The Shaky Foundations of Clinical Foundation Models: A Survey of Large
Language Models and Foundation Models for EMRs
- URL: http://arxiv.org/abs/2303.12961v2
- Date: Fri, 24 Mar 2023 21:50:03 GMT
- Title: The Shaky Foundations of Clinical Foundation Models: A Survey of Large
Language Models and Foundation Models for EMRs
- Authors: Michael Wornow, Yizhe Xu, Rahul Thapa, Birju Patel, Ethan Steinberg,
Scott Fleming, Michael A. Pfeffer, Jason Fries, Nigam H. Shah
- Abstract summary: We review over 80 foundation models trained on non-imaging EMR data.
We find that most models are trained on small, narrowly-scoped clinical datasets.
We propose an improved evaluation framework for measuring the benefits of clinical foundation models.
- Score: 5.7482228499062975
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The successes of foundation models such as ChatGPT and AlphaFold have 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.
We review over 80 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. In
light of 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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