Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
September 5, 2025NEJM AIOpen Access

MedAgentBench: A Virtual EHR Environment to Benchmark Medical LLM Agents

View Full Paper
Ask AI
Bookmark
Share

Authors

YJYixing JiangKBKameron Collin BlackGGGloria Geng

Discussion

Loading...

Member takes

Overview

Evaluation suite assesses agent capabilities of LLMs in medical applications, indicating gaps for improvement.

Key Points

  • Current state-of-the-art LLMs achieved a success rate of 69.67% on complex medical tasks, indicating room for improvement.
  • MedAgentBench includes 300 patient-specific tasks across 10 categories, offering a framework for benchmarking agent capabilities.
  • The evaluation suite utilizes standard APIs in modern EHR systems, suggesting easy integration into live environments.
  • Improving AI systems within clinical workflows requires an approach that emphasizes agent-based task frameworks and benchmarks.

Cite This Study

Jiang et al. (2025) studied this question.

synapsesocial.com/papers/68c23922b210217d6477a8b7https://doi.org/10.1056/aidbp2500144
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1BioML-bench: Evaluation of AI Agents for End-to-End Biomedical ML2025
  2. 2MedAgentGym: A Scalable Agentic Training Environment for Code-Centric Reasoning in Biomedical Data Science2025
  3. 3AI Agents in Clinical Medicine: A Systematic Review2025 · 36 citations
  4. 4Large Language Model Agents for Biomedicine: A Comprehensive Review of Methods, Evaluations, Challenges, and Future Directions2025
  5. 5Healthcare agent: eliciting the power of large language models for medical consultation2025 · 13 citations