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The successes of foundation models such as ChatGPT and AlphaFold have spurredsignificant interest in building similar models for electronic medical records(EMRs) to improve patient care and hospital operations. However, recent hypehas 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 theirarchitectures, training data, and potential use cases. We find that most modelsare trained on small, narrowly-scoped clinical datasets (e.g. MIMIC-III) orbroad, public biomedical corpora (e.g. PubMed) and are evaluated on tasks thatdo not provide meaningful insights on their usefulness to health systems. Inlight of these findings, we propose an improved evaluation framework formeasuring the benefits of clinical foundation models that is more closelygrounded to metrics that matter in healthcare.

The Shaky Foundations of Clinical Foundation Models: A Survey of Large Language Models and Foundation Models for EMRs