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: medical knowledge retrieval, which retrieves the latest and most comprehensive medical knowledge snippets from the web, and data retrieval, 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.