MedAssist: LLM-Empowered Medical Assistant for Assisting the Scrutinization and Comprehension of Electronic Health Records

Efficiently comprehending diagnosis and treatment plans remains a significant challenge for both medical professionals and patients, particularly when dealing with rare or newly emerging diseases and specific combinations of comorbidities. We present MedAssist, a large language model (LLM)-empowered medical assistant designed to support the scrutinization and comprehension of electronic health records (EHRs). MedAssist leverages two key components: <i>medical knowledge retrieval</i>, which retrieves the latest and most comprehensive medical knowledge snippets from the web, and <i>data retrieval</i>, which extracts diagnosis and treatment plans for similar patients from existing EHR databases. By integrating these capabilities into user-friendly interfaces, MedAssist bridges critical gaps in medical knowledge accessibility and understanding, and advances patient care in realistic clinical scenarios.

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Publication Details

Published
2025-05-08
DOI
https://doi.org/10.1145/3701716.3715186
Citations
2
Primary Topic
Electronic Health Records Systems
Type
article
Field-Weighted Citation Impact
5.88

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article

MedAssist: LLM-Empowered Medical Assistant for Assisting the Scrutinization and Comprehension of Electronic Health Records

Carl Yang, Ran Xu, Wenqi Shi, Jonathan Wang et al.
2 citations
Electronic Health Records Systems
5.88
article

MedAssist: LLM-Empowered Medical Assistant for Assisting the Scrutinization and Comprehension of Electronic Health Records

Carl Yang, Ran Xu, Wenqi Shi, Jonathan Wang, Jasmine Zhou
article en
2 citations

Abstract

Efficiently comprehending diagnosis and treatment plans remains a significant challenge for both medical professionals and patients, particularly when dealing with rare or newly emerging diseases and specific combinations of comorbidities. We present MedAssist, a large language model (LLM)-empowered medical assistant designed to support the scrutinization and comprehension of electronic health records (EHRs). MedAssist leverages two key components: <i>medical knowledge retrieval</i>, which retrieves the latest and most comprehensive medical knowledge snippets from the web, and <i>data retrieval</i>, which extracts diagnosis and treatment plans for similar patients from existing EHR databases. By integrating these capabilities into user-friendly interfaces, MedAssist bridges critical gaps in medical knowledge accessibility and understanding, and advances patient care in realistic clinical scenarios.

Vol. 2025(Companion)
Emory University (US), Southwestern Medical Center (US), Southwestern Medical Center (US), The University of Texas Southwestern Medical Center (US)
National Science Foundation, Emory University, National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases
Openalex Percentile: Top 4%
Electronic Health Records Systems
5.88
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MedAssist: LLM-Empowered Medical Assistant for Assisting the Scrutinization and Comprehension of Electronic Health Records — Carl Yang, Ran Xu, et al. · (2025) | TGRS Research Map | TGRS