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

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.

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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.

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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

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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.

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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

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.  

25 Healthcare AI Use Cases with Examples

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

An article recently highlighted AI Appreciation Day, but it also shed light on a noteworthy piece of legislation recently enacted in Rhode Island: the “Use of Artificial Intelligence by Healthcare Providers Notification Act,” which is set to take effect on June 16, 2026.

The primary goal of this law is to ensure that patients are adequately informed about the use of artificial intelligence by healthcare providers. It includes three key definitions: two concerning healthcare entities—“Healthcare providers” and “healthcare facility”—and a third defining AI as “any technology that can simulate human intelligence.” This encompasses a variety of techniques, such as natural language processing, language model training, reinforcement learning from human input, and machine learning systems.

In contrast to the often lengthy AI regulations seen in other states, Rhode Island’s law is succinct. Section 23-106-3 of the Rhode Island General Statutes provides a clear directive:

All healthcare providers and facilities that utilize artificial intelligence to document both in-person and telehealth appointments must inform patients of AI's involvement in this documentation and subsequently verify the accuracy of the AI-generated records after the consultation.

In a concise 43-word summary, the law mandates that healthcare providers and facilities

(1) notify patients about the use of AI in documenting their appointments, whether in-person or virtual; and 

(2) ensure a review is conducted on the AI-generated documentation.

Our Perspective

This straightforward law addresses a generally non-controversial matter: informing patients that AI is employed in their healthcare documentation process. It acknowledges longstanding privacy concerns related to healthcare data, providing patients with clarity on how their information is being handled. This focus on transparency not only benefits patients but also alleviates some documentation burdens from providers, potentially allowing more time for direct patient care. It’s important to note that the law only mandates notification regarding AI's role in documentation and does not extend to other AI applications, such as diagnostic imaging. Furthermore, the law’s simplicity aids compliance, as there are no strict wording requirements for the notifications.

Healthcare facilities and licensed providers in Rhode Island must stay informed and compliant with these emerging AI-related regulations. With over 450 AI-related legislative measures currently under consideration across the United States, it’s likely that other states will take note and possibly introduce comparable laws.

Rhode Island introduces new legislation on AI and healthcare privacy.

A sunlit view of the Rhode Island State House lawn with the state flag waving gently, and in the foreground a physician holding a tablet that projects a glowing digital shield over a patient’s medical chart—symbolizing the new AI and healthcare privacy law.

Rhode Island’s new AI and healthcare privacy law - Data Protection Report

- Rhode Island's new law requires healthcare providers to notify patients using AI for documentation.  
- Providers must review AI-generated documentation post-visit for accuracy before patient interactions.  
- The law focuses specifically on AI used in documentation, excluding other AI applications.  

We recently published an article to commemorate AI Appreciation Day, but readers may also appreciate a law that recently passed In Rhode Island, known as the “Use of Artificial Intelligence by Healthcare Providers Notification Act.” The bill went into effect on June 16, 2026.

The law’s stated purpose is “is to ensure patients are properly notified of the use of artificial intelligence by healthcare providers.” The law contains three definitions, two for healthcare (“Healthcare providers” and “healthcare facility”), and the third definition is for AI: “any technology that can simulate human intelligence including, but not limited to, natural language processing, training language models, reinforcement learning from human feedback and machine learning systems.”

Unlike many other states’ AI laws that can require multiple pages to explain, Section 23-106-3 of Rhode Island General Statutes reads in its entirety:

Any and all healthcare providers and healthcare facilities that employ artificial intelligence (” AI” ) to document in-person or telehealth visits shall notify patients of the use of AI for that sole purpose and review the AI-generated documentation for accuracy after the visit.

In 43 words, Rhode Island requires healthcare providers and facilities to

(1) notify patients of the use of AI to document the in-person or telehealth visit; and

(2) require review of the AI-generated documentation.

Our Take

This very brief law addressed a non-controversial topic (notice to patient that their provider is using AI in healthcare), recognized the long-standing patient privacy concerns surrounding healthcare data by notifying patients of that use (documentation is also known to be a particular pain point for providers so freeing up time that can be spent on patient care is in theory for the benefit of both patient and provider), and also addressed the potential for harm to patients (review to make sure that it’s penicillin and not penny selling that was prescribed). Note that the law does not require that the healthcare providers and facilities notify patients of other AI uses, such as reading X-rays; instead, the notice obligation applies only for the use in documenting the visit. The law should be easy for those subject to it to understand, and offers them flexibility in how they choose to comply with patient notice requirements because Rhode Island did not require any specific wording.

Healthcare facilities and healthcare providers licensed in Rhode Island will need to comply and should keep abreast of the growing AI-specific requirements in healthcare. Other state legislatures may notice the Rhode Island law and introduce similar legislation (there are over 450 AI-related laws pending in the U.S. as of the date of this post).

Rhode Island’s new AI and healthcare privacy law

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.

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