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Study Finds AI Systems Can Repeat Fake Medical Claims When Framed Credibly

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4 months ago

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“Large language models accept fake medical claims if presented as realistic in medical notes and social media discussions, a study has found.”

As more people turn to the internet to research symptoms, compare treatments and share personal health experiences, artificial intelligence tools are increasingly being used to answer medical questions. A new study warns that many of these systems remain vulnerable to medical misinformation, particularly when false claims are presented in authoritative or realistic language.

The findings, published in The Lancet Digital Health, show that leading artificial intelligence systems can mistakenly repeat incorrect medical information when it appears in formats that resemble professional healthcare documents or trusted online discussions. Researchers analysed how large language models respond when faced with false medical statements written in a credible tone.

The study examined responses from 20 widely used language models, including systems developed by OpenAI, Meta, Google, Microsoft, Alibaba and Mistral AI, as well as several models specifically fine-tuned for medical use. In total, researchers assessed more than one million prompts designed to test whether AI would accept or reject fabricated health information.

Fake statements were inserted into real hospital discharge notes, drawn from common health myths shared on Reddit, or embedded in simulated clinical scenarios written to resemble authentic healthcare guidance. Across all models tested, incorrect information was accepted around 32 percent of the time. Performance varied significantly, with smaller or less advanced models accepting false claims in more than 60 percent of cases, while more advanced systems, including ChatGPT-4o, did so in roughly 10 percent of responses.

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The researchers also found that medical fine-tuned models performed worse than general-purpose systems, raising concerns about tools designed specifically for healthcare use.

“Our findings show that current AI systems can treat confident medical language as true by default, even when it’s clearly wrong,” said Eyal Klang of the Icahn School of Medicine at Mount Sinai, one of the study’s senior authors. He added that how a claim is written often matters more to the model than whether it is accurate.

Some of the accepted misinformation could pose real risks to patients. Several models endorsed claims such as Tylenol causing autism during pregnancy, rectal garlic boosting immunity, mammograms causing cancer, and tomatoes thinning blood as effectively as prescription medication. In another case, a discharge note incorrectly advised patients with oesophageal bleeding to drink cold milk, which some models repeated without flagging safety concerns.

The study also tested how AI systems responded to flawed arguments known as fallacies. While many fallacies prompted scepticism, models were more likely to accept false claims framed as expert opinions or warnings of catastrophic outcomes.

Researchers say future work should focus on measuring how often AI systems pass on falsehoods before they are used in clinical settings. Mahmud Omar, the study’s first author, said the dataset could help developers and hospitals stress-test AI tools and track improvements over time.

The authors said stronger safeguards will be essential as AI becomes more deeply embedded in healthcare decision-making.

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World Cup Emotion Can Strain the Heart, Cardiologists Warn Fans at Risk

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2 days ago

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June 13, 2026

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As the World Cup begins, medical experts are cautioning football fans with underlying heart conditions that the emotional intensity of matches can place unexpected strain on the cardiovascular system.

Cardiologists say that the excitement, tension, and anxiety generated during high-stakes games can trigger physical reactions similar to intense exercise, raising heart rate, blood pressure, and stress hormones.

“Intense emotions, whether positive or negative, can act as ‘precipitating risk factors’ for cardiovascular events such as heart attack,” said Paola Santalucia, a cardiologist and board member of the European Heart Network.

She explained that moments of extreme excitement, such as a decisive penalty shootout or a last-minute goal, may pose risks for people already living with heart disease. Those with additional risk factors, including hypertension, obesity, or smoking habits, are also advised to be cautious during emotionally charged matches.

Research using wearable devices has shown that during major football events, some fans experience heart rates climbing as high as 150 beats per minute. That level is comparable to sprinting and reflects how strongly the body reacts to emotional stress.

A study examining supporters during the 2025 German Cup final found that even watching from home can significantly affect physiological responses. “They still had an increase in heart rate that compares to walking, even though they didn’t walk,” said Christian Deutscher, professor of sports economics at Bielefeld University and co-author of the study.

He noted that the most intense reactions often occur not during goals themselves, but during moments of uncertainty such as VAR checks, penalty shootouts, or shots striking the post. These unpredictable situations, he said, are what drive the strongest emotional and physical responses among fans.

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Deutscher also pointed out that stadium spectators may experience even greater strain due to environmental factors such as heat and alcohol consumption.

However, experts emphasize that football itself is not inherently dangerous. Instead, it is the body’s natural response to excitement that can create temporary stress.

“The adrenergic stimulation is at its max: extreme high blood pressure, high heart rate, and adrenaline, cortisol, skyrocketing,” said Dan Atar, professor of cardiology at Oslo University Hospital. In rare cases, he added, this surge can contribute to the rupture of arterial plaque in vulnerable individuals, potentially leading to a heart attack.

Atar stressed that such events can occur in everyday situations as well, including physical exertion like shoveling snow. “It is in no way dangerous to watch a football game,” he said. “All this is physiologic. It’s not dangerous to be excited.”

Still, he acknowledged that combining emotional stress with alcohol, heat, and pre-existing conditions can increase risk for some viewers.

Doctors advise those at higher risk to continue prescribed medications, limit alcohol intake, avoid smoking, and watch for warning signs such as chest pain or irregular heartbeat.

“The key message is not to avoid enjoying the match, but to do so with moderation and awareness,” Santalucia said.

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AI Models Show Ability to Mimic Human Emotions, Offering New Pathways for Mental Health Research

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4 days ago

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June 11, 2026

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Artificial intelligence systems may be able to imitate human emotional responses in controlled settings, potentially opening new directions for mental health research, according to a recent study from Dresden University of Technology in Germany.

The findings come at a time when mental health conditions are rising globally, with projections suggesting that up to 1.2 billion people could be affected by 2050. Researchers say this growing challenge highlights the need for improved understanding of psychological disorders and more effective treatment approaches, particularly in talk-based therapies that are difficult to model through traditional methods.

Unlike drug development, which can rely on biological testing, psychotherapy research faces limitations because neither animal models nor human trials can fully capture the complexity of emotional and cognitive processes. Scientists involved in the study argue that large language models (LLMs) may help bridge part of this gap.

“Our results show that large language models can reproduce patterns of human affective and cognitive processes under controlled conditions,” said Dr Magdalena Wekenborg, who leads the PsychoDigital Research group at TU Dresden. She added that such systems could support efforts to better understand underlying psychological mechanisms and help explore new forms of psychotherapy research.

The study examined whether LLMs could replicate emotional states such as fear, anxiety, anger, sadness, disgust, worry, and stress when prompted. Researchers then tested whether those induced states could be altered using different emotional regulation techniques, and whether emotional prompting would lead the models to make errors similar to those seen in humans experiencing the same feelings.

Findings showed that while artificial intelligence systems do not experience emotions in a human sense, they are capable of reproducing certain patterns of emotional reasoning through language processing. This allows researchers to observe behaviour that resembles human cognitive responses under structured conditions.

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The team also noted that such models offer a level of experimental control that is not possible in human or animal studies. According to researcher Jakob N. Kather, experiments can be repeated under identical conditions and adjusted systematically, allowing for more precise comparisons.

He said this could enable new data-driven approaches in psychological and biomedical research, particularly in areas where ethical or practical constraints have limited traditional experimentation.

While the study does not suggest that artificial intelligence understands emotion as humans do, it highlights how language models may serve as useful tools for exploring aspects of mental health and human cognition in ways that were previously out of reach.

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AI Saves Clinicians Weeks of Work but Health Systems Struggle to Keep Up, Philips Report Finds

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5 days ago

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June 10, 2026

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Healthcare professionals are gaining significant time savings through artificial intelligence tools, but health systems are struggling to adapt quickly enough to the rapid pace of change, according to a new global report.

The findings come from the Future Health Index 2026 report published by health technology company Philips, which examined how AI is being used across hospitals and clinics and its impact on clinical workflows.

The study surveyed more than 2,000 clinicians and over 20,000 patients across 10 countries, including the United Kingdom, United States, Germany, France, China and India. It found that AI adoption among healthcare workers has increased significantly over the past year, with growing confidence in its ability to improve patient care.

More than 80% of healthcare professionals said they are optimistic about AI’s impact on patient outcomes, while around 70% believe the benefits already outweigh the risks. Many clinicians reported that AI is already making a measurable difference in their daily work.

According to the report, 46% of clinicians said they save at least 132 hours per year through AI-enabled tools, equivalent to more than three working weeks. Nurses were among those reporting the greatest time savings, particularly from reduced administrative workloads.

Shez Partovi, Chief Innovation Officer at Philips, said clinicians are increasingly able to redirect that time toward patient care, collaboration and reflection on complex medical cases. He noted improvements in work-life balance, reduced stress and greater efficiency across clinical teams.

Around 71% of respondents said AI has improved workflow efficiency, while half said it has allowed them to see more patients. Approximately the same proportion reported better work-life balance and lower stress levels.

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Common uses of AI include transcribing medical notes, scheduling appointments and assisting with diagnostic tasks such as reviewing X-rays. Some clinicians also described using AI systems as support tools for clinical discussions and decision-making, including identifying potential drug interactions.

The report also found that 39% of clinicians had seen AI help identify or prevent potential medical errors multiple times in recent months, while more than 65% said it had improved their confidence in clinical decisions.

Despite these gains, the report highlights growing pressure on health systems to keep pace with demand for AI tools. Nearly two-thirds of clinicians said they turn to personal AI applications when workplace systems are insufficient, raising concerns about governance and data security.

Seven in 10 respondents said training for AI tools is limited or inconsistent, suggesting organisations are struggling to implement structured adoption programmes. Partovi said this reflects a gap between rapid technological advancement and slower institutional rollout.

He added that hospitals face complex challenges including privacy, safety, regulatory oversight and role-specific training, all of which must be addressed to ensure safe deployment.

Looking ahead, 96% of healthcare professionals expect AI to change their roles, with more than half anticipating major shifts in how they work. However, concerns remain, with 44% worried about losing clinical skills due to over-reliance on AI and 37% saying changes are happening faster than they are comfortable with.

Even so, most clinicians emphasised that human oversight remains essential. Around 86% said AI outputs must always be reviewed by healthcare professionals, while more than 80% said technology will not replace the patient-clinician relationship.

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