Charlotte Deane, co-leader of the OpenBind initiative, discusses the shortage of protein–small molecule binding data that limits AI drug discovery. She argues that filling this data gap is needed to improve computational models. OpenBind aims to generate more of this binding data for the field.
Scientists are warning that autonomous AI agents may scoop their research after two recent cases. OpenAI announced on 8 September that its agents had solved the Navier–Stokes problem one day after mathematician Tristan Buckmaster posted progress on it, and Anthropic reported that its Claude LLM agents found CRISPR-like repeated DNA patterns in viruses that PhD student Mario Rodríguez Mestre had been studying unpublished for years. Both companies denied training on user data, and researchers including Sandra Laurentino and Samuel Mehr are now restricting what they upload to commercial AI tools.
Polina Brangel, research-and-development data-innovation lead at the Coalition for Epidemic Preparedness Innovations (CEPI) in Oslo, is using AI to prepare for Disease X, the WHO term for a hypothetical pandemic pathogen. CEPI's programme works across the 25 known families of virus that infect humans, using simulations and AI to predict mutations and design vaccine candidates. She is also developing CEPI's Pandemic Preparedness Engine, a chatbot-like tool that would let researchers input a pathogen's genomic sequence and receive suggestions for vaccine antigens.
MAGIC is an international network, described in Nature Medicine, for evaluating generative artificial intelligence in global health. Its contributors are based at institutions including Duke-NUS Medical School, Weill Cornell Medicine, the University of Tokyo, the WHO Regional Office for South-East Asia, the International Telecommunication Union, Harvard Medical School and the University of Ghana Medical School. The effort spans biomedical data science, computational biology, clinical medicine and health policy across Asia, Africa, Europe and North America.
Why it matters: A new international network sets out how generative AI will be evaluated for use in global health.
Nature Medicine published on 23 September 2026 a piece on scaling a deep learning clinical tool from a single hospital to more than a million screened patients across India, Thailand and Australia. The three distinct settings yielded cross-cutting lessons on deployment. The authors suggest these insights can inform wider global expansion of healthcare artificial intelligence.
Why it matters: Documents real-world scaling evidence for clinical AI across three countries and over a million patients.
A Comment in Nature Medicine outlines the Nordic AI-Health Initiative platform, built on large-scale longitudinal and multimodal health data sets from across the Nordic region. The platform is designed to provide secure, regulation-compliant access to these data assets and to deliver generalizable models for responsible AI-driven discovery in medicine. The authors lay out technical foundations and a roadmap for deploying the infrastructure region-wide.