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New benchmark reveals mixed clinical utility for EHR foundation models

By PulseAugur Editorial · [1 sources] ·

A new benchmark called FoMoH has been developed to evaluate foundation models (FMs) for structured electronic health records (EHRs). This benchmark includes 14 clinically meaningful prediction tasks and was tested on over 6 million patient records from Columbia University Irving Medical Center and MIMIC-IV. The evaluation found that while top-performing FMs outperform traditional models in discriminative performance, especially with limited labeled data, they may underperform in low-prevalence settings and exhibit lower calibration. Cross-institutional transportability also remains a challenge for these EHR FMs. AI

IMPACT Highlights limitations in current EHR foundation models, guiding future research towards improved calibration and cross-institutional applicability.

RANK_REASON The cluster contains an academic paper introducing a new benchmark for evaluating foundation models in a specific domain (EHRs). [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New benchmark reveals mixed clinical utility for EHR foundation models

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The cluster contains an academic paper introducing a new benchmark for evaluating foundation models in a specific domain (EHRs). [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Vincent Jeanselme, Zilin Jing, Aparajita Kashyap, Chao Pang, Florent Pollet, Young Sang Choi, Xinzhuo Jiang, Yuta Kobayashi, Yanwei Li, Sara Matijevic, Karthik Natarajan, Shalmali Joshi ·

    FoMoH: A clinically meaningful foundation model evaluation for structured electronic health records

    arXiv:2505.16941v4 Announce Type: replace-cross Abstract: Foundation models (FMs) promise to address core limitations of traditional supervised machine learning: (i) reliance on large amounts of labeled data, (ii) task specificity, and (iii) poor transportability. Despite methodo…

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