A unified multi-modal foundation model for end-to-end emergency care.

Authors

Zhenwei Huang, Yunfeng Xu, Zixuan Nie, Huiyu Wang, Xiaodong Huang, Ling Huang, Jianhuang Lai, Xiaotu Xi, Li Li, Changdong Wang, Peiyuan Lai, Cai Wen, Tao Yu

Published in

NPJ digital medicine. Jul 14, 2026. Epub Jul 14, 2026.

Abstract

Against the backdrop of population aging and the rising burden of chronic diseases in China, emergency departments (EDs) in tertiary hospitals remain persistently overcrowded. ED overcrowding is a multifactorial phenomenon arising from the interplay of input, throughput, and output factors along the emergency care workflow. Existing artificial intelligence approaches for emergency care are predominantly task-specific. Although effective for isolated clinical tasks, they lack patient-centric, end-to-end integration and generalize poorly under heterogeneous and incomplete inputs, limiting scalable deployment in resource-heterogeneous real-world settings. Here we propose ED-Foundation, a unified emergency foundation model built on the BEIT-3 architecture. By jointly leveraging aligned image-text pairs and unaligned pure-text data, ED-Foundation learns continuous patient-centric representations within a single model, and a two-stage self-supervised learning framework further improves robustness to pervasive missingness. We evaluate ED-Foundation on nine downstream validation datasets spanning three core emergency scenarios that address bottlenecks at different points along the emergency care workflow: early emergency triage, outcome prediction during the clinical course, and clinical decision-making support in the emergency treatment phase. ED-Foundation consistently achieves state-of-the-art retrospective performance and maintains robustness under limited information and modality missingness, outperforming prior task-specific approaches and existing foundation models across diverse institutional and cross-system evaluation settings. These results provide retrospective evidence supporting the benchmark performance and representational transferability of ED-Foundation as a unified representation learning framework for emergency care tasks.

PMID:
42448807
Bibliographic data and abstract were imported from PubMed on 15 Jul 2026.

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