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'Patients will need doctors who can stand apart from the machine long enough to know when it is wrong, incomplete, or right for the wrong reason.' 'Patients will need doctors who can stand apart from the machine long enough to know when it is wrong, incomplete, or right for the wrong reason.' What happens when medical students rely on AI - and never develop their own judgment? AI's danger isn't just in experts losing the ability to reason. It's that trainees may never learn how to do so in the first place I n healthcare, there's growing concern over doctors becoming less clinically adept as they increasingly rely on AI tools.

As generative AI captures public attention, a different kind of AI is reshaping drug discovery. Machine learning models are helping to compress decade-long timelines and cracking problems that were previously unsolvable. Designing and developing a new medicine is an expensive, failure-prone scientific challenge. A new drug can take many years to develop, at the cost of a significant investment. And even then, most possible candidates never reach the patient. For biologic medicines, therapies made from engineered proteins rather than synthetic chemistry (which are often used to treat conditions across most major acute and chronic diseases), the complexity is even greater.

The maker of Wegovy and Ozempic, Novo Nordisk, has launched legal action accusing its arch rival Eli Lilly of false advertising in suggesting its weight-loss drugs perform better. The Danish company filed a lawsuit in the US on Tuesday claiming Eli Lilly, which makes Mounjaro and Zepbound, deployed ad campaigns to create the misleading impression that Eli Lilly's medicines are superior. Novo said its rival compared the highest approved doses of its medicines for obesity and type-2 diabetes with lower doses of Novo Nordisk's, while omitting newer, higher-dose options. The BBC has contacted Eli Lilly for comment. The lawsuit comes as Novo and Eli Lilly are locked in battle to dominate the fast-growing weight-loss drug industry, especially in the US, which analysts have estimated could be worth more than $100bn by 2030.

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This is health care's Uber moment. Every knowledge-based profession may one day reach the point when AI outperforms the human experts. In medicine, that day appeared to come in April. A group of primarily Harvard and Stanford researchers announced the results of a study that pitted ChatGPT against hundreds of physicians in a diagnostic obstacle course involving written medical mysteries and information from real-world patients. The bot had won, and the humans weren't entirely happy about it.

Health-wise, recent decades have been dominated by a rise in obesity and associated metabolic disease, significantly affecting the life and health span of Americans. GLP-1s provide the first possibility for a widespread improvement in these issues. Beyond this, it increasingly seems as if they may have wider effects on alcohol and substance abuse and other diseases. Continued understanding of this will reshape how we think about health, food, and beyond. American history is marked by crucibles, from the Industrial Revolution to the atomic age, in which both scientific possibility and human character were subjected to extraordinary pressure.

Traditional Chinese Medicine (TCM), as an effective alternative medicine, has been receiving increasing attention. In recent years, the rapid development of large language models (LLMs) tailored for TCM has highlighted the urgent need for an objective and comprehensive evaluation framework to assess their performance on real-world tasks. However, existing evaluation datasets are limited in scope and primarily text-based, lacking a unified and standardized multimodal question-answering (QA) benchmark. To address this issue, we introduce TCM-Ladder, the first comprehensive multimodal QA dataset specifically designed for evaluating large TCM language models. The dataset covers multiple core disciplines of TCM, including fundamental theory, diagnostics, herbal formulas, internal medicine, surgery, pharmacognosy, and pediatrics.

The outspoken longevity scientist David Sinclair has been predicting that one day, you'll go to the doctor and get a prescription that will make you 10 years younger. Now has learned that he has plans to launch human tests of an oral reprogramming drug as part of a $101 million competition organized by the XPrize Foundation. The foundation is offering cash awards to teams able to "restore" a person to an earlier apparent age, as measured by improvements in immune, cognitive, and muscle function. The grand prize goes to any team able to show a 10-year (or greater) relative improvement after one year of treatment. Reached by phone, Sinclair, a biologist at Harvard Medical School, confirmed that he plans to give an oral drug mixture to volunteers in a bid to seek "evidence for age restoration in humans."