A team of researchers at Stanford University has released MedagentbenchA new benchmark suit designed to evaluate large language model (LLM) agents in healthcare contexts. Unlike pre-questioning dataset, Medagentbench offers one Virtual Electronic Health Record (EHR) Environment Where the AI system should interact, plan and execute multi-step clinical functions. It marks a significant change from testing of static argument to assess the abilities of the agent. Live, tool-based medical workflows,
Why do we need agentic benchmark in healthcare?
Recently gone beyond stable chat-based conversation towards LLM Agentic behavior-Explaining high-level instructions, calling APIs, integrating patient data and automating complex processes. In medicine, it can help in development address Staff shortage, documentation burden and administrative disabilities,
While general-purpose agent benchmarks (eg, agentbench, agentboard, tau-bench) are present, Healthcare lacks a standardized benchmark It catches the medical data, FHIR interoperability and complication of the longitudinal patient records. Medagentbench fills this difference by offering a copyable, clinically relevant assessment structure.
What’s in Medagentbench?
How is the tasks structured?
Near Medagentbench 300 tasks in 10 categoriesWritten by licensed physicians. These functions include patient information recover, laboratory result tracking, documentation, testing order, referral and drug management. Work average 2-3 stages and mirror workflows faced in in -topic and outpatient care.
Does patient data support benchmark?
Takes advantage of benchmark 100 realistic patient profiles Stanford’s star data was extracted from repository, including 700,000 records Lab, vital, diagnosis, procedures and drug orders. Data was de-identified and nervous for privacy, preserving clinical validity.
How is the environment created?
Environment is AgainSupporting both Get and Amendment of EHR data. AI systems can follow realistic clinical interactions such as documentation of Vital or ordering the drug. This design makes the benchmark directly translated to live the EHR system.
How is the model evaluated?
- Metric: Task success rate (SR), measured with strict Pass@1 To reflect the security requirements of the real world.
- Tested models,
- Agent orchestrator: A baseline orchestation setup with nine FHIR functions, limited to limited Eight interaction round per work,
Which models performed the best?
- Cloud 3.5 sonnet V2: Best overall 69.67% successStrong (85.33%) especially in recovery functions.
- GPT-4o: 64.0% success shows success, balanced recovery and action performance.
- Deepsek-V3: 62.67% success, leading among open weight models.
- Overview: Most models performed excellent Querry work But struggled Action-based task Safe multi-step execution is required.
What errors did the model make?
Two major failure patterns emerged:
- Instructions failure failure – Rejuvenated API calls or wrong JSON formatting.
- Output Missmach – When structured numerical values were required, providing full sentences.
These errors highlight intervals Accurate and reliabilityBoth are important in clinical deployment.
Summary
Medagentbench establishes a massive benchmark first to evaluate LLM agents in realistic EHR settings, with 300 physician-operated tasks with FHIR-analog environment and 100 patient profiles. The results show strong potential but limited reliability – Clade 3.5 Sonnet V2 leads to 69.67% – Querry promotes the difference between success and safe action execution. Forced by solo-institution data and EHR-focused scope, the Medagentbench provided an open, copy presented structure to the next generation to run reliable healthcare AI agents.
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Michal Sutter is a data science professional, with Master of Science in Data Science from the University of Padova. With a concrete foundation in statistical analysis, machine learning, and data engineering, Mishhala excelled when converting the complex dataset into actionable insights.
