Health
US Researchers Develop New Tool to Predict Disease Risk from Rare Genetic Mutations
A team of scientists in New York has developed a new model designed to help doctors interpret complex genetic test results and provide patients with clearer guidance on their health risks. The research, published in the journal Science, could improve early detection of serious conditions and reduce unnecessary medical treatments.
Genetic testing can identify changes, or variants, in a person’s DNA, but interpreting the results is often difficult. While some variants directly cause disease, many others have little or no effect, leaving doctors and patients uncertain about what the findings mean. The problem is compounded by the fact that most diseases result not from a single mutation, but from the combined influence of multiple genes and environmental factors.
To address this challenge, researchers at the Icahn School of Medicine at Mount Sinai built a model that draws on both genetic information and electronic health records (EHRs), which include lab results and a patient’s medical history. By combining these data sources, the model can calculate the likelihood that an individual with a specific variant will develop conditions such as breast cancer or polycystic kidney disease.
“Traditional genetic tests often leave patients in limbo, because the results don’t always provide a clear answer,” said Professor Ron Do, one of the study’s senior authors. “By using real-world medical data—like cholesterol levels and blood counts that are already part of routine care—we can make far more accurate predictions about disease risk.”
The researchers trained the model on more than one million health records and applied it to patients carrying rare genetic mutations. Each patient was assigned a risk score between zero and one, reflecting the probability of developing a particular condition. In total, the team calculated risk scores for more than 1,600 genetic variants.
In some cases, the tool clarified the significance of variants previously labelled as “uncertain.” For example, the model revealed strong links between specific mutations and known diseases, providing new insights for clinicians.
Dr. Iain Forrest, the study’s lead author, said the tool is intended to support, not replace, doctors. “This model could guide decisions on whether a patient needs further screening, preventive steps, or reassurance that their genetic result poses little risk,” he explained.
The team is now working to expand the model by including a wider range of genetic variants, more diseases, and a more diverse patient population to ensure broader accuracy.
“Ultimately, our work highlights a future where clinical data and genetic information can be combined to give patients more personalised and actionable answers,” Do said.
If widely adopted, the approach could change the way genetic testing is used in medicine—helping patients avoid unnecessary interventions while ensuring those at higher risk receive timely care.
Health
Study Finds AI Systems Can Repeat Fake Medical Claims When Framed Credibly
“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.
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.
Health
Moderate Caffeine Intake Linked to Lower Dementia Risk, Study Finds
Health
Growing Research Links Tattoos to Possible Cancer Risks, Experts Say
Tattoos are more popular than ever, but a growing body of research suggests a connection between permanent ink and certain types of cancer. How concerned should the public be?
From tribal sleeves to lower-back butterflies, humans have been inking their skin for thousands of years. For most, the main concern has been the fear of future regrets. However, recent studies suggest that tattoos could carry more serious long-term health risks.
The popularity of tattoos has risen sharply in recent years. Research published in the European Journal of Public Health estimates that between 13 and 21 percent of people in Western Europe now have at least one tattoo. Despite this prevalence, relatively little is known about the potential long-term effects of permanent ink.
Previous studies have shown that tattoo pigments can accumulate in the lymph nodes, sometimes causing inflammation and, in rare cases, lymphoma—a type of blood cancer. A 2025 study by the University of Southern Denmark (SDU) expanded on this, reporting that individuals with tattoos may face higher risks of skin cancer and lymphoma. Using a cohort of randomly selected twins, the researchers found that tattooed participants had nearly four times the risk of skin cancer compared with their non-tattooed siblings.
The study also suggested that tattoo size could affect risk, with designs larger than the palm associated with higher hazard rates.
“We have evidence that there is an association [between the amount of ink and risk] for lymphoma and for skin cancer,” said Signe Bedsted Clemmensen, co-author of the study and assistant professor of biostatistics at SDU. “For lymphoma, the hazard rate is 2.7 times higher, so this is quite a lot. And for skin cancers, before it was 1.6 and now it’s 2.4. This indicates that the more ink you have, the higher the risk, the higher the hazard rate.”
Clemmensen emphasized that these findings remain preliminary, with many variables—including ink types, tattoo placement, and genetic and environmental factors—still under investigation. “The bottom line is, more research is needed,” she said. “But also, the next step I think is studying the biological mechanisms [of getting tattooed] and trying to understand what happens there.”
Experts also note other risks unrelated to cancer. Tattoo inks consist of pigments combined with a carrier fluid to deposit color into the dermis. Some inks, often imported, can contain trace amounts of heavy metals such as nickel, chromium, cobalt, and lead, which can trigger allergic reactions or immune sensitivity. In 2022, the European Union restricted more than 4,000 hazardous substances in tattoo inks under its REACH regulations.
While tattoos are generally considered safe when applied hygienically, the long-term health consequences remain uncertain. “It’s up to each of us how we choose to live our lives, right? But as a researcher, it’s also my job to inform people of these risks,” Clemmensen said. “Or, when it comes to tattooing, right now it’s more about informing people about how little we know.”
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