ChatHealthAI: Aligning Electronic Health Record Representations with Large Language Models for Grounded Clinical Reasoning
ChatHealthAI is a multimodal reasoning framework that aligns structured EHR representations from a pretrained EHR foundation model with the semantic space of a frozen LLM via a task-aware resampler. It integrates longitudinal patient data with refined clinical event descriptions to enable interpretable natural-language reasoning while maintaining predictive performance. Evaluated on three tasks from the EHRSHOT benchmark, ChatHealthAI improves reasoning quality and interpretability without sacrificing accuracy.
[2606.02802] ChatHealthAI: Aligning Electronic Health Record Representations with Large Language Models for Grounded Clinical Reasoning
[Submitted on 1 Jun 2026]
Title:ChatHealthAI: Aligning Electronic Health Record Representations with Large Language Models for Grounded Clinical Reasoning
View a PDF of the paper titled ChatHealthAI: Aligning Electronic Health Record Representations with Large Language Models for Grounded Clinical Reasoning, by Bo-Hong Wang and 5 other authors
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Abstract:Large language models (LLMs) exhibit strong natural-language reasoning abilities for clinical decision support, but struggle to effectively model structured longitudinal electronic health records (EHRs). In contrast, EHR foundation models can learn predictive patient representations, yet lack interpretable language-based reasoning. To bridge this gap, we propose ChatHealthAI, a multimodal reasoning framework that aligns structured EHR representations from a pretrained EHR foundation model with the semantic space of a frozen LLM through a task-aware resampler. By integrating longitudinal patient representations with refined clinical event descriptions, ChatHealthAI enables clinically grounded natural-language reasoning while maintaining accurate patient prediction. We evaluated ChatHealthAI on three clinical predictive tasks from the EHRSHOT benchmark. Results show that ChatHealthAI improves reasoning quality and interpretability while preserving competitive predictive performance. These findings highlight the potential of integrating EHR foundation models with pretrained LLMs for interpretable clinical prediction.
Comments: Main paper with appendix, 13 pages
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.02802 [cs.AI]
(or arXiv:2606.02802v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.02802
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Jun Bai [view email] [v1] Mon, 1 Jun 2026 19:21:18 UTC (3,938 KB)
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