The authors recommend nationwide adoption of ACN and advanced automatic crash notification (AACN), along with a standardized approach to share and interpret crash data among automakers, emergency responders, trauma experts, and federal agencies.

ACN alerts emergency responders after a serious collision. It can provide the vehicle’s location and, in many systems, establish voice communication between an operator and the people inside the vehicle. AACN systems also transmit detailed crash information that helps emergency responders estimate injury severity before arriving at the scene. The paper draws on prior research, including National Highway Traffic Safety Administration modeling that estimated AACN systems could save between 360 and 721 lives each year.

“Seat belts and air bags are standard because they are safety features, not luxury services. Automatic crash notification should be treated the same way,” said lead author Theresa L. Chin, MD, MPH, FACS, a trauma and burn surgeon at UCI Health. “In a serious crash, the system can tell EMS where the crash happened and provide early signs on the severity of crash and potential for significant injury. That can help the right resources start moving sooner, especially in rural communities where help may be farther away.”

Minutes Matter After a Serious Crash

Longer delays between a crash and notification of emergency medical services have been associated with a greater risk of death, particularly in rural areas. ACN can shorten that interval by automatically reporting the crash, its location, the time of the crash, even if occupants are unconscious, disoriented, or otherwise unable to call 911. AACN can add vehicle telemetry (data such as the change in vehicle speed, rollover, airbag deployment, and restraint status) to provide an early estimate of how likely there is to be severe injury.

Information showing that a vehicle rolled over several times may prompt a different response than a low-speed collision. Information on seat belt use and two-way communication with occupants may help responders determine how many people are involved and whether multiple ambulances are needed. The data may support decisions about deploying specialized trauma care such as advanced life support units, air medical transportation, extrication equipment (for example, the “jaws of life”), or direct transport to a trauma center.

Safety Technology Should Not Be Bundled with Convenience Features

Many ACN systems are packaged with paid connected-vehicle services such as remote start, door locking, mobile Wi-Fi, roadside assistance, or concierge features. As a result, drivers may unknowingly lose crash notification when a complimentary trial ends or when they cancel a package of services they don’t otherwise use.

Dr. Chin said she discovered, while working on the project, that ACN in her own family’s vehicle was no longer active because the bundled subscription had not been renewed. In fact, at a conference in a room of trauma professionals, “only one person knew that their vehicle had an active crash notification service,” she added.

The European Union has required inclusion of an emergency notification system known as eCall in all new vehicles since March 2018. The system automatically contacts emergency services following a serious crash and does not require a paid subscription.

How Drivers Can Check Their Vehicle

Drivers can take several steps to determine whether their current vehicle has ACN or AACN:

1. Search the owner’s manual or manufacturer’s app for terms such as “automatic crash notification,” “emergency call,” “connected services,” “911 assist,” “eCall,” or “SOS.”
2. Look for an SOS or emergency button, often located near the rearview mirror or overhead console. The presence of a button is a clue, but drivers should still confirm that the service is activated.
3. Check whether the system requires a paired phone or an active subscription. Some systems use a built-in cellular connection, while others work only when the driver’s phone is connected through Bluetooth.
4. Use the vehicle identification number (VIN) when contacting the manufacturer or dealership to confirm whether ACN or AACN was included in that specific model, model year, and options package.

“Most people assume that the safety systems built into their vehicle will be available when they need them,” Dr. Chin said. “Drivers should not have to discover after a serious crash that an emergency feature was tied to a premium service they stopped paying for.”

ACS Calls for Standard Access and Better Data Sharing

The ACS COT has previously issued a statement supporting:

• Making ACN and AACN standard in all new motor vehicles without additional subscription fees, while improving availability and connectivity in rural areas with limited cellular coverage.
• Developing reliable pathways for crash information to reach 911 centers and emergency response systems, with more consistent technical standards across automakers.
• Making ACN and AACN data available for research by injury prevention scientists and trauma system experts studying crashes, EMS response times, and injury patterns.
• Supporting federal research and collaboration with National Highway Traffic Safety Administration and other organizations to better understand how vehicle data can improve communication among 911 centers, EMS agencies, and hospitals.

The authors of the JACS’ paper also emphasize the need for common definitions across manufacturers. A notification that describes a crash as having a “high risk” of severe injury should have a consistent and clinically useful meaning regardless of whether the vehicle was made by one automaker or another. Standardization could help emergency responders and clinicians use the information more efficiently, particularly in communities with limited personnel, transportation, or hospital resources.

“Injury prevention does not end when a crash occurs; it also means reducing the harm that follows,” said co-author Leah C. Tatebe, MD, FACS, chair of the ACS COT Injury Prevention Program Area and associate professor of surgery at Northwestern University Feinberg School of Medicine. “This information could close a dangerous gap between time of crash and treatment. That protection should be built into every new vehicle, not reserved for people who can afford an ongoing subscription.”

Co-authors are Julie Y. Valenzuela, MD, FACS; Thomas Duncan, DO, FACS; Peter E. Fischer, MD, FACS; Alexandra Briggs, MD, FACS; Alexis Moren, MD, MPH, FACS; David S. Shapiro,MD, MHCM, MEd, CPHQ, FCCM, FACS; Anne Rizzo, MD, FACS, DABS; Haley Etskovitz, DO, MBS; Leah C. Tatebe, MD, FACS; Brendan T. Campbell, MD, MPH, FACS; and Eileen Bulger, MD, FACS.

This study is published as an article in press on the JACS website.

Citation: Chin TL, Valenzuela JY, Duncan T, et al. Advanced Automatic Crash Notification Systems: It Is Time to Adopt Potentially Life-Saving Technology. Journal of the American College of Surgeons, 2026. DOI: 10.1097/XCS.0000000000002168

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RTPB tools sit within the electronic prescribing workflow and show clinicians a patient’s expected out-of-pocket cost for a selected medication, alongside lower-cost alternatives where these are available. The intention is to allow cost to be factored into prescribing decisions at the point of ordering rather than at the pharmacy counter, where an unexpected price can lead to a prescription going unfilled.

The analysis found no difference in fill rates across all prescriptions written. When the researchers looked specifically at high-cost drugs, however, prescriptions were more likely to be filled when the tool was in use. The effect was most pronounced among patients from low-income communities, the group for whom out-of-pocket costs are most likely to act as a barrier to starting treatment.

The authors note an important limitation. The RTPB tool generated recommendations for only a small proportion of prescription orders, which means the findings apply to a narrow segment of the randomised population rather than to prescribing as a whole.

The corresponding author is Sunita M. Desai, PhD, of the Department of Population Health at NYU Grossman School of Medicine.

Read the full study: Real-Time Prescription Benefit Tool Availability and Prescription Medication Fill Rates

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More than one-third of high-risk therapeutic medical devices authorised for use in the United States have been subject to a serious recall, according to a cross-sectional study published today in JAMA Health Forum.

The study examined the relationship between the strength of premarket clinical evidence supporting high-risk therapeutic devices and their subsequent recall history. Serious recalls — those involving a reasonable probability of serious adverse health consequences or death — were identified in over 35% of the devices assessed.

Critically, the researchers found that device characteristics, the strength of the premarket clinical evidence submitted to regulators, and regulatory characteristics were not consistently associated with whether a device went on to be seriously recalled. In other words, stronger evidence at the point of approval did not reliably predict a safer performance record once the device reached patients.

The authors conclude that robust postmarket surveillance is needed to ensure patient safety, and that premarket review alone cannot be relied upon to identify devices that will later present serious risks to patients.

The corresponding author is Maryam Mooghali, MD, MSc, of the Department of Internal Medicine, Yale School of Medicine.

Study: Premarket Clinical Evidence Strength and Recalls of High-Risk Therapeutic Medical Devices
Journal: JAMA Health Forum
DOI: 10.1001/jamahealthforum.2026.2626
Full article (free access for readers for one year): https://jamanetwork.com/journals/jama-health-forum/fullarticle/10.1001/jamahealthforum.2026.2626?guestAccessKey=0809ea7a-d9c4-44c3-b758-f5703f67bccd&utm_source=for_the_media&utm_medium=referral&utm_campaign=ftm_links&utm_term=080726

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Why discharge summaries matter

A discharge summary is the primary method of communicating what happened during a hospital stay to GPs, community teams, community pharmacists, and sometimes patients’ carers. When this transfer of information is delayed or incomplete, the consequences can be serious: GPs may fail to follow up appropriately, or may unknowingly re-prescribe medication that caused harm in the first place. Patients themselves are often unclear about their own medication changes, increasing the risk of errors after discharge. The project’s goal, says Mr Chan, was to “improve both the quality and the timeliness of discharge information while reducing the administrative burden on clinicians.”

A process under strain

Before the redesign, the discharge process was slow and highly variable. Clinicians often had to review hundreds of pages of notes before compiling a summary — a particular challenge on mental health wards, where admissions can range from 30 to 120 days. Completion rates within 24 hours of discharge varied considerably between wards, from around 70% down to just 30%. Ownership of the task was also unclear, with doctors and pharmacists sometimes each assuming the other was responsible, while junior doctors juggled competing priorities. The result was delay, and community clinicians left without timely information on diagnosis, treatment, and medication changes.

Redesigning the pathway before adding AI

Recognising that the problem could not be solved by one professional group alone, the trust convened a multidisciplinary co-production group, chaired by a consultant psychiatrist. The team used performance data and direct feedback from GPs to clarify responsibilities and standardise workflows before considering any technology. This groundwork alone raised the overall proportion of discharge summaries reaching GPs within 24 hours to around 60% — but progress then plateaued.

Introducing AI

The trust had already been using an AI platform, Anathem, for about two years to support ambient voice transcription of outpatient consultations. Extending this technology to discharge summaries was, “a natural progression” says Mr Chan. The AI reviews up to 300 pages of clinical notes and drafts the two-part discharge document: a clinical summary and a medication summary, including the reason for admission, diagnosis, interventions, medication changes and reasons for stopping drugs, and follow-up requirements. Feedback from GPs showed they rarely read beyond two pages, so the AI-generated document was deliberately condensed to that length.

The impact on efficiency has been substantial. Producing a discharge summary manually took a junior doctor 45 minutes to an hour; with AI support, this has fallen to around 20 minutes. Timeliness has also improved sharply, with 24-hour completion rates rising from 60% to around 90%.

Safeguards and accountability

Crucially, the AI does not send documents automatically. The resident doctor must review, edit, and formally sign off the summary using a smart card before it is transmitted electronically to the GP, retaining full professional accountability. This reflects lessons learned from early problems, including “hallucinations or confabulations”; in one case, the AI mistakenly recorded a patient’s suicidal ideation as an intent toward strangulation. Such incidents reinforced the trust’s view that AI can support clinicians but cannot replace their judgement.

Access to the tool is similarly staged. Foundation-level doctors (F1 and F2) are excluded. Specialty Trainee (ST) level doctors must first demonstrate they can produce a discharge summary manually before AI access is granted — an approach Mr Chan compares to a pilot needing to be able to fly a plane manually before using autopilot.

What’s next

Having piloted the approach on three wards, the trust now plans to scale it across the wider hospital and into community services, while continuing to gather feedback from clinicians, GPs, and patients. Mr Chan believes the model is transferable across the NHS, since safe information transfer between care settings is a universal challenge, even if local workflows differ.

Looking ahead, he is clear that AI will not replace healthcare professionals. “The future is not about replacing healthcare professionals with machines,” he says. “It is about augmenting them… The combination of human judgment, compassion and professional accountability, supported by well-governed technology, will deliver the safest care to the patient.”

Poster presented at the Clinical Pharmacy Congress. London 8-9th May 2026

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

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

“AI is being adopted in medicine at a scope and velocity we have never seen before, but hospitals haven’t had a good way to weigh these decisions as a whole,” said Taylor, vice chair of research and innovation for the University of Virginia School of Medicine’s Department of Emergency Medicine. “Typical approaches tend to measure cost, because cost is the easiest thing to measure. We built this framework to give organizations a structured way to also weigh what an AI tool does for patients, for staff and for the quality of care.”

The Future of AI in Healthcare

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

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

Patient care, which the authors place at the top of the framework, emphasizes the importance of “patient-centered” 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. “Hospitals are seeing a huge number of new AI tools marketed to improve healthcare. The challenge is to figure out which ones actually will,” said Declan, clinical assistant professor in Clemson University’s School of Health Research. “That requires weighing an AI tool’s impact across clinical, operational and financial dimensions, while keeping patient care at the center of every decision.”

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

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

Framework Published

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

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 UVA’s new Paul and Diane Manning Institute of Biotechnology, bookmark the Making of Medicine blog at https://www.uvahealth.com/making-of-medicine.

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Researchers at the Technical University of Munich (TUM), Imperial College London, and the Hasso Plattner Institute (HPI) demonstrate that earlier calculations of AI model security are misleading. Attacks that aim to determine whether an individual’s data was used to train a model are known as membership inference attacks (MIAs). Until now, common medical AI models were considered largely secure against MIAs.

“Unfortunately, previous risk assessments have only ever measured the average risk across all patients. We examined the risk at the level of individual patients for the first time – and it paints a very different picture,” says TUM researcher Moritz Knolle, first author of the study. While the attacks were unsuccessful for a large proportion of the datasets, some patients could be linked to the models with near‑100 percent certainty. “This is not a tolerable risk. Health data is highly sensitive,” says Daniel Rückert, Professor of Artificial Intelligence in Healthcare and Medicine at TUM and, together with Professor Georg Kaissis (HPI), senior author of the study.

Different data types tested

The researchers attacked models based on seven established medical datasets. Each model relied on a different type of data, such as imaging data, electrocardiograms, or electronic health records. “An attacker needs three things to carry out a MIA,” explains Georg Kaissis, Professor of Digital Health: Human-Centered Transformative AI at the Hasso Plattner Institute. “First, access to the AI model being targeted, for example via a hospital network. Second, access to a data point for which they want to know whether it was included in the model, for example, data obtained in a cyberattack. Third, their own AI infrastructure – that is, computers running models based on the same type of data as the target model.”

With this setup, it would be possible, for example, to attack an AI model that uses blood test results to predict the likelihood of success of cancer immunotherapy. On its own, a blood test does not reveal whether a person has a disease. However, if an attacker can show that a specific data point was used to train the model, it becomes more likely that the patient has or had cancer.

Potential impact on individuals

Such digital attacks can have serious real-world consequences, as Moritz Knolle illustrates with a hypothetical example: “Imagine you were treated for cancer and made your data available for research,” says the medical informatics expert. “Years later – the cancer has not returned since then – you want to take out private supplemental insurance. However, an attacker has discovered that your data was used to train a tumor analysis model. This information reaches the insurer, for example through data analysis by third-party providers or corresponding risk profiles. You are then classified as a high-risk patient, with the corresponding premiums – and may never even find out why.”

The MIAs were particularly successful when targeted individuals belonged to groups that were underrepresented in the dataset. This could include certain anatomical characteristics in imaging data, but also data from minority groups. “This is especially serious because discrimination in AI also plays a role in medicine, and some models, for example, make less accurate predictions when the patient belongs to a minority group,” says Daniel Rückert.

Larger models show greater vulnerability

The researchers show that the attacks become more successful as the models grow larger and more complex. In the researchers’ view, the fact that high-performance models are particularly vulnerable indicates that the problem could become significantly more severe in the coming years if no countermeasures are taken.

They therefore advocate assessing the risks of new models at the level of individual patients before their release. Additional countermeasures include strict control of access to AI models. “There are already effective safeguards against MIAs that can be applied during model training. For example, differential privacy introduces small modifications into the training data that do not affect the model’s calculations but make MIAs significantly more difficult,” says Georg Kaissis.

Publication:

Knolle, M.A., Menten, M.J., Jungmann, F. et al. Disparate privacy risks from medical AI. Nature (2026). DOI:10.1038/s41586-026-10688-0.

Further information:

• Prof. Daniel Rückert holds the Chair of AI in Healthcare and Medicine at the TUM School of Medicine and Health and is a member of the TUM School of Computation, Information, and Technology, the Munich Data Science Institute (MDSI) as well as the Munich Center for Machine Learning (MCML).
• Original article: https://www.tum.de/en/news-and-events/all-news/press-releases/details/study-reveals-privacy-risks-in-medical-ai

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Fortunately, we are not quite there yet, but digitalization is advancing rapidly in the healthcare sector as well. AI chatbots and digital symptom checkers are playing an increasingly important role and are more and more serving as the first point of contact for so-called “self-triage”—that is, the initial assessment of the urgency of treatment by the patients themselves.

But while the technical capabilities of these systems are constantly growing, another factor is coming into the focus of research: how humans communicate with the machine. This is an important topic because even the best technology, especially in medical diagnostics, relies on precise information that users do not always provide in full.

Human reluctance limits the potential of AI

This is the central finding of a study now published in the journal Nature Health. The study was led by Professor Wilfried Kunde, holder of the Chair of Psychology III at the University of Würzburg, and Moritz Reis, a research associate in that department. It involved scientists from Charité – Universitätsmedizin Berlin, the University of Cambridge, as well as Helios Klinikum Emil von Behring and Vivantes Klinikum Neukölln in Berlin.

“The 500 study participants were tasked with writing simulated symptom reports for two common conditions – unusual headaches and flu-like symptoms” describes lead author Moritz Reis the study design. They were led to believe that their reports would be read either by an AI chatbot or a human doctor. The goal was to examine the quality of these reports in terms of their suitability for a medical urgency assessment.

Loss of quality is evident in reduced level of detail

The key finding: When participants believed they were communicating with artificial intelligence, the suitability of their descriptions for an initial medical assessment deteriorated measurably compared to interactions with supposed medical professionals. This effect was even observed among participants who were actually experiencing the relevant symptoms at the time of the survey.

This loss of quality is directly reflected in the level of detail in the reports. While descriptions provided to medical professionals averaged 255.6 characters, those provided to chatbots averaged only 228.7 characters.

Even though a difference of 28 characters may sound small, the research team states that this effect is practically relevant and can result in even high-performance AI models ultimately providing incorrect medical advice. After all, these models also fail to make an accurate medical assessment if patients do not provide all essential information. The success of digital initial assessments depends less on computational power than on the patient’s willingness to provide a detailed description.

Psychological Barriers: Concerns About a “One-Size-Fits-All Diagnosis”

But why are people so hesitant when it comes to machines? A key reason is likely what’s known as “uniqueness neglect.” “Many people assume that AI cannot grasp the individual nuances of their personal situation and instead merely matches standardized patterns,” explains Wilfried Kunde.

In addition, skepticism about algorithms’ diagnostic capabilities, as well as privacy concerns, may lead people to provide abbreviated or vague information. Moritz Reis sums up the human component this way: “If we don’t trust a machine to understand our uniqueness, we may unconsciously withhold the information it would need to provide precise assistance.” This psychological filter can have the effect that medically relevant details never even reach the system, thereby lowering the quality of the diagnosis.

Improving the dialogue with the machine

In the research team’s view, the findings clearly show that the technical advancement of AI alone is not sufficient. They therefore see a potential solution in the intelligent design of user interfaces.

To improve the quality of symptom reports, developers should provide concrete examples of high-quality descriptions and program the AI to actively request missing details. Only when users are encouraged to provide detailed information misdiagnoses can be avoided and the burden on the healthcare system could be effectively reduced.

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Continuous piperacillin/tazobactam infusion via elastomeric pump offers a safe, cost-effective alternative to inpatient antibiotic therapy in paediatric oncology, with measurable benefits for ward capacity, healthcare costs and quality of life.

Bacterial infections are among the most common and serious complications in children with cancer, frequently requiring prolonged antibiotic therapy. Piperacillin/tazobactam (pip/taz) is the established first-line intravenous treatment, but its standard three-times-daily dosing schedule creates a significant logistical burden — particularly for families living far from tertiary care centres. A six-month pilot at Oulu University Hospital (OYS) explored whether continuous pip/taz infusion via elastomeric pump could safely shift this therapy out of the inpatient setting, with meaningful benefits for patients, families and the healthcare system.

Background and rationale

OYS serves children across the Northern Finland collaboration area, a large and sparsely populated region where many families cannot realistically attend three outpatient infusion visits per day. Prior to the pilot, pip/taz therapy therefore required full hospitalisation for the entire treatment course — often three to five days, but sometimes several weeks. Elastomeric pumps, well-established in adult oncology, offered an alternative: continuous 24-hour infusion requiring only once-daily pump replacement, enabling home-based treatment or a single daily outpatient visit.

Methods

The pilot ran from November 2024 to April 2025. Weight-based dosing was established for children weighing 15–37.5 kg, using 120 ml FOLfusor (Baxter) pumps for lighter patients and 240 ml pumps for those weighing 30 kg and above. Children weighing 40 kg or more received the standard adult pump containing 12/1.5 g of pip/taz. Piperacillin was reconstituted at 173 mg/ml; tazobactam calculations were unnecessary given the fixed 4:0.5 ratio of the infusion powder. All pumps were prepared centrally at the hospital pharmacy under cleanroom conditions, with full batch documentation for every dose.

Results

The pilot demonstrated clear clinical and operational benefits. First, children were discharged from hospital earlier; once home, families reported improved appetite and increased physical activity in their children. Second, ward workload fell substantially — 117 hospital days were saved over the six-month period. Each elastomeric pump was priced at €95, a figure that covers the medication, the device itself, all required supplies and materials, and pharmacy preparation costs including personnel, cleanroom facilities, and microbiological monitoring. Compared with the cost of inpatient care, this translated to total savings of €54,000–73,000 over the pilot period. Third, pump therapy was successfully delivered to children throughout the collaboration area, including the smallest eligible patients, with centralised pharmacy preparation supporting consistent medication safety.

Implications for practice

These results confirm that elastomeric pump-delivered pip/taz, long used in adults, can be extended effectively to the paediatric oncology population. The model reduces pressure on inpatient beds, lowers nursing workload and generates significant cost savings — while simultaneously improving quality of life for children and their families during an already demanding period of treatment. On the basis of the pilot’s findings, pump-based pip/taz therapy has been adopted as standard practice at OYS Paediatric Haematology and Oncology.

Healthcare professionals seeking further information may contact the OYS Pharmacy team at elina.smolander@pohde.fi or tiina.kallio@pohde.fi.

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In an expert commentary titled ‘AI can reason like a physician; what comes next? published in Science, Flinders researchers caution that while new AI systems show impressive capabilities, strong results in controlled studies do not mean they are ready for routine use in hospitals or clinics.

The authors say there is an urgent need to understand how emerging AI tools can be safely integrated into everyday clinical practice, with patient outcomes remaining the central focus.

Despite these warnings, the researchers acknowledge that recent advances in AI create genuine opportunities to support doctors, particularly in busy and high-pressure care settings.

The commentary reviews new research showing that advanced reasoning-based AI systems can work through diagnostic scenarios step by step and, in some cases, closely match or even exceed the diagnostic performance of experienced doctors.

Erik Cornelisse, a PhD candidate at Flinders University and co-author of the commentary, says this shift marks a move from simple question answering tools towards algorithms capable of seemingly human-like clinical reasoning on text-based tasks.

However, the Flinders team stresses that real world medical care involves far more than text-based reasoning or test performance.

They say clinical practice depends on physical examination, listening to patients, understanding medical and social context, and taking responsibility for outcomes, elements that current AI systems cannot safely provide on their own.

“Health care decisions are complex, high stakes, and deeply human, and accuracy alone, particularly on just text-based cases, does not make a system safe for patients,” says Mr Cornelisse from the College of Medicine and
Public Health.

Senior author Associate Professor Ash Hopkins, an NHMRC Investigator and leader of Flinders’ Clinical Cancer Epidemiology Lab, says modern healthcare relies on judgement, accountability, and ethical oversight.

“AI systems have demonstrated that they can reason through clinical problems with similar performance to doctors, notably on the same scenarios used to train clinicians themselves. This presents genuine opportunities to support clinicians in the future,” says Associate Professor Hopkins.

“Multiple stakeholders are currently working on the frameworks for AI in terms of legal, professional, or moral responsibility for its decisions, and presently there is a critical need for deliberate and controlled integration into clinical care.”

The commentary highlights known risks linked to poorly evaluated systems, including bias, inequitable care, and unintended patient harm.

“History shows that algorithms can worsen outcomes when deployed without sufficient safeguards and can amplify problems as easily as they solve them, particularly when systems are trained on incomplete or unrepresentative data,” says Mr Cornelisse.

Looking ahead, the Flinders researchers argue that enthusiasm for medical AI must be matched by strong governance and clearer standards for evaluation.

“We do not allow doctors to practise without supervision and evaluation, and AI should be held to comparable standards,” says Mr Cornelisse.

The researchers stress that improvement in real patient outcomes, not exam scores, benchmarks, or demonstrations, must be the true measure of success.

Associate Professor Hopkins says AI holds enormous promise but must be applied responsibly.

“Patients deserve technology that improves care in the real world, not systems that only look impressive in studies,” he says.

“With careful design, strong oversight, and rigorous evaluation, AI could become a powerful tool to deliver safer, fairer, and more effective care across health systems worldwide,” concludes Associate Professor Hopkins.

The paper, ‘AI can reason like a physician; what comes next?’, by Ashley M. Hopkins and Erik Cornelisse is published in SCIENCE.  DOI 10.1126/science.aeg8766 (link live after embargo lifts)

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The findings are based on a nationally representative survey of more than 5,500 U.S. adults conducted from October through December 2025.

In the past 30 days, did you use an AI tool or chatbot for health-related information or advice for any of the following reasons?

% Yes, among adults who have used AI tools or chatbots for health-related information or advice in the past 30 days

Note. Categories are for descriptive purposes only and were not shown on the survey.

Among Americans who have used AI for health-related information or advice in the past 30 days, the most frequently cited motivations are wanting answers quickly (71%) and wanting additional information (71%). Nearly seven in 10 (67%) say they were curious about what AI would say, and roughly six in 10 report using AI to do research on their own before (59%) or after (56%) seeing a doctor.

Regardless of the reason, almost half (46%) of Americans who used AI for healthcare information say the AI tool or chatbot made them feel more confident talking with or asking questions of a provider. Others say it helped them identify issues earlier (22%) or avoid unnecessary medical tests or procedures (19%).

“Artificial intelligence is already reshaping how Americans seek health information, make decisions and engage with providers, and health systems must keep pace,” said Tim Lash, President, West Health Policy Center, a nonprofit and nonpartisan organization focused on aging and healthcare affordability. “The risk isn’t that AI is moving too fast — it’s that health systems may move too slowly to guide its use in healthcare responsibly.”

A Smaller Share Turn to AI in Place of a Provider

While self-directed research is the primary driver of AI health use, a smaller but notable share of recent users report turning to AI instead of seeing a healthcare provider, particularly when faced with cost, access or quality barriers. Among recent AI health users, 27% say they didn’t want to pay for a doctor’s visit and 14% say they were unable to pay. One in five (21%) say they didn’t have time to make an appointment, and 16% say they couldn’t access a doctor or provider. Another 21% say they felt dismissed or ignored by a provider in the past, and 18% say they were too embarrassed to talk to a person.

In the past 30 days, did you use an AI tool or chatbot for health-related information or advice for any of the following reasons?

% Yes, among adults who have used AI for health-related information and advice in the past 30 days

Among recent AI health users, 84% still saw a healthcare provider, but 14% report not seeing a provider they otherwise would have seen because of information or advice they received from AI. When projected to the full U.S. adult population, this represents roughly 14 million Americans who did not see a provider after receiving AI-generated health information.

Trust in that AI-generated health information, however, remains divided. Among those who consulted it in the past 30 days, roughly one-third say they trust it (33%), one-third neither trust nor distrust it (33%), and about one-third distrust it (34%). However, only 4% say they strongly trust the accuracy, indicating that many Americans are making healthcare decisions based on AI-generated information without full confidence in its accuracy.

About one in 10 (11%) who report using AI for health information or advice in the past 30 days say that AI recommended healthcare information or advice they believed was unsafe.

“This data indicates that while some Americans may be using artificial intelligence as a substitute for going to the doctor’s office, many see it as a tool to complement their healthcare, helping them understand symptoms they might be feeling and clarify any diagnosis they receive from their doctors,” said Joe Daly, Global Managing Partner at Gallup.

Motivations Vary by Age and Income

While information-seeking is the dominant reason Americans turn to AI for health purposes, use patterns differ by demographics. Younger adults are more likely than older adults to use AI for self-directed research — 69% of adults aged 18 to 29 say they do research before seeing a doctor, compared with 43% of those 65 and older.

Income differences are most visible in barrier-driven motivations. Among adults earning less than $24,000 annually, 32% say they used AI because they could not pay for a doctor’s visit, compared with just 2% among those earning $180,000 or more.

Everyday Health Questions Top the List of AI Use Cases

Americans who used AI for health information or advice in the past 30 days most often report using it to gather information about everyday health concerns, including physical symptoms (58%) and nutrition or exercise (59%). But AI use extends beyond symptom-checking — Americans who used AI in the past 30 days also report using AI to understand medication side effects (46%), interpret medical information (44%), or research a diagnosis or medical condition (38%). Nearly one in four (24%) report using AI to explore mental health or emotional concerns.

Methodology

West Health-Gallup Center on Healthcare, October-December 2025

Results are based on a Gallup Panel study conducted Oct. 27-Dec. 22, 2025, with a sample of 5,660 adults aged 18 and older who are members of the Gallup Panel, a nationally representative, probability-based panel of U.S. adults. Gallup uses random selection methods to recruit Panel members, including random-digit-dial (RDD) phone interviews that cover landlines and cellphones and address-based sampling (ABS) methods. Respondents with internet access completed the questionnaire as a web survey, and those without regular internet access were sent a printed questionnaire to complete and return by mail. The sample for this study was weighted to be demographically representative of the U.S. adult population, using the most recent Current Population Survey figures. For results based on this sample, one can say that the maximum margin of sampling error is ±2.1 percentage points at the 95% confidence level. Margins of error are higher for subsamples. In addition to sampling error, question wording and practical difficulties in conducting surveys can introduce error and bias into the findings of public opinion polls.

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“When we negatively interpret a situation, it impacts how we feel and respond—especially in people experiencing anxiety and depression,” said senior author Courtney Beard, PhD,  director of the Cognition and Affect Research Education (CARE) Laboratory at McLean Hospital, a member of the Mass General Brigham healthcare system. “By providing a simple, game-like exercise through an app, we have shown that we can help individuals gain insight into their thinking patterns in a more accessible and engaging way, that leads to meaningful improvements.”

Access to evidence-based treatments for anxiety and depression remains a significant challenge for many individuals due to provider shortages, high costs, and stigma surrounding mental health care. Digital tools have the potential to bridge these gaps; however, most available apps are not rigorously studied, resulting in a wide variance in quality and effectiveness. In addition, users often drop off these apps shortly after download. The researchers designed HabitWorks with these limitations in mind, working with an advisory board of individuals with lived experience of anxiety and depression.

In their new study, the investigators enrolled 340 adults across 44 states, who were randomized to use the HabitWorks app for four weeks or to a control condition that involved self-assessment surveys tracking symptoms of depression and anxiety.

Participants using HabitWorks reported significantly greater improvements in interpretation bias, functioning, and overall mental health symptom severity after one month compared to the control group. HabitWorks also achieved excellent retention rates with 77.8% of participants still using the app in week 4 and 84.4% of participants completing the post-intervention assessment.

“One thing that makes our approach unique in digital mental health is its focus on short, five-minute exercises,” said lead author Alexandra Silverman, PhD, a clinical investigator in the CARE Laboratory. “Unlike traditional interventions that mimic long therapy sessions, HabitWorks aligns with how people use their phones in short bursts, creating an approach that fits into daily life.”

HabitWorks is currently not available to the public. Further research is needed to identify which populations would benefit most from HabitWorks, the longevity of its effects and methods for delivering the intervention beyond a research setting. For more information on HabitWorks and to sign up for its waitlist, visit this website.

Authorship: In addition to Silverman and Beard, Mass General Brigham authors include Gabriela Kovarsky Rotta and Doah Shin.
Disclosures: None.
Funding: This work was supported by the National Institute of Mental Health (R01MH12937) and by Harvard Medical School’s Livingston Fellowship and McLean Hospital’s Pope-Hintz Endowed Fellowship.
Paper cited: Silverman, A. et al. “Randomized Controlled Trial of Smartphone-Based Interpretation Bias Intervention for Anxiety and Depression” Journal of Consulting and Clinical Psychology DOI: xxx

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The authors show that existing regulatory requirements for approval have so far only partially addressed many of these so-called “human factors-related risks”. This can create gaps that impact the safety and quality of care. To address these, the researchers identify seven key risks and develop practical recommendations for action that can be integrated into existing regulatory and documentation processes.

Risks in the use of AI systems

AI-based medical devices can be used in various areas of clinical environments. In radiology, for example, they assist in detecting cancer. Clinical decision support systems help select personalized therapies for patients.  AI can also support real-time monitoring and early warning systems, as well as chatbots for applications such as patient communication and software that automatically generate medical reports or summarize findings. The analysis focuses on risks that may arise in the practical use of such AI systems. These include, for example, an increased likelihood of outputs being misunderstood or misinterpreted due to the sometimes-opaque nature of AI systems. Problems can also occur when trust in the application is miscalibrated: resulting in users either relying too heavily on AI assistance or ignoring relevant recommendations. The researchers also point to the risk of automation bias: the tendency to uncritically adopt recommendations from automated systems, potentially overlooking errors or forgoing independent judgment. Additional risks include potential deskilling, technostress among users, an unchecked expansion of indications beyond the originally intended scope (indication creep), and errors related to system changes or different operating modes. Such factors can create additional burdens or unexpected failures in clinical practice – even when the technical performance of a system itself is strong.

A practical guide for manufacturers and evaluators

For their analysis, the research team evaluated existing standards on usability and safety, regulatory guidelines, alongside the scientific literature on AI in healthcare. In addition, expert discussions from the fields of clinical application, regulation, and human factors were incorporated. The result is a practical guide, that fills a gap in current standards, with seven recommendations. These are intended to support manufacturers and evaluators both before and after a product is placed on the market. The aim is to identify AI-specific risks in interaction with human users at an early stage and to address them systematically.

The framework recommends developing and deploying AI-based medical devices in a way that clearly defines the users, in which context the systems are applied, and which tasks are assigned to humans and which to the system. Furthermore, results should be presented in a way that is easy to understand, integrated into existing clinical workflows, and supplemented by training where needed as well as safe fallback options in the event of system failures. The authors emphasize the importance of continuous monitoring after market entry. Usage patterns, potential misuse, or overreliance on AI systems should be systematically observed and corrected as needed. Changes to the systems must also be communicated transparently so that work processes can be adjusted accordingly.

The recommendations are deliberately formulated in general but regulatory-aligned terms so that they can be applied to different AI-enabled medical devices and application scenarios. In a next step, the researchers aim to test and further develop their recommendations based on concrete pilot applications with AI-enabled medical devices. In the long term, human factors should be systematically considered in the regulation and evaluation of AI-based health technologies – reducing avoidable risks while supporting safe innovation in medicine.

The article was authored by researchers from TU Dresden (EKFZ for Digital Health, Chair of Industrial Design Engineering, and Faculty of Business and Economics), in collaboration with experts from the University of Oxford (United Kingdom) and Geneva University Hospital (Switzerland).

Publication

Rebecca Mathias, Anne Schmitt, Mateo Campos, Baptiste Vasey, Sebastian Lorenz, Peter McCulloch, Stephen Gilbert: Evaluation of Human Factors-Related Risks in AI-Enabled Medical Devices: A Practical Guide, NEJM AI, 2026. Link: https://ai.nejm.org/doi/full/10.1056/AIpc2501297

Else Kröner Fresenius Center (EKFZ) for Digital Health

The EKFZ for Digital Health at the Faculty of Medicine at TUD Dresden University of Technology and University Hospital Carl Gustav Carus Dresden was established in September 2019. It receives funding of around 40 million euros from the Else Kröner Fresenius Foundation for a period of ten years. The center focuses its research activities on innovative, medical and digital technologies at the direct interface with patients. The aim here is to fully exploit the potential of digitalization in medicine to significantly and sustainably improve healthcare, medical research and clinical practice.

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