Large language models for science and medicine

Review

doi: 10.1111/eci.14183.

Online ahead of print.

Affiliations

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.

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Review

Amalio Telenti et al.

Eur J Clin Invest.

2024.

doi: 10.1111/eci.14183.

Online ahead of print.

Affiliations

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.

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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.

Keywords:

applications; large language models; limitations; medicine; science; validation.

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References

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