ChatHealthAI: Aligning Electronic Health Record Representations with Large Language Models for Grounded Clinical Reasoning
ChatHealthAI is a proposed multimodal reasoning framework that aligns electronic health record (EHR) representations with large language models (LLMs) for improved clinical reasoning. The framework integrates structured patient data with natural language processing to enhance interpretability and predictive performance in clinical decision support. Evaluation results indicate that ChatHealthAI successfully improves reasoning quality while maintaining competitive accuracy in patient predictions.
- ▪ChatHealthAI aims to bridge the gap between EHR foundation models and large language models.
- ▪The framework utilizes a task-aware resampler to align structured EHR data with LLMs.
- ▪Evaluation on the EHRSHOT benchmark shows improved reasoning quality and interpretability.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2606.02802 |
| Publication time | Wed, 03 Jun 2026 00:00:00 -0400 |
| Retrieval time | 2026-06-03T04:11:55.408Z |
| Last seen | 2026-06-03T04:11:55.408Z |
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Computer Science > Artificial Intelligence arXiv:2606.02802 (cs) [Submitted on 1 Jun 2026] Title:ChatHealthAI: Aligning Electronic Health Record Representations with Large Language Models for Grounded Clinical Reasoning Authors:Bo-Hong Wang, Baicheng Peng, Ruilin Wang, Jun Bai, Ziyang Song, Yue Li 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 View PDF HTML (experimental) 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).
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