Large language models for science and medicine

Author:

Telenti Amalio12,Auli Michael3,Hie Brian L.34,Maher Cyrus2,Saria Suchi5,Ioannidis John P. A.678910ORCID

Affiliation:

1. Department of Integrative Structural and Computational Biology Scripps Research La Jolla California USA

2. Vir Biotechnology, Inc. San Francisco California USA

3. FAIR, Meta Menlo Park California USA

4. Department of Chemical Engineering Stanford University Stanford California USA

5. Malone Center for Engineering and Healthcare Johns Hopkins University Baltimore Maryland USA

6. Department of Medicine Stanford University Stanford California USA

7. Department of Epidemiology and Population Health Stanford University Stanford California USA

8. Department of Biomedical Data Science Stanford University Stanford California USA

9. Department of Statistics Stanford University Stanford California USA

10. Meta‐Research Innovation Center at Stanford (METRICS) Stanford University Stanford California USA

Abstract

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.

Publisher

Wiley

Reference73 articles.

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