability to operate, JLL said at the time

The AI enhancements are intended to reduce the chance of an organization coming up short by, among other things, automating the lifecycle of inspection report analysis and workflows to eliminate the risk of human error — typos and missed details, among other things — that often occur during manual spreadsheet entries, JLL said in its press release.

The compliance manager dashboard, which features a visual calendar view and pass-or-fail indicators to highlight discrepancies between inspection reports, will help managers identify trends, the company says.

The goal is to “turn compliance management from an administrative burden into a competitive advantage,” said Cheryl Carron, JLL Work Dynamics Americas COO and president of the Healthcare Division.

JLL adds AI to healthcare facility compliance platform

The healthcare landscape is undergoing a significant shift due to the advent of artificial intelligence (AI) and various other technologies, which are reshaping the core principles of medical practice that have long rested on bedside diagnosis. While AI's capabilities in image analysis and disease forecasting are invaluable, traditional methods of collecting patient histories—typically carried out by healthcare professionals—play a crucial role in diagnosing as much as 80% of clinical cases.

Looking ahead, the healthcare field must seek a harmonious blend of clinical expertise and AI technology. Historically, bedside diagnosis has formed the bedrock of medical practice. Physicians relied heavily on thorough history taking, detailed physical examinations, keen observational skills, and sound clinical reasoning to diagnose ailments prior to the advent of laboratory tests and imaging.

In today's technologically driven environment, significant advancements such as AI, machine learning, electronic health records, wearable devices, digital biomarkers, and sophisticated imaging equipment are revolutionizing how we identify and address medical conditions. The healthcare AI market is projected to grow to approximately $427.5 billion by 2032, with countries like India and China taking the lead in the Asia Pacific sector thanks to favorable government policies promoting technological innovation.

Nonetheless, the erosion of bedside diagnosis and educational approaches is noteworthy. In the 1960s, medical training involved up to 75% of hands-on teaching occurring at patients' sides, whereas current estimates suggest that physicians now spend an average of only two-and-a-half minutes each day interacting intimately with patients.

While technological advancements have enhanced diagnostic precision across numerous specializations, they have inadvertently led to a decline in the practice of bedside medicine. This shift has affected the fundamental doctor-patient relationship, as physicians increasingly divert their attention from patients to digital displays, lab results, and predictive analytics, often while multitasking on mobile devices.

AI is pushing bedside diagnosis to the background. Studies indicate that patient history is instrumental in diagnosing around 70% to 80% of clinical cases, with physical examinations contributing an additional 10% to 20%. This leaves a minor fraction of diagnoses reliant mostly on laboratory tests and imaging, underscoring the need to champion the art of history taking and examination.

Traditionally, bedside diagnosis has operated at the intersection of professional dedication and human compassion, aimed at treating the ill and providing reassurance to those struggling with psychological challenges. The societal role of doctors is closely linked to their professional integrity, upholding the esteem of the medical field and nurturing trust with patients.

However, technology's rise has shifted this dynamic considerably. Today’s AI systems can quickly analyze vast arrays of medical images, detect subtle issues unnoticed by human practitioners, predict disease development, and pinpoint high-risk patients well before symptoms manifest. While these breakthroughs hold substantial clinical promise, they also risk relegating the careful art of bedside assessment to a lower priority.

The increasing automation in healthcare is diminishing the vital human connections, converting healthcare settings into sterile environments dominated by technology. Patients, as inherently emotional beings, require human warmth, empathy, and understanding—qualities that AI simply cannot replicate. AI cannot soothe anxious patients, navigate family complexities, recognize emotional turmoil, or provide the compassionate touch that is often necessary during challenging discussions. Trust, a cornerstone of the doctor-patient relationship, is not built through technology alone; it necessitates conscious cultivation.

It’s critical for practitioners to avoid excessive reliance on AI to ensure they don't overlook vital clinical signs. Challenges that need tackling include the often distracting nature of prolonged screen use during patient interactions, diminished eye contact, and a decline in active listening and empathetic engagement—all of which can negatively impact patient satisfaction and adherence to treatment.

The bedside is at risk of evolving into just another workstation, raising the likelihood of missed clinical indicators. A thorough bedside examination is key to accurate diagnosis, whereas negligence in this area could lead to delayed assessments or unnecessary tests. Moreover, AI systems are not infallible—they are subject to algorithmic biases, limited general applicability, false positives, and incomplete data, highlighting the necessity for robust clinical judgment alongside technological advancements. 

AI constitutes a significant stride forward in modern medicine, promising enhanced diagnostic accuracy, expedited disease detection, and more personalized patient care options. Yet, its swift incorporation into healthcare has paralleled a subtle decline in the reliance on bedside diagnosis, fueled by a growing dependence on technology, pressures of time, defensive medical practices, and an evolution in educational focus.

The real concern is not the existence of AI but the potential for clinicians to substitute nuanced observation and personal interaction with algorithmic recommendations. Bedside diagnosis remains a fundamental aspect of compassionate, patient-focused caregiving. Those physicians who actively listen, perform thorough examinations, and judiciously integrate AI into their decision-making processes will continue to deliver care that combines scientific rigor with profound humanity.

The impact of AI on bedside diagnosis and potential solutions.

A dimly lit hospital room at night, a concerned doctor stands beside an IV-draped patient in bed, the only light a warm bedside lamp. Behind them, dark red digital tendrils—formed like circuitry—creep out of a dormant tablet, stretching toward the patient’s chest. Medical charts lie scattered on the floor, half-obscured by shadow. The scene conveys an unseen AI presence overtaking the traditional bedside diagnosis.

How AI is eroding bedside diagnosis and what to do about it - The World Economic Forum

- AI and technology are transforming healthcare, diminishing the importance of bedside diagnosis.  
- Patient history contributes to 70-80% of diagnoses, emphasizing the need for clinician involvement.  
- Over-reliance on technology risks compromising doctor-patient relationships and essential clinical skills.  

Artificial intelligence (AI) and other technologies are transforming healthcare in ways that erode its foundations built on bedside diagnosis.

AI for image analysis and disease prediction is invaluable, but patient history, traditionally gathered by clinicians, can help diagnose up to 80% of clinical encounters.

In the future, medicine should combine clinical judgment with the power of AI.

For centuries, bedside diagnosis has been considered the cornerstone of medical practice. Physicians long relied on careful history taking (the gathering of personal and medical information by clinicians), as well as meticulous physical examination, keen observation and clinical reasoning to identify disease before laboratory investigations and imaging studies were available.

Now, healthcare in the 21st century is witnessing a profound transformation. New technologies such as artificial intelligence (AI), machine learning, electronic health records, wearable technologies, digital biomarkers and advanced imaging systems are rapidly changing how diseases are detected and managed.

The healthcare AI market is expected to reach $427.5 billion in size by 2032, with India and China dominating the Asia Pacific region due to strategic government support for technological advancement.

However, the decline of bedside diagnosis and teaching is significant. In the 1960s, up to three-fourths of clinical teaching happened at the patient's side. Now, some studies estimate that the median time spent at the bedside per day is just two-and-a-half minutes.

While technology has undoubtedly improved diagnostic accuracy in many specializations, it has also unintentionally contributed to the gradual decline of bedside medicine, and has led to the erosion of the doctor-patient relationship.

Increasing dependence on technology has shifted physicians' attention from patients to computer screens, laboratory reports, and predictive algorithms, while also requiring them to juggle mobile phones.

AI is relegating bedside diagnosis to a secondary role

Research has consistently demonstrated that patient history alone contributes to the diagnosis in approximately 70% to 80% of clinical encounters, while physical examination adds another 10% to 20%, leaving only a minority of diagnoses dependent primarily on laboratory or imaging investigations.

Bedside diagnosis has traditionally been influenced by maximum exploitation of wisdom and craft, focused on curing the sick, reassuring those struggling with mental health, and treating physical afflictions as much as possible.

That means doctors have had civic responsibilities intertwined with their professional performance, bringing a status and honour to the medical community and strengthening doctor-patient relationships built on faith.

This has undergone a massive change due to technology. Today, AI can analyse millions of medical images within seconds, detect subtle abnormalities invisible to the human eye, predict disease progression and identify high-risk patients through precision diagnosis long before symptoms emerge.

These innovations have enormous clinical value. However, as diagnostic confidence shifts from physician judgment to algorithmic recommendations, bedside examination often becomes secondary.

The incoming tide of technology is phasing out the human connection, and reducing health systems to robotic environments populated by white-coated physicians. However, patients are emotional beings who require the empathy, reassurance, compassion and communication that AI cannot provide.

AI cannot comfort an anxious patient, understand family dynamics, recognize emotional distress and hold a patient’s hand during difficult conversations. The doctor-patient relationship has been built on trust rather than technology, and any change to this requires attention.

Practitioners must not over-rely on AI to avoid missing clinical signs

Pain points that need to be addressed include excessive screen time during consultations and reduced eye contact, and a lack of active listening and empathetic communication that potentially affect patient satisfaction and adherence.

The bedside is gradually being reduced to a workstation where the risk of missing clinical signs is high.

Failure to perform proper bedside examination may delay diagnosis or result in unnecessary investigations. In addition, AI is not free from error and is plagued by algorithmic bias, poor generalizability, false positives, incomplete datasets and the need for strong clinical judgement; it must augment clinical skills, not replace bedside teaching.

It should be acknowledged that AI represents one of the greatest advances in modern medicine, and has improved diagnostic precision, accelerated disease detection, and expanded opportunities for personalized care.

Nevertheless, its rapid integration into healthcare has coincided with a gradual decline in bedside diagnosis, driven by technological dependence, time pressures, defensive medicine and changing educational priorities.

The concern is not that AI exists, but that clinicians may increasingly substitute algorithms for observation, listening, and hands-on examination. Bedside diagnosis remains more than a diagnostic method; it is the foundation of compassionate, patient-centred care.

The physician who carefully listens, observes, examines, and then thoughtfully incorporates AI into clinical decision-making will continue to provide care that is both scientifically rigorous and deeply human.

How AI is eroding bedside diagnosis and what to do about it

Brian Manning has officially taken the helm as CEO of Nabla, the clinical documentation firm powered by artificial intelligence. This leadership change comes as the company seeks to enhance its operations and attract a broader range of healthcare clients, as announced in a recent press release. Manning's new role is effective immediately.

He steps into this position following the tenure of Alex LeBrun, Nabla's co-founder and former CEO, who shifted to lead the Paris-based AI research company AMI Labs earlier this year. Although LeBrun has transitioned from his CEO role, he will remain actively involved with Nabla as executive chairman and chief AI officer.

Before joining Nabla, Manning held significant roles in the health technology landscape, most recently as President and Chief Revenue Officer at Bamboo Health, a company that focuses on care coordination, behavioral health, and monitoring controlled substances. He also served as the Chief Revenue Officer at PatientPing, a care coordination platform acquired by Bamboo in 2021, and has experience working at Zocdoc, a platform that connects patients with doctors.

Manning's appointment aligns with Nabla's ambitions to strengthen its positioning within the increasingly competitive AI scribe market. Currently, the company collaborates with over 190 healthcare organizations, including notable names like CVS Health, M Health Fairview, and UCLA Health. Last year, Nabla successfully secured a $70 million Series C funding round to further its mission.

As artificial intelligence continues to innovate within the healthcare sector, the demand for AI solutions has surged, particularly in addressing workforce shortages and mitigating the administrative load faced by providers. AI scribes, which automate the documentation of patient-provider conversations and generate clinical notes, are becoming increasingly prevalent. Various companies, including major tech players such as Microsoft and Amazon, as well as significant electronic health record providers like Epic and Oracle Health, are exploring opportunities in this space.

Nabla appoints new CEO as AI writing service aims for growth.

A bright, modern boardroom flooded with warm morning sunlight. At center, a poised executive in a sharp suit shakes hands across a polished glass table with a senior colleague. On the table, a tablet screen shows lines of text appearing in real time—an AI scribe at work. Behind them, the Nabla logo is prominently displayed on a textured wall. natural lighting, story-driven composition.

Nabla names new CEO as AI scribe looks to scale - Healthcare Dive

- Brian Manning becomes CEO of Nabla to enhance AI-backed clinical documentation services.  
- He replaces co-founder Alex LeBrun, who remains as executive chairman at Nabla.  
- Nabla aims to expand its customer base amid growing demand for AI in healthcare.  

Listen to the article 2 min This audio is auto-generated. Please let us know if you have feedback

Name: Brian Manning

New title: CEO, Nabla

Previous title: President and chief revenue officer, Bamboo Health

Brian Manning, CEO of Nabla Permission granted by Nabla

Manning is joining the artificial intelligence-backed clinical documentation firm as the company looks to scale and snag more healthcare customers, Nabla said in a press release Tuesday. His appointment is effective immediately, a spokesperson said.

He replaces Alex LeBrun, the AI scribe’s co-founder and former CEO. LeBrun took on the chief executive position at Paris-based frontier AI research firm AMI Labs earlier this year, though he continues to serve at Nabla as executive chairman and chief AI officer.

Manning previously worked at Bamboo Health, a health technology company that assists with care coordination, behavioral health capacity and referrals, and controlled substance monitoring and analytics. He also served as chief revenue officer of care coordination tech firm PatientPing, which was acquired by Bamboo in 2021, and worked at doctor search company Zocdoc, according to his LinkedIn.

He joins as Nabla aims to expand its reach in the competitive AI scribe space. The company works with more than 190 healthcare organizations, including CVS Health, M Health Fairview and UCLA Health, according to the release. Nabla also raised a $70 million Series C round last year.

The appointment comes as AI has become one of the healthcare sector’s most sought-after technologies, promising to alleviate workforce shortages and providers’ hefty burden of administrative tasks.

AI scribes, or tools that typically record conversations with patients and draft clinical notes, have become more common in healthcare. The field includes a number of companies offering documentation and clinical assistants, including technology giants like Microsoft and Amazon and major electronic health record vendors Epic and Oracle Health.

Nabla names new CEO as AI scribe looks to scale

Artificial intelligence (AI) has transcended its status as a futuristic concept and is now actively transforming healthcare practices, clinical decisions, and patient interactions on a global scale. The recent conference titled "AI in Healthcare: Innovation, Risk and Responsibility" showcased the immense potential of AI to enhance patient outcomes, streamline operations, and assist medical professionals in navigating the complexities of modern healthcare. The discourse at the conference also focused on the inherent risks, regulatory challenges, and legal considerations associated with AI from an international standpoint.

Here are the main insights shared during the conference, highlighting focal points for public health entities, private healthcare practitioners, and insurance providers.

### The Effect of AI on Care Providers and Patients

Recent studies indicate that AI systems operating with “humans in the loop” are yielding more favorable patient results. Nonetheless, it is crucial for clinicians to articulate their reasoning if they disagree with an AI-generated recommendation, and established procedures such as peer reviews should be in place to oversee clinical judgments in these cases. Patients must be informed whenever differing opinions arise that may influence their treatment options.

Healthcare providers are encouraged to maintain their personal connections with patients and to apply their clinical judgment alongside AI tools. Clinicians should be educated about AI outputs to effectively guide patients regarding decisions informed by AI. Understanding the workings of the AI “black box” is ideal.

It is recommended that clinicians utilize only those AI tools they are trained on and that have received the appropriate approvals from their healthcare institutions.

Implementing AI solutions in healthcare demands a strategic approach. To yield tangible benefits and improve patient care, healthcare organizations must first assess the specific needs or issues before deploying AI technology.

Providers should continuously assess the clinical impact of AI technologies to verify their effectiveness in real time prior to widespread implementation, thereby mitigating risk to patients.

Integrating AI to automate mundane tasks can free up healthcare professionals for more targeted patient engagement. Additional training is essential to capture the learning potential from these routine activities, such as compiling discharge summaries.

The employment of AI scribes in healthcare settings should be restricted to those that have been vetted and authorized for use. Caution is advised post-training, as clinicians remain accountable for the documentation produced and must ensure its accuracy. Patients should also be informed about the use of AI tools in their medical care and be required to give written consent.

### Safety and Risk Considerations for Healthcare Providers

The establishment of the National Commission into the Regulation of AI in Healthcare by the Medicines and Healthcare products Regulatory Authority represents a significant step toward developing a comprehensive regulatory framework for health-related AI. However, as the pace of technological advancement accelerates, aligning regulations with these changes will require time.

Regulatory measures exist for medical devices, and to minimize the potential for harm and liability, healthcare providers must ensure that the devices in their facilities possess the highest levels of classification approval. Conducting thorough due diligence on suppliers and using the devices strictly for their intended purposes is essential.

Healthcare staff must be appropriately trained in using AI tools, including medical devices, before their integration into practice.

Healthcare organizations should conduct risk mapping regarding the AI and medical device tools employed within their facilities. This process, ideally initiated from the board level, is crucial for developing robust policies, ensuring protective contracts with suppliers, and maintaining adequate indemnity coverage.

Implementing solid governance and risk mapping across the AI tool chain in each specialty setting will facilitate quick identification of incidents, trace their origins, and enable prompt action, thus reducing potential harm and accelerating liability assessment.

### Data Usage and Security Issues

To fully harness the capabilities of AI in healthcare, facilitating access to sensitive patient data for machine learning is essential. However, establishing access rights for data usage poses significant challenges for public health organizations and other providers. Consent from patients must be obtained for each specific instance of data usage, and clinicians must be aware of how AI utilizes this data to accurately inform patients.

Healthcare providers must also navigate hurdles related to data storage and cross-border data use. In the UK, strict regulations govern the processing and usage of health data. Thus, healthcare institutions should ensure that their operational AI tools comply with data protection standards, maintaining patient data securely, preferably on-site or in private cloud environments, as public clouds may be susceptible to security breaches.

Should patient data be shared with a third party, it is vital for healthcare providers to establish contractual restrictions governing data disclosure and usage, alongside implementing practical safeguards such as enhancing interface security, limiting the queries AI tools can address, and fortifying protection against hacking and unauthorized access.

In conclusion, while AI offers tremendous potential in revolutionizing healthcare delivery, it is accompanied by its own unique set of challenges.

For more insights and guidance on managing emerging legal risks associated with AI, please reach out to the author.

AI in healthcare: Advancements, challenges, and accountability

A bright, sunlit hospital corridor: a doctor in a crisp white coat studies a transparent tablet projecting a 3D digital heart entwined with glowing circuit patterns. A sleek robotic arm gently passes a vial of medicine, while a subtle wall mural of balanced scales looms behind them—warm natural light streaming through large windows, blending human care, cutting-edge AI innovation, and the weight of responsibility.

AI in healthcare: Innovation, risk and responsibility - Kennedys Law LLP

- AI is transforming healthcare, enhancing decision-making and patient outcomes, yet poses regulatory challenges.  
- Clinicians must be trained on AI tools and maintain oversight in clinical decision-making processes.  
- Patient consent and data security are critical for the effective use of AI in healthcare.  

Artificial intelligence (AI) is no longer a future concept in healthcare - it is already reshaping clinical practice, decision‑making, and patient experience across the world. Our recent AI in healthcare: innovation, risk and responsibility conference examined the significant potential of AI to improve patient outcomes, enhance efficiency, and support healthcare providers and clinicians in increasingly complex environments. Alongside the potential benefits, the conference explored the associated risks, regulatory considerations and legal implications from a global perspective.

We highlight below the key takeaways from this conference and provide insight into the key focus areas for public healthcare organisations, private healthcare providers, and insurers.

The impact of AI on patients and clinicians

Studies of AI tools with “humans in the loop” are demonstrating that this collaboration is leading to better patient outcomes. However, when clinicians do not agree with an AI decision they need to be clear as to their reasoning and there should be procedures in place such as peer reviews to oversee clinical decision making in these scenarios. Where there is a difference of opinion which impacts treatment decisions, patients should be informed.

Clinicians should continue to “know their patients” and use their clinical expertise and intuition when using AI tools. Clinicians should be trained to understand AI output in order to counsel patients as to clinical decisions made with AI assistance. They should ideally know what is in the AI “black box”.

Clinicians are advised to only use AI tools that they have been trained to use, and which have been pre-approved by the healthcare organisation/provider.

AI implementation in healthcare needs a targeted approach. To drive value and positively impact patient outcomes, healthcare providers should identify the patient need/problem before introducing the AI solution.

The clinical impact of AI tools should be measured in real time by healthcare providers to monitor effectiveness before scaled implementation and to reduce patient risk.

Automation of routine tasks through the use of AI can help maximise opportunities for targeted patient care. Clinicians should receive supplemental training to ensure that the learning opportunities which those tasks provided are not lost, for example, in the production of discharge summaries.

Use of AI scribes within healthcare settings should be limited to those AI scribes that have been approved for use by the healthcare provider and used with caution once training has been given. The clinician remains responsible for the notes created and for checking the note to ensure accuracy. Patients should be made aware of AI tools being used in their treatment or diagnosis and be asked to consent in writing.

Risk and patient safety considerations for public healthcare organisations and private healthcare providers

The National Commission into the Regulation of AI in Healthcare has been established by the Medicines and Healthcare products Regulatory Authority to advise upon the development of a new regulatory framework for AI in healthcare, however this process will take time, and regulation will need to catch up with the fast pace of technological change.

Regulatory requirements are in place for medical devices. To reduce the risk of harm and liability risk, healthcare providers should ensure medical devices in use in their settings have the highest classification approval and that they undertake adequate due diligence of the suppliers, with devices only being used for the purpose for which they are designed.

It is essential to ensure that clinicians and staff are trained in how to use AI tools including medical devices before they are integrated into the healthcare system.

Healthcare providers should undertake risk mapping in respect of all medical devices and AI tools in use in their settings. This mapping process – which ideally should be undertaken from board level down – is necessary to ensure there are policies and codes of practice in place governing their use which are adequate for each speciality, that they are fully protected with regard to contractual indemnities with suppliers, and that they have indemnity cover.

Clear governance and risk mapping throughout the AI tool chain within each healthcare setting by speciality, should help to ensure that if any incidents occur, they can be identified as quickly as possible in real time, traced to source and interrogated, so that harm can be mitigated and liability apportionment fast-tracked.

Use of sensitive patient data and security risks

For AI tools in healthcare to reach their full potential, access to sensitive patient data for machine learning is key. Access rights around data use therefore present a very real challenge for public healthcare organisations and other healthcare providers. Patient consent must be obtained specifically for each element of the use of their data and clinicians need to be informed as to how data is being used by AI in order to communicate this to patients.

Public healthcare organisations and other healthcare providers should also be aware of the challenges to be faced as to the storage of patient data and the use of patient data across borders. There remain restrictions in the UK on processing and using health data. Healthcare providers should ensure that their operational AI tools are compliant with data protection laws and that patient data is kept securely on the premises or in a private cloud where possible; public clouds present a greater security risk.

If patient data is being disclosed to a third party then healthcare providers should have contractual restrictions in place which govern disclosure and use of data, alongside practical measures being adopted such as considering interface security, having guardrails which limit questions that AI tools will answer and ensuring as much as possible that they are protected against hacking and unauthorised access.

In summary, AI provides excellent opportunities in respect of healthcare delivery, but it comes with a corresponding set of challenges.

For further information and guidance as to how Kennedys can assist in this emerging area of legal risk, please contact the author.

AI in healthcare: Innovation, risk and responsibility

A recent investigation reveals that hybrid teams, which consist of human healthcare professionals and artificial intelligence (AI) systems, yield more precise medical diagnoses. This improvement arises primarily from the fact that both entities tend to make distinct yet complementary errors, allowing for corrections between them. These outcomes underscore the promising role of AI in enhancing patient safety and fostering more equitable healthcare solutions.

Evaluating Healthcare AI Systems 

In a comparative study by Nature Medicine, researchers assessed two specialized clinical AI applications—OpenEvidence and UpToDate Expert AI—alongside three general-purpose models: GPT-5.2, Gemini 3.1 Pro, and Claude Opus 4.6. The evaluation also included Google Search AI Overview as a common reference point in real-world scenarios.

The research employed three evaluation methods:

1. MedQA: 500 questions from medical licensing examinations. 
2. HealthBench: 500 clinical scenarios graded against expert-designed rubrics.
3. Real Clinical Queries (RCQ): 100 anonymized inquiries posed by physicians in actual clinical practice, assessed by 12 blinded healthcare professionals for correctness, completeness, safety, and clarity.

This study primarily explored whether these clinically marketed products provide superior medical knowledge and practical utility compared to general-purpose large language models (LLMs).

A crucial aspect of the RCQ evaluation is that it reflects real questions clinicians face, rather than relying solely on exam-style data.

Benchmark Findings

The findings indicated that general-purpose models outperformed specialized clinical tools across all three assessment areas:

- In the MedQA section, Gemini achieved a score of 97.4%, exceeding OpenEvidence's 89.6% and UpToDate's 88.4%.
- For HealthBench, GPT-5.2 scored 88.0, while OpenEvidence and UpToDate lagged at 62.6 and 61.3, respectively.
- In the RCQ evaluation, Gemini garnered an average rating of 3.62 out of 4, followed closely by GPT-5.2 at 3.54 and Claude at 3.52, whereas OpenEvidence and UpToDate scored 3.24 and 3.17, respectively.

Notably, OpenEvidence rejected 19% of real clinical queries compared to only 1–3% for the general-purpose models. The study found no statistically significant discrepancies between the systems regarding harmful content or hallucinations.

Implications of the Findings

Interestingly, having a healthcare-focused interface or tool does not inherently guarantee superior clinical performance. For the tasks examined, model scalability and general reasoning skills appear to be more beneficial than strictly domain-specific tools. This suggests that healthcare organizations should conduct independent benchmarks of clinical AI products prior to procurement rather than assume that specialized tools are inherently more accurate or safe. However, the study did not cover aspects such as citation quality, system latency, costs, or performance in highly specialized medical tasks.

MedAgentBench: Assessing Medical LLM Agents

MedAgentBench serves as an assessment platform to evaluate whether LLM agents can perform foundational tasks within electronic health records (EHRs) rather than merely answering medical inquiries. Comprising 300 clinician-written tasks across 10 categories and utilizing over 700,000 clinical data points, the platform enables models to interact with a simulated EHR via FHIR standards used in major healthcare systems.

The tasks encompass retrieving patient details, reviewing lab results, documenting vital signs, ordering tests, and creating medication lists. Success is determined by whether an AI model can understand clinician instructions, locate correct information, apply necessary clinical logic, and complete requested actions accurately without producing invalid results.

Benchmark Results

In the assessments, Claude 3.5 Sonnet v2 recorded the highest success rate at 69.67%, followed by GPT-4o at 64.0%, DeepSeek-V3 at 62.67%, and Gemini 1.5 Pro at 62.0%.

Significance of Results

While current models can adequately execute numerous structured EHR tasks, including information retrieval, even the most effective model failed about 30% of tasks overall, especially in action-based initiatives. This suggests that, while systems might be useful for supervised pilots or administrative support, they fall short for unsupervised tasks like medication, laboratory orders, or referrals, where errors could significantly impact patient records.

MAST: The Medical AI Superintelligence Test

The Medical AI Superintelligence Test (MAST) is a continuously updated suite of benchmarks by the ARISE AI Research Network designed to assess various aspects of medical AI capabilities, ranging from diagnostic reasoning to patient safety and radiology.

MAST incorporates several benchmarks, aiming to measure whether an AI system possesses balanced clinical capabilities as opposed to excelling in finite tasks. Notably, the composite scoring system penalizes weak performance more heavily than simpler averages.

As of mid-2026, GPT-5.5 leads the public composite rankings with a score of 61.6%, closely followed by Gemini 3.5 Flash at 59.4%, Claude Opus 4.7 at 59.0%, and Gemini 3.1 Pro at 58.7%. The relatively close scores indicate that while top models perform similarly overall, discrepancies exist at individual benchmarks.

Healthcare Applications of AI 

1. Virtual Wards: Patients receive hospital-level care at home while monitored by healthcare professionals, as observed in NHS virtual wards, reducing stress for families and freeing up hospital resources.

2. Assisted Diagnosis & Prescription: AI chatbots help patients with self-diagnosis and support physicians in defining conditions, although reliability can vary. For instance, Ochsner Health partnered with DeepScribe to automate clinical documentation, improving turnaround times and patient satisfaction.

3. Mental Health Tools: AI analytics elevate mental health care, analyzing various metrics for early detection and support. For example, Cogito provides real-time emotional intelligence coaching, and Headspace uses predictive analytics for proactive outreach.

4. Customer Service Chatbots: Chatbots streamline patient inquiries about appointments and medications, reducing healthcare providers' workloads. 

5. Prescription Auditing: AI reviews prescriptions for errors, minimizing adverse drug events.

6. Pregnancy Management: AI monitors maternal and fetal health via wearables, predicting complications early to enhance safety.

7. Personalized Care: AI enables tailored treatment plans based on patient data, optimizing effectiveness and minimizing side effects.

As AI technology continues to evolve within healthcare, it demonstrates potential for improving efficiency, enhancing patient safety, and offering equitable care solutions. While challenges remain regarding privacy, bias, and overarching regulatory frameworks, the future of AI in healthcare looks promising, proposing innovative pathways to refine patient care and clinic operations.

25 Examples of AI Applications in Healthcare

A bright hospital scene bathed in warm natural light: an adult physician in a white coat studies patient vitals on a sleek tablet showing AI analytics, while behind them a robotic surgical arm gently hovers over an operating table and a lab bench displays AI-driven drug discovery charts. Above it all, a large, elegant “25” formed from interconnected medical icons—a stethoscope, DNA strand, heart monitor waveform—floats subtly. Realistic style, avoid harsh blue tones.

25 Healthcare AI Use Cases with Examples - AIMultiple

- Hybrid teams of clinicians and AI systems improve diagnostic accuracy and patient safety.  
- General-purpose AI models outperform specialized clinical tools in medical knowledge evaluations.  
- Healthcare organizations should benchmark AI products instead of assuming specialized tools are superior.  

PwC explores Agentic AI in healthcare using AWS and Anthropic.

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