• Amalio Telenti, Michael Auli, Brian L Hie, Cyrus Maher, Suchi Saria, and IoannidisJohn P AJPA0000-0003-3118-6859Department of Medicine, Stanford University, Stanford, California, USA.Department of Epidemiology and Population Health, Stanford University, Stanford, California, USA.Department of Biomedical Data Science, Stanford .
• Department of Integrative Structural and Computational Biology, Scripps Research, La Jolla, California, USA.
• Eur. J. Clin. Invest. 2024 Jun 1; 54 (6): e14183e14183.

AbstractLarge language models (LLMs) are a type of machine learning model that learn statistical patterns over text, such as predicting the next words in a sequence of text. Both general purpose and task-specific LLMs have demonstrated potential across diverse applications. Science and medicine have many data types that are highly suitable for LLMs, such as scientific texts (publications, patents and textbooks), electronic medical records, large databases of DNA and protein sequences and chemical compounds. Carefully validated systems that can understand and reason across all these modalities may maximize benefits. Despite the inevitable limitations and caveats of any new technology and some uncertainties specific to LLMs, LLMs have the potential to be transformative in science and medicine.© 2024 Stichting European Society for Clinical Investigation Journal Foundation. Published by John Wiley & Sons Ltd.

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