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.
Authors
- Carl Yang (ORCID: https://orcid.org/0000-0001-9145-4531)
- Ran Xu (ORCID: https://orcid.org/0000-0002-2189-0386)
- Wenqi Shi (ORCID: https://orcid.org/0000-0001-8972-7342)
- Jonathan Wang (ORCID: https://orcid.org/0009-0005-1100-6031)
- Jasmine Zhou
Institutions
- Emory University (US)
- Southwestern Medical Center (US)
- Southwestern Medical Center (US)
- The University of Texas Southwestern Medical Center (US)
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
Funders
- National Science Foundation
- Emory University
- National Institutes of Health
- National Institute of Diabetes and Digestive and Kidney Diseases