The shaky foundations of large language models and foundation models for electronic health records
The success of foundation models such as ChatGPT and AlphaFold has spurred significant interest in building similar models for electronic medical records (EMRs) to improve patient care and hospital operations. However, recent hype has obscured critical gaps in our understanding of these models’ capa...
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Published in | NPJ digital medicine Vol. 6; no. 1; pp. 135 - 10 |
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Main Authors | , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
London
Nature Publishing Group UK
29.07.2023
Nature Publishing Group Nature Portfolio |
Subjects | |
Online Access | Get full text |
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Summary: | The success of foundation models such as ChatGPT and AlphaFold has spurred significant interest in building similar models for electronic medical records (EMRs) to improve patient care and hospital operations. However, recent hype has obscured critical gaps in our understanding of these models’ capabilities. In this narrative review, we examine 84 foundation models trained on non-imaging EMR data (i.e., clinical text and/or structured data) and create a taxonomy delineating their architectures, training data, and potential use cases. We find that most models are trained on small, narrowly-scoped clinical datasets (e.g., MIMIC-III) or broad, public biomedical corpora (e.g., PubMed) and are evaluated on tasks that do not provide meaningful insights on their usefulness to health systems. Considering these findings, we propose an improved evaluation framework for measuring the benefits of clinical foundation models that is more closely grounded to metrics that matter in healthcare. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 ObjectType-Review-3 content type line 23 |
ISSN: | 2398-6352 2398-6352 |
DOI: | 10.1038/s41746-023-00879-8 |