Large language models for science and medicine.

Journal: European journal of clinical investigation
PMID: 38381530

Abstract

Large 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.

Authors

  • Amalio Telenti
    J. Craig Venter InstituteLa Jolla, CAUnited States.
  • Michael Auli
    FAIR, Meta, Menlo Park, California, USA.
  • Brian L Hie
    Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
  • Cyrus Maher
    Vir Biotechnology, Inc., San Francisco, California, USA.
  • Suchi Saria
    Department of Computer Science, Johns Hopkins University, Baltimore, MD.
  • John P A Ioannidis
    Stanford Prevention Research Center, Department of Medicine, Stanford University, Stanford, California.

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