Application of Foundation Models in Emergency and Critical Care: A Scoping Review

Abstract

<title>Abstract</title> Emergency and critical care (ECC) settings demand rapid decision-making with emergent conditions, dynamic patient trajectories, and diagnostic uncertainty. Foundation models (FMs), large neural networks pretrained on extensive datasets using self-supervised learning, show promise for diverse clinical tasks in these high-acuity environments. However, no prior work has comprehensively reviewed FM applications in ECC or the barriers limiting their implementation. Following PRISMA-ScR guidelines, we identified 49 eligible studies. Most focused on language models, with comparatively limited exploration of multimodal architectures. FMs utilized diverse data modalities, including free text, tabular records, time-series signals, and imaging, supporting tasks such as outcome prediction, diagnosis, information extraction, and text generation. Despite promising applications such as triage, discharge instructions, and disease diagnosis, current evidence is predominantly retrospective, with minimal external validation or prospective testing. Also, many FMs were trained on internet text that may be misaligned with medical reasoning, introducing safety and ethical risks, and highlighting the need for clinically supervised deployment. FMs have yet to demonstrate benefit in the ECC context; none of the included studies had real-world model deployment or improvements in clinical outcomes. Future research should prioritize the development of multimodal FMs with multicenter, temporally robust validation and prospective trials that emphasize safety, equity, and clinician trust.

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