Tag: artificial intelligence

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Making Medical AI Smarter and Safer

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A conversation with Qingyu Chen, PhD, about what medical artificial intelligence learns, what it memorises, and what it takes to use it responsibly.

Qingyu Chen, PhD, and his team set out to study how artificial intelligence language models are adapted for medicine and found that what these models memorise can be both useful and risky. A model may retain valuable medical knowledge, but in a controlled study using real hospital records, the same fine-tuning – the added training that adapts a model to a specific task – that improved diagnostic performance also made it more likely to reproduce material it had seen during training, including sensitive patient information.

The study, published recently in Nature Communications, reflects a question at the center of Chen’s research: How can medical AI become not only more capable but also more reliable and safer? The study was led by its first author, Anran Li, PhD, who conducted the research as a postdoctoral researcher in Yale’s Department of Biomedical Informatics and Data Science.

Chen is an assistant professor of biomedical informatics and data science at Yale School of Medicine, with a secondary appointment in ophthalmology. He leads research on the accuracy and reasoning of medical language models and on multimodal AI-assisted disease diagnosis, which draws on both text and medical images.

The following conversation with Chen discusses what medical AI learns, what it memorises, and what it takes to use it responsibly.

What is your lab’s research focus?

Our lab does two things that are usually treated as separate: We build medical AI, and we study where it fails.

On the building side, we work with two main kinds of information medicine runs on. We develop models that read clinical records and the medical literature, models that analyse medical images to help diagnose disease and predict its course, and systems that combine both, so an AI can weigh a patient’s written history alongside their scans, the way a physician would.

But a model that performs well on a test is not the same as a model you can trust with a patient. So, we also study how these systems fail. They can state falsehoods with complete confidence. They can reach a right answer through faulty reasoning. And, as our recent work shows, they can memorise sensitive information from the data they were trained on.

Our goal is to develop medical AI that is genuinely useful, understand where it breaks down, and produce the evidence needed to know when it can – and cannot – be trusted.

Why work across both text and images?

Because medicine is inherently multimodal. A patient cannot be understood through a single paragraph or a single image. Clinical decisions often require bringing together a patient’s history, laboratory results, medical notes and imaging findings.

Our work spans both sides of that. On the language side, we develop methods to help computers understand clinical records and biomedical literature. On the imaging side, much of our research focuses on medical images and specialties that depend heavily on them—ophthalmology in particular, where we work on diagnosing eye disease and predicting how it will progress. This is also why I hold a secondary appointment in ophthalmology.

What excites us most is bringing these areas together: systems that can integrate text, images and other clinical information to form a more complete picture of a patient and better support medical decision-making.

Progress here requires more than developing new models. One of the biggest barriers is the limited availability of medical data that is large enough, reliable enough and free for researchers to share.

To help address this, we recently developed MedPMC, a system that has assembled 11 million medical images paired with their accompanying text, drawn from research literature that is openly licensed for reuse—and that is designed to keep growing as new research is published. We’ve made the data, the tools used to assemble it, the tests for measuring performance and the resulting models publicly available so that other institutions can develop, evaluate, reproduce and adapt these systems rather than starting from scratch.

Your team recently published a study in Nature Communications on how AI models ‘memorise’ medical data. What does memorisation mean here?

Memorisation means that a model can recall or reproduce content it encountered during training. If a model has been trained on clinical guidelines, it may reproduce part of a guideline when you give it the opening of that passage. If it has had additional training on a set of medical exam questions, it may produce an answer choice that appeared in that training data – even after we removed that choice from the question.

That is different from simply saying that a model performs well. When a model answers a question correctly, there are several possible explanations. It may have acquired genuine medical knowledge. It may have learned a pattern of reasoning it can apply to problems it hasn’t seen before. Or it may simply recognise the question and reproduce something it previously saw during training. If all we check is whether the final answer is correct, we cannot tell these apart.

So, our goal was to look beyond accuracy and ask a different set of questions: How often does memorisation occur? What types of content get memorised? How much can a model reproduce? Does what it memorised earlier survive further training? And what does all of this mean for using these systems in medicine?

What did you find?

We looked at the main stages a general-purpose model goes through on its way to becoming a medical one.

First, we examined models that had already undergone continued pretraining, in which a general-purpose model is trained further on large collections of medical text, including biomedical literature, clinical guidelines and clinical notes. Second, we evaluated models that had been fine-tuned on the standard question-and-answer datasets that the field uses to test medical models. Third, we conducted a privacy-protected, HIPAA-compliant study using more than 13 000 medical records to fine-tune models for disease diagnosis.

This was done in an isolated and secure computing environment. These records had already been collected in the course of care; the study did not recruit patients or change anyone’s treatment.

Across those settings, we examined both general-purpose models and models trained on medical data, 10 different datasets containing hundreds of thousands of records and thousands of model responses that we manually reviewed.

The patterns differed depending on the training stage. Continued pretraining was more likely to produce long, word-for-word matches to source documents. Fine-tuning produced less long-form copying in some settings, but more memorisation tied to the specific task the model was trained on. For example, after fine-tuning on medical question-and-answer datasets, models reproduced roughly 14% to 21% of the answer choices that had been removed from the question the model was shown.

We also found that memorisation was persistent. Depending on the setting, as much as 87% of what a model memorised during continued pretraining was still present after it had been fine-tuned on a new medical task. Fine-tuning does not necessarily erase what a model previously memorised. It may preserve that content while adding new memorisation specific to the task it was just trained on.

What did the clinical case study show?

The clinical case study showed both the potential benefit and the risk of adapting these models to real-world medical data. Fine-tuning improved diagnostic performance; for one model, the correct diagnosis came up as its first choice 54.8% of the time, up from 48.6%. In some specialties, the gains were larger than that – more than 10 percentage points in areas such as cardiology and nephrology, which deal with heart and kidney conditions.

At the same time, the study showed a real privacy risk. In a controlled test conducted in a secure research setting, we found that the model could sometimes reproduce sensitive information from the records used to train it. This was not something that would happen during patient care, but it shows that privacy risks should be evaluated before models trained on clinical data are shared or deployed.

Is memorisation always harmful?

No. One of the central findings of our study is that memorisation in medicine is not a single behaviour.

We identified three broad types. The first is beneficial memorisation. A model may accurately retain biomedical concepts, clinical guidelines, the medical literature it has read or specific medical knowledge tied to its task. That kind of memory may support factual accuracy and help the model perform medical tasks more effectively.

The second is uninformative memorisation. Models sometimes reproduce document disclaimers, section headings, formatting instructions or other boilerplate language. This adds little medical value and may indicate that the model is learning surface-level patterns rather than deeper medical understanding.

The third is harmful memorisation. This includes reproducing quirks specific to a particular dataset, word-for-word passages from patient notes, protected health information or other sensitive patient content. This form of memorisation may create privacy risks and may also indicate that the model is leaning too heavily on its training data rather than generalising to new cases.

The important question, then, is not simply whether a model memorises; it is what the model memorises, why it memorises it, and whether that memory supports or undermines the medical use it is intended for.

Did anything about the way memorisation develops surprise you?

One thing that stood out: Memorisation did not only show up late, after a model had been trained too long. It began early.

As we trained the models, we tracked their progress and compared three things: how much they were memorising, how well they were learning by the usual measure, and how accurate their diagnoses were. Memorisation began increasing relatively early, even while the standard measures still showed the model improving, and before its diagnoses had reached their peak accuracy.

That means traditional indicators researchers watch during training – such as whether the model keeps improving on held-out data, or the point at which they would normally stop training – are useful but may not be sufficient on their own. A model can appear to be learning effectively according to standard performance measures while simultaneously increasing its retention of training-specific content.

We also found two other patterns. Larger models and longer inputs were generally associated with more memorisation. By contrast, changing common generation settings such as temperature – which controls how varied the model’s answers are – had relatively limited effects. This suggests that memorisation is fundamentally connected to how a model is trained and what data it is exposed to, rather than being something that can simply be solved by adjusting how the model generates its answers after training.

What do you most want readers to take away from this work?

Adapting an AI model to medicine does not simply make it “more medical.” It changes what the model knows, what it remembers and what it may reproduce.

Some of that memory is valuable. We want models to retain accurate medical knowledge and clinical guidance. But we do not want them to rely on meaningless repetition, echo back the answers to test questions or expose sensitive information from patient records.

Trustworthy medical AI therefore requires more than measuring whether a model gets the answer right. We need to understand how it got there, what it retained from training, and whether it will stay safe and reliable when used in a new setting.

Source: Yale School of Medicine

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New AI Tool Predicts Hip Fracture Risk Better than Current Screening

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A tool developed using data on more than 3.5 million adults in Sweden can identify individuals at high risk of hip fracture using registry data, with no in-person assessment

Taokinesis, Pixabay Image by Dr. Manuel González Reyes from Pixabay

A machine-learning tool built from Swedish national health registry data can predict hip fracture risk with high accuracy and no in-person assessment, and identifies far more at-risk individuals than current clinical screening practices, according to a study published August 27thin the open access journal PLOS Medicine by Kristian Axelsson and Mattias Lorentzon of the University of Gothenburg, Sweden, and colleagues.

Hip fractures are associated with substantial disability, illness, and death in older adults, but existing risk prediction tools typically require in-person patient assessment, including measurements like body mass index and lifestyle information, making large-scale screening difficult.

Researchers analysed nationwide registry data from 3 542 647 individuals aged 50 and older in Sweden, following them for up to ten years. During the study period, 142 327 of the participants sustained a hip fracture. Using more than 100 000 variables drawn from diagnoses, medications, procedures, and demographic and socioeconomic data, the research team developed and tested a deep-learning approach called FRACTURE-ML.

When tested on data from a separate group of people, not included in the original model development, FRACTURE-ML showed good discrimination of people who went on to fracture their hip from those who didn’t with an area under the curve (AUC) of 0.89 one year ahead, and only slightly worse with AUC 0.85 when predicting five years ahead. A simplified version using just 35 variables performed nearly as well. Compared with the current screening methods used in Swedish clinical practice, FRACTURE-ML identified nearly seven times more people at risk of hip fracture within two years (sensitivity 0.84 versus 0.12), with only a modest reduction in specificity (0.79 versus 0.98).

Because the model relies solely on registry data, it lacks information on lifestyle factors such as smoking and alcohol use, which may also affect fracture risk. The authors note that validation in other countries and studies testing real-world implementation are still needed.

“The findings show that it is possible to predict hip fracture risk at the population level without direct patient interaction,” lead author Kristian Axelsson says. “This approach could help target preventive measures more efficiently and potentially reduce the number of hip fractures.”

Mattias Lorentzon adds, “FRACTURE-ML accurately identified people at high risk of hip fracture using routinely collected healthcare and population data, without requiring an in-person clinical assessment. This could make large-scale screening more efficient and help preventive care reach people before a hip fracture occurs.”

“Hip fractures have serious consequences for independence, health and survival. A tool that can identify high-risk individuals directly from existing data could support earlier intervention and potentially reduce the burden of hip fractures across the population,” the authors say.

“One important finding was that a reduced model using only 35 predictors performed nearly as well as the much larger machine-learning model. This suggests that strong predictive performance may be achievable with a comparatively practical and interpretable tool.”

“By using information already available in national registers, FRACTURE-ML could help shift hip-fracture care from reacting after an injury to preventing the injury in the first place.”

“Machine learning performed very well, but carefully developed traditional statistical models achieved similar accuracy. The key advance may therefore be less about a particular algorithm and more about making better use of comprehensive, routinely collected data.”

Provided by PLOS

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Most Approved AI Medical Devices Were Not Tested on Patient Outcomes

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 Of 1357 devices authorised by the US FDA, only 3 were evaluated on clinical effectiveness 

Growth of FDA-cleared AI/ML-enabled medical devices from 1995 to December 2025. Of 1,357 cleared devices, only 34 were linked to registered clinical trials and only 3 were evaluated for patient-centred outcomes. (Fig 1 of the article.), Credit: Abulibdeh R, Cajas Ordóñez SA, Celi LA, Gorijavolu R, Izath N, Markussen Lunde T, 2026, PLOS Digital Health, CC-BY 4.0 (https://creativecommons.org/licenses/by/4.0/)

A new analysis shows that, of 1357 artificial intelligence (AI)-based medical devices authorised by the US Food and Drug Administration (FDA) for use in patient care, only three had been tested on whether they actually improve patients’ health. Rawan Abulibdeh of the University of Toronto, Canada, and colleagues present these findings in the open access journal PLOS Digital Health on August 19, 2026. 

New AI devices increasingly inform clinical care, such as systems that aid surgical planning, calculate cardiovascular risks, and guide interpretation of mammograms and other imaging. In order to be authorised for use in the US, AI devices typically only need to show “substantial equivalence” to an existing authorised device, and developers are not required to demonstrate whether new AI devices help people live healthier lives – with benefits shared equitably across diverse subgroups. 

To deepen understanding of this topic, Abulibdeh and colleagues investigated how all 1357 AI devices authorised by the FDA as of December 5, 2025, had been evaluated in patients prior to authorization. 

They found that only 34 of the devices had been included in registered clinical trials, with results posted for 12 and peer-reviewed manuscripts published for 12. Only 3 devices had been tested on patient-centred outcomes, such as death rates, strokes, hospitalizations, and quality of life. Most studies were conducted in highly resourced healthcare systems, and most excluded key patient subgroups, such as pregnant women, adults over 75, and non-English speakers. 

The researchers suggest that structural barriers such as financial incentives and logistical challenges discourage developers from testing AI devices on patient outcomes, resulting in greater emphasis on speedy development than on rigor. They discuss how this framework could allow new tools to amplify existing disparities in healthcare and how it could lead to patients in low- and middle-income countries becoming inadvertent test populations for under-studied AI devices, as many countries rely on higher-income countries’ authorisation decisions. 

On the basis of their findings, the researchers conclude that existing policies for AI medical device authorization should be redesigned. They propose a novel, three-phase framework that includes demonstration of effectiveness across diverse patient subgroups and healthcare settings.

 The authors add: “We expected the evidence base to be thin, but not this thin. Out of 1357 AI devices the FDA has cleared for use in patient care, only three have been tested on whether patients actually live longer or better. Clearance tells you a device resembles something already on the market. It does not tell you it helps anyone.”

Provided by PLOS

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Real World Test of AI Clinical Support Tool Improved Clinician Decisions

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Trial did not show statistically significant difference for patient outcomes but helped clinicians improve quality of notes and recommendations

A large real-world clinical trial has found that a generative AI-powered support tool used to support frontline clinicians was safe and improved the quality of clinical decision-making but did not significantly change short-term patient outcomes.

The study, published today in Nature Medicine is one of the first randomised controlled trials worldwide to test whether generative AI can improve patient-level outcomes, rather than just clinician performance or simulated cases.

The trial involved more than 9600 patients attending 16 primary care clinics in Kenya, and was delivered by experts at the University of Birmingham supported by the National Institute for Health and Care Research (NIHR) Biomedical Research Centre: Birmingham.

What this study shows is that AI can be integrated safely into real clinical workflows, without undermining patient trust or clinician autonomy – which is a critical foundation for any future impact.

Alastair Denniston, Chair of Regulatory Science and Innovation

Clinicians were randomly assigned to use an electronic medical record system with or without an integrated AI consult tool that provided real-time diagnostic and treatment suggestions. The AI system, known as ‘AI Consult’, was a large language model–based clinical decision support tool embedded directly within the existing electronic medical record system.

During consultations, the tool worked in the background by:

  • Analysing information entered by the clinician into the medical record
  • Generating context‑specific diagnostic and treatment suggestions, aligned with Kenyan national clinical guidelines
  • Flagging potential concerns using a simple colour‑coded alert system (green, yellow or red)

Clinicians retained full autonomy; they were not required to follow the AI’s advice, and retained responsibility for all diagnosis, prescribing and referral decisions. The AI interface was not visible to patients, helping preserve normal patient–clinician interaction.

Senior author Professor Bilal Mateen, Honorary Professor of Machine Learning for Health at the University of Birmingham, and Chief AI Officer at PATH, said: “This is one of the first studies to rigorously ask the hardest question about AI in healthcare: whether it actually improves outcomes for patients.

“What we found is reassuring but also sobering. The technology appears safe and clearly improves aspects of clinical decision-making, but translating those gains into measurable patient benefit is much more challenging, particularly in everyday primary care.”

Serious outcomes such as hospitalisation or death are rare in primary care, meaning extremely large studies – potentially involving more than 100 000 patients – would be needed to detect modest effects.

Professor Alastair Denniston, co-author, Professor of Regulatory Science and Innovation at the University of Birmingham and lead for health data research at the NIHR Biomedical Research Centre: Birmingham, said: “A large part of primary care is to deal with common conditions, including those that are self-limiting, where many patients require low levels of healthcare intervention. In that context, even meaningful improvements in clinical reasoning may only result in small changes in patient outcomes that are very difficult to measure.

“What this study shows is that AI can be integrated safely into real clinical workflows, without undermining patient trust or clinician autonomy – which is a critical foundation for any future impact.”

Findings: safety, quality and costs

Researchers found no statistically significant difference in treatment failure within 14 days between patients seen with AI-supported care and those receiving standard care (2.2% vs 2.0%). The study found no evidence of harm, with similar rates of hospitalisation and death in both groups.

While the AI tool did not produce measurable improvements in short-term patient outcomes, it significantly improved the quality of clinical documentation and treatment planning, as assessed by an independent panel of experienced clinicians who were blinded to whether AI had been used.

Patient satisfaction was the same in both groups, suggesting that AI support did not alter patients’ experience of care.

The study also found that, although overall antibiotic prescribing rates were similar, antibiotic‑related costs were lower in the AI‑supported group, due to more cost-conscious prescribing choices.

Although the trial was conducted in Kenya, the researchers emphasise that the findings have global relevance, including for high-income health systems.

Professor Richard Riley, Professor of Biostatistics at the University of Birmingham and senior author, said: “Robust trials like this are so important to establish the real impact of using AI in practice. They help set realistic expectations of what AI can actually contribute within existing care pathways, and helps guide where future investment and research effort should be focused. Generalisability of our findings to higher-income settings, where baseline standards of care are already high, needs to be evaluated.”

Source: The University of Birmingham

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Incorrect AI Advice is a Blind Spot – Even for Doctors

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 New study highlights potential challenges for using automated tools in healthcare

Photo by Accuray on Unsplash

In experiments in which physicians made decisions about treating hypothetical patients, the physicians tended to trust incorrect advice presented as being generated by artificial intelligence (AI), even after given the opportunity to notice that patient recovery data contradicted the recommendations. Aranzazu Vinas of the University of the Basque Country, Spain, and colleagues present these findings in the open-access journal PLOS Digital Health. 

AI systems can help physicians categorise patients according to their different care needs, such as whether a patient is more or less likely to benefit from a certain treatment. Since these systems are not perfect, they are meant to be used as suggestions, with potential errors caught and corrected by physicians.

 Prior research has shown that, in general, people struggle to notice and correct mistakes made by AI. To explore how this challenge may extend to physicians, Vinas and colleagues analysed data from 223 physicians who anonymously participated in online experiments.

 The physicians were asked to imagine they had the option to treat patients for a rare disease using a not-yet-proven treatment still under development. They were told that an AI system had identified which patients were more or less likely to benefit from the treatment. The physicians then chose which patients to treat, and after being presented with data on patient recovery, rated their perceptions of how reliable the AI was.

 Crucially, the actual effectiveness of the hypothetical treatment did not align with the AI recommendations. In one experiment, the treatment was equally moderately effective for all patients, and in a second experiment, it was equally ineffective for all.

 However, in both experiments, the physicians tended to rate the AI system as reliable and apparently did not use the patient recovery data to conclude that the AI recommendations were incorrect. In the second experiment, the physicians did not realise that the treatment was entirely ineffective.

These findings highlight potential challenges for incorporating AI-based classification into healthcare. Future research could build on this study, such as by developing and testing strategies and protocols that could increase human critical thinking and detection of AI errors, in order to maximize the benefits of the human-AI collaboration while minimising potential errors.”

Lead author Aranzazu Vinas notes: ” In both experiments, physicians mostly trusted the AI’s classifications and had trouble learning from the feedback. Furthermore, in the second experiment, professionals did not notice that the treatment was completely ineffective.”

 Co-author Helena Matute adds, “People tend to say that there is always a human controlling the algorithm, but our experiments show that doctors (as well as anyone else) have problems in learning from the available evidence when it contradicts the suggestions of an algorithm.”

Co-author Fernando Blanco summarizes: “It is important to investigate the errors that humans (including doctors) make when working with algorithms, in order to learn how to minimize the problems that arise from them.”

Press Preview: https://plos.io/4wjPxSs

In your coverage please use this URL to provide access to the freely available article in PLOS Digital Health: https://plos.io/4blGKHA

Contact: Aranzazu Vinas, aranzazu.vinas@ehu.eus

Image Caption: Doctors working on an AI-support system

Image Credit: Photo by Accuray on Unsplash. Free to share under the Unsplash license.

High-Resolution Image Link: https://unsplash.com/photos/a-few-men-looking-at-a-computer-screen-S34fEzWT6eE

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AI Mistakes Can Cost Doctors Time when Writing to Patients

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Errors and irrelevant details mean physicians may spend more time editing AI-drafted responses than it would take to write them, a large study of an online patient portal shows

Photo by National Cancer Institute on Unsplash

Artificial intelligence is spreading rapidly in health care, with the goal of streamlining critical but onerous clerical tasks such as note-taking and charting so that physicians and nurses can devote more time to patients. But even when AI can free up doctors to correspond with patients, it may fall short in helping them do it by introducing errors and extraneous details into their messages, according to a new Dartmouth study presented at the 2026 Annual Meeting of the Association for Computational Linguistics and published in the conference proceedings. 

The result is that physicians may spend more time editing responses than it would’ve taken to write them, the researchers report.

“We find that AI can sound like a doctor but not think like one,” says Sarah Preum, an assistant professor of computer science and the study’s co-corresponding author with Parker Seegmiller, a graduate researcher in Preum’s PersistLab at Dartmouth. 

The researchers conducted the first large-scale study of an online patient portal that uses AI to draft responses from physicians to patients. The team developed a tool that compares AI-generated replies to a dataset of real responses they developed with health care professionals from Dartmouth Health. 

They then analysed 146 000 conversations between 10 105 patients and their primary care physicians at the large rural health system, with data anonymised.

The researchers also used their tool to evaluate physician responses drafted by Claude, Gemini, and ChatGPT, as well as the three smaller commercial platforms, Llama, Aloe, and Qwen. 

“We find that AI can sound like a doctor but not think like one.” 

Sarah Preum, corresponding author and assistant professor of computer science

The team reports that AI-generated answers frequently misalign with what clinicians would actually write. This includes automated responses that are too long, don’t ask follow-up questions, and use irrelevant or inaccurate medical details.

“There are smaller studies that say, ‘Oh, AI is amazing,’ but we realised there is a gap in the existing literature of a large-scale evaluation of this technology,” Preum says. “We didn’t just want to measure a platform’s accuracy, but whether it actually helps with the workload, which in this case is measured by how much editing the physician is doing.” 

For example, the portal’s AI suggested telling a 32-year-old woman who is taking an acid reflux drug and was concerned about constant nausea that the medication might take some adjustment in diet. A physician replaced that by asking if there’s any chance she was pregnant. 

Even little changes can add up over hundreds or thousands of messages, Preum says. “You don’t want to integrate large language models into the workflow and just shift the bottleneck so that doctors are devoting their cognitive energy to playing AI janitor and fixing mistakes,” Preum says. “But if we’re not careful, that’s a likely outcome.”

The researchers show, however, that adapting AI to how individual physicians communicate can improve accuracy by 33% and reduce editing by 26%. 

“If message generation is really efficient and high quality, if it asks the right things, then it really has potential to improve efficiency,” says co-author Tim Burdick, an associate professor of community and family medicine in Dartmouth’s Geisel School of Medicine and a family medicine physician at Dartmouth Health. 

“I don’t foresee a time when the portal can respond to a patient without a clinician editing it first. But as we make the models better, we’ll be able to address portal messages much more quickly and with less mental energy,” Burdick says. 

The study shows that there are such things as “good” AI responses and provides a framework for implementing them into patient-physician portals, Preum says. These platforms are increasingly common among large health care systems and often customized, she says.

“That took us a long time to figure out, but if you’re trying to measure how effective this technology is, you need to define what a good response is,” she says. “We can only improve what we can measure and objectively evaluate.” 

The researchers created a technique called TADPOLE (Thematic Agentic Direct Preference Optimization for Learning Enhancement) that trains AI platforms using the hybrid model they constructed from physician- and AI-generated responses.

They plugged TADPOLE into the six commercial LLMs and found that drafted responses better matched physicians’ standards for precision and information quality. “That could save a busy clinician an hour or two of work a day,” Burdick says. 

Doctors and nurses today are inundated with messages from patients and caregivers who can write them online anytime, he says. An ongoing project between Burdick and the Preum Lab called PortalPal aims to streamline patient portals using AI, including by automating some steps in following up with patients to get more information.

“We’re still nowhere near the point of having clinicians removed from the workflow.”

Tim Burdick, co-author and associate professor of community and family medicine

Physicians who Burdick works with say that AI-generated drafts save about 25% of their time on shorter messages. “It’s easier to make small edits to an LLM-generated message than it is to write it from scratch,” he says. But longer drafts can include information that is not correct or accurate. 

“If you have to edit 75% of the message, you may be spending more time and energy on making changes than if you were to just write it from scratch,” Burdick says. “I would guess we need to get to where the physician is editing less than 30% of the content before it has substantial benefit.”

One advantage of AI’s verbosity is that it tends to be more empathetic and thorough than physicians crunched for time, the researchers find. For example, AI is more likely to tell a patient experiencing an upset stomach that it’s sorry to hear they’re feeling nauseated. 

This means AI could be used to help “nudge” doctors to show more understanding and care for the patient’s situation, or answer patient’s questions more effectively so that patients feel more heard, Preum says. The team produced sample responses such as showing empathy by praising patients for following a treatment plan (“You’ve been doing a great job with your tapering.”) or planning for changes in symptoms (“If you’re feeling dizzy, please call triage.”).

The researchers also find that 65% of all the portal messages they studied came from people over 55, with patients over 65 generating 24% of all messages. These figures suggest that patient portals in general should be designed to accommodate older people, Preum says.

Future work will study how much actual time doctors spend editing automated drafts. The team also plans to evaluate their training model TADPOLE from the user perspective, studying if and how it lightens a physician’s workload, and how doctors and patients rate its performance. 

“This is one of the first studies that uses real patient portal messages to establish a generative AI model. In that regard, it’s innovative and shows us that this is not a simple task,” Burdick says. “We’re still nowhere near the point of having clinicians removed from the workflow.”

Source: EurekAlert!

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Study Shows AI Can Help Clinicians Identify Brain Tumour Risks

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By Katelin Shaft

Mayo Clinic researchers and collaborators have shown that an artificial intelligence (AI) tool can analyse routine pathology slides to help clinicians classify meningiomas, the most common primary brain tumour in adults, and better understand a patient’s risk of tumour recurrence.

The study, published in The Lancet Digital Health, demonstrates that deep learning models can support the extraction of molecular and prognostic information from standard haematoxylin and eosin, or H&E, slides – the same type of tissue images already used in routine clinical care. These insights are typically obtained through DNA methylation profiling, an advanced genetic test which provides valuable diagnostic and prognostic information but can be costly, time-consuming and is unavailable in many hospitals.

“This is one of the many studies where we can harness the strength of digital pathology by capturing the last two decades of genomic and molecular knowledge into AI algorithms,” says Gelareh Zadeh, MD, PhD, chair of the Department of Neurologic Surgery at Mayo Clinic in Rochester and Chief Medical Officer for Mayo Clinic Platform.

Making advanced tumor insights more accessible

Meningiomas can vary widely in behaviour. Some grow slowly and may never return after treatment, while others are more aggressive and more likely to recur. Understanding that risk is critical for patients and care teams deciding whether additional treatment, such as radiation therapy, may be needed after surgery.

Molecular testing can help identify which tumours are more likely to recur and which may respond differently to treatment. But these tests require specialized technology and expertise, limiting access for many patients.

Using tissue samples, pathology images and clinical data from 672 patients, researchers developed and tested AI models designed to help identify patterns linked to a tumour’s biology. Drawing on multiple de-identified datasets, including data resources from Mayo Clinic Platform, the models supported classification of meningioma subtypes and recurrence risk prediction using standard pathology slides that are already part of routine patient care.

The findings suggest that, with further validation, AI-based tools could one day help clinicians obtain more detailed tumour information to inform patient care, without requiring every patient to undergo advanced genetic testing.

Helping guide treatment decisions

For patients with meningiomas, recurrence risk can influence follow-up care, imaging frequency and whether radiation therapy should be considered. The study found that AI-based predictions remained useful even after accounting for traditional clinical factors such as tumour grade, the extent to which surgery was able to remove the tumour and patient age.

Researchers also found that the AI models could identify patterns of tumour heterogeneity – differences within the same tumour – that may help explain why some tumours behave more aggressively or respond differently to treatment.

The researchers note that additional prospective studies are needed before the AI models can be used routinely in clinical care. Still, they say the findings lay the groundwork for more accessible, personalised care for patients with meningiomas – and potentially for similar AI approaches in other cancers.

As with any clinical decision-support tool, the researchers emphasise that these models would require rigorous evaluation, validation and ongoing physician oversight before being considered for routine care. “The aim is to make these algorithms readily and simply accessible for use globally, improving patient care across many healthcare settings,” says Dr Zadeh.

For a complete list of authors, disclosures and funding, review the publication.

Source: Mayo Clinic

Categories : IT in HealthcareTags :

AI Language Models Struggle with Basic Hospital Data Tasks, Study Finds

On By ModernMedia

Nine leading AI models were tested on simple administrative queries drawn from real-world emergency department records—and most failed unless paired with code-generation tools.

A new study finds that large language models (LLMs), used with straightforward prompting, perform poorly on routine number-crunching tasks that hospital administrators depend on every day to track patients and allocate resources. The findings were published this week in the open-access journal PLOS Digital Health by Eyal Klang of the Icahn School of Medicine at Mount Sinai, New York, USA, and colleagues.

Hospitals rely on structured electronic health record (EHR) data to monitor patient counts and resources and to generate administrative reports. These tasks are currently handled by data analysts using programming languages, creating delays when staff need fast answers. AI tools known as large language models, such as GPT-4o and Llama, have been proposed to simplify that process.

In the new study, researchers evaluated nine leading LLMs on two basic administrative tasks—counting patients meeting a condition and filtering records based on multiple criteria—using data drawn from 50 000 real emergency department visits at the Mount Sinai Health System.

The researchers found that straightforward prompting—asking the model a plain question like “how many patients in this table were admitted?”—produced uniformly poor results across all models. Chain-of-thought reasoning, in which the model is prompted to show step-by-step work before giving an answer, offered only modest improvements that degraded sharply as table size increased. Even GPT-4o, the top-performing model, saw accuracy drop from roughly 95% on the smallest datasets to below 60% on larger ones under chain-of-thought conditions.

A tool-based approach—where models were asked to generate code that was then executed—substantially improved accuracy for the most capable models, with GPT-4o and Qwen-2.5-72B achieving near-perfect performance. However, distilled DeepSeek models, optimised for speed and efficiency, struggled even with this approach. One model, Llama-3.1-8B, failed to produce usable output in the majority of trials and was excluded from further analysis.

“Our findings indicate that without using a tool-based strategy, current LLMs are unsuitable for standalone use even on minimally complex administrative tasks in clinical settings,” says Benjamin Glicksberg. “Structured data tasks in clinical workflows will require agentic approaches that combine LLMs with code execution to ensure accuracy and consistency.”

Provided by PLOS

Categories : IT in HealthcareTags :

Much Medical Information Provided by Popular Chatbots is Inaccurate and Incomplete

On By ModernMedia

Half of answers to evidence based questions “somewhat” or “highly” problematic

A substantial amount of medical information provided by 5 popular chatbots is inaccurate and incomplete, with half of the answers to clear evidence based questions “somewhat” or “highly” problematic, show the results of a study published in the open access journal BMJ Open.

Continued deployment of these chatbots without public education and oversight risks amplifying misinformation, warn the researchers.

Generative AI chatbots have been rapidly adopted across research, education, business, marketing and medicine, with many people using them like search engines, including for everyday health and medical queries, explain the researchers.

To gauge the level of accuracy provided in areas of health and medicine already prone to misinformation, and therefore with consequences for everyday health behaviour, the researchers probed 5 publicly available and popular generative AI chatbots in February 2025: Gemini (Google); DeepSeek (High-Flyer); Meta AI (Meta); ChatGPT (OpenAI); and Grok (xAI).

Each chatbot was prompted with 10 open ended and closed questions in each of 5 categories of cancer, vaccines, stem cells, nutrition, and athletic performance. The prompts were designed to resemble common ‘information-seeking’ health and medical queries and misinformation tropes online and in academic discourse. 

And they were developed to ‘strain’ models towards misinformation or contraindicated advice—a strategy increasingly used for stress testing AI chatbots and picking up behavioural vulnerabilities, note the researchers.

Closed prompts required chatbots to provide pre-defined responses, often with one correct answer, that aligned with the scientific consensus. Open ended prompts typically required chatbots to generate multiple responses in list form.

Responses were categorised as non-, somewhat, or highly problematic, using objective pre-defined criteria. A problematic response was defined as one that could plausibly direct lay users to potentially ineffective treatment or come to harm if followed without professional guidance.

The information was scored for accuracy and completeness, and particular attention was given to whether a chatbot presented a false balance between science and non-science based claims, regardless of the strength of the evidence.

Each response was also graded on readability, ranging from whether it was written in easy, plain English, to difficult, academic language, using the Flesch Reading Ease score.

Half (50%) the responses were problematic: 30% were somewhat, and 20% were highly problematic. 

Prompt type was influential: open-ended prompts, for example, produced 40 highly problematic responses—significantly more than expected—and 51 non-problematic responses—significantly fewer than expected. The opposite was true of closed prompts.

While the quality of responses didn’t differ significantly among the 5 chatbots, Grok
generated significantly more highly problematic responses than would be expected (29/50; 58%). Gemini generated the fewest highly problematic responses and the most non-problematic ones.

The chatbots performed best in the area of vaccines and cancer, and worst in the area of stem cells, athletic performance, and nutrition. 

Answers were consistently expressed with confidence and certainty, with few caveats or disclaimers. Out of the total 250 questions, there were only two refusals to answer, both of which came from Meta AI in response to queries about anabolic steroids and alternative cancer treatments.

Reference quality was poor, with an average completeness score of 40%. Chatbot hallucinations and fabricated citations meant that no chatbot provided a fully accurate reference list. 

All readability scores were graded as ‘difficult’, equivalent in complexity to suitability for a college graduate.

The researchers acknowledge that they only assessed 5 chatbots and that commercial AI is rapidly evolving, so their findings might not be universally applicable. And not all real-world queries are deliberately adversarial, an approach they took which may have overstated the prevalence of problematic content.

Nevertheless, “Our findings regarding scientific accuracy, reference quality, and response readability highlight important behavioural limitations and the need to re-evaluate how AI chatbots are deployed in public-facing health and medical communication,” they point out. 

“By default, chatbots do not access real-time data but instead generate outputs by inferring statistical patterns from their training data and predicting likely word sequences. They do not reason or weigh evidence, nor are they able to make ethical or value-based judgments,” they explain.

“This behavioural limitation means that chatbots can reproduce authoritative-sounding
but potentially flawed responses.” 

The data chatbots draw on also includes Q&A forums and social media, and scientific content is typically limited to open access or publicly available articles, which comprise only 30–50% of published studies. While this enhances conversational fluency, it  may come at the cost of scientific accuracy, advise the researchers.

“As the use of AI chatbots continues to expand, our data highlight a need for public education, professional training, and regulatory oversight to ensure that generative AI supports, rather than erodes, public health,” they conclude.

Source: BMJ Group

Categories : IT in Healthcare Lab Tests and ImagingTags :

Deepfake X-Rays Fool Radiologists and AI

On By ModernMedia

Findings raise concerns about cybersecurity and diagnostic trust

Anatomy-matched real and GPT-4o-generated radiographs: (A) real and (B) GPT-4o-generated posteroanterior chest radiographs, (C) real and (D) GPT-4ogenerated lateral cervical spine radiographs, (E) real and (F) GPT-4o-generated posteroanterior hand radiographs, and (G) real and (H) GPT-4o-generated lateral lumbar spine radiographs. The pairs demonstrate that GPT-4o can produce radiographically plausible images across different anatomic regions.
https://doi.org/10.1148/radiol.252094 ©RSNA 2026

Neither radiologists nor multimodal large language models (LLMs) are able to easily distinguish AI-generated “deepfake” X-ray images from authentic ones, according to a study published in Radiology. The findings highlight the potential risks associated with AI-generated X-ray images, along with the need for tools and training to protect the integrity of medical images and prepare health care professionals to detect deepfakes.

The term “deepfake” refers to a video, photo, image or audio recording that appears real but has been created or manipulated using AI.

“Our study demonstrates that these deepfake X-rays are realistic enough to deceive radiologists, the most highly trained medical image specialists, even when they were aware that AI-generated images were present,” said lead study author Mickael Tordjman, MD, post-doctoral fellow, Icahn School of Medicine at Mount Sinai, New York. “This creates a high-stakes vulnerability for fraudulent litigation if, for example, a fabricated fracture could be indistinguishable from a real one. There is also a significant cybersecurity risk if hackers were to gain access to a hospital’s network and inject synthetic images to manipulate patient diagnoses or cause widespread clinical chaos by undermining the fundamental reliability of the digital medical record.”

Seventeen radiologists from 12 different centers in six countries (United States, France, Germany, Turkey, United Kingdom and United Arab Emirates) participated in the retrospective study. Their professional experience ranged from 0 to 40 years. Half of the 264 X-ray images in the study were authentic, and the other half were generated by AI. Radiologists were evaluated on two distinct image sets, with no overlapping between the datasets. The first dataset included real and ChatGPT-generated images of multiple anatomical regions. The second dataset included chest X-ray images—half authentic and the other half created by RoentGen, an open-source generative AI diffusion model developed by Stanford Medicine researchers.

When radiologist readers were unaware of the study’s true purpose, yet asked after ranking the technical quality of each ChatGPT image if they noticed anything unusual, only 41% spontaneously identified AI-generated images. After being informed that the dataset contained synthetic images, the radiologists’ mean accuracy in differentiating the real and synthetic X-rays was 75%.

Individual radiologist performance in accurately detecting the ChatGPT-generated images ranged from 58% to 92%. Similarly, the accuracy of four multimodal LLMs—GPT-4o (OpenAI), GPT-5 (OpenAI), Gemini 2.5 Pro (Google), and Llama 4 Maverick (Meta)—ranged from 57% to 85%. Even ChatGPT-4o, the model used to create the deepfakes, was unable to accurately detect all of them, though it identified the most by a considerable margin compared to Google and Meta LLMs.

Radiologist accuracy in detecting the RoentGen synthetic chest X-Rays ranged from 62% to 78% and the LLM models’ performance ranged from 52% to 89%.

There was no correlation between a radiologist’s years of experience and their accuracy in detecting synthetic X-ray images. However, musculoskeletal radiologists demonstrated significantly higher accuracy than other radiology subspecialists.

Spotting the Risks in Synthetic Imaging

“Deepfake medical images often look too perfect,” Dr. Tordjman said. “Bones are overly smooth, spines unnaturally straight, lungs overly symmetrical, blood vessel patterns excessively uniform, and fractures appear unusually clean and consistent, often limited to one side of the bone.”

Recommended solutions to clearly distinguish real and fake images and help prevent tampering include implementing advanced digital safeguards, such as invisible watermarks that embed ownership or identity data directly into the images and automatically attaching technologist-linked cryptographic signatures when the images are captured.

“We are potentially only seeing the tip of the iceberg,” Dr. Tordjman said. “The logical next step in this evolution is AI-generation of synthetic 3D images, such as CT and MRI. Establishing educational datasets and detection tools now is critical.”

The study’s authors have published a curated deepfake dataset with interactive quizzes for educational purposes.

For More Information

Access the Radiology study, “The Rise of Deepfake Medical Imaging: Radiologists’ Diagnostic Accuracy in Detecting ChatGPT-generated Radiographs,” and the related editorial, “The Democratization of Deceit: Seeing Is No Longer Believing.”

Source: Radiological Society of North America