A newly published Viewpoint article in the journal BioScience argues that the contentious debate over the origins of SARS-CoV-2 could benefit from a well-established scientific approach for weighing competing explanations. Multiple working hypotheses and strong inference, argues author Alan B. Franklin (formerly of the USDA National Wildlife Research Center) could break the logjam of single-hypothesis reasoning that has marked the politically charged accusations that the SARS-CoV-2 originated from either an intentional or unintentional release from the Wuhan Institute of Virology versus from the proximate live animal market.

Large language models (LLMs), including the models behind ChatGPT and Claude as well as numerous other systems developed specifically for medical applications, are increasingly used in clinical workflows. They support medical documentation, summarize knowledge, and assist with clinical decision-making. However, their adoption is outpacing the development of systems for oversight and safety. An interdisciplinary team of researchers at the Else Kröner Fresenius Center (EKFZ) for Digital Health at TUD Dresden University of Technology and University Hospital Dresden, together with national and international colleagues, has systematically analyzed the risks associated with LLM use in medicine. The review, published in Nature, brings together evidence from medical AI, cybersecurity, regulatory science, ethics and behavioral psychology and outlines strategies for trustworthy and responsible use of artificial intelligence (AI) in clinical practice.

A National Institutes of Health (NIH)-funded scientific team has shown that under the right circumstances, 3D cell culture models of human brain tissue, known as organoids, can express features of later stage brain development. This breakthrough could turn previously unattainable, long-term, benchtop studies of neurodevelopmental disorders into a reality.