Stanford Researchers Introduced MedAgentBench: A Real-World Benchmark for Healthcare AI Agents
A workforce of Stanford University researchers have launched MedAgentBench, a brand new benchmark suite designed to guage giant language mannequin (LLM) brokers in healthcare contexts. Unlike prior question-answering datasets, MedAgentBench supplies a digital digital well being report (EHR) atmosphere the place AI methods should work together, plan, and execute multi-step medical duties. This marks a major shift from testing static reasoning to assessing agentic capabilities in reside, tool-based medical workflows.
Why Do We Need Agentic Benchmarks in Healthcare?
Recent LLMs have moved past static chat-based interactions towards agentic habits—decoding high-level directions, calling APIs, integrating affected person information, and automating advanced processes. In medication, this evolution might assist tackle workers shortages, documentation burden, and administrative inefficiencies.
While general-purpose agent benchmarks (e.g., AgentBench, AgentBoard, tau-bench) exist, healthcare lacked a standardized benchmark that captures the complexity of medical information, FHIR interoperability, and longitudinal affected person data. MedAgentBench fills this hole by providing a reproducible, clinically related analysis framework.
What Does MedAgentBench Contain?
How Are the Tasks Structured?
MedAgentBench consists of 300 duties throughout 10 classes, written by licensed physicians. These duties embrace affected person data retrieval, lab end result monitoring, documentation, check ordering, referrals, and medicine administration. Tasks common 2–3 steps and mirror workflows encountered in inpatient and outpatient care.
What Patient Data Supports the Benchmark?
The benchmark leverages 100 practical affected person profiles extracted from Stanford’s STARR information repository, comprising over 700,000 data together with labs, vitals, diagnoses, procedures, and medicine orders. Data was de-identified and jittered for privateness whereas preserving medical validity.
How Is the Environment Built?
The atmosphere is FHIR-compliant, supporting each retrieval (GET) and modification (POST) of EHR information. AI methods can simulate practical medical interactions reminiscent of documenting vitals or inserting medicine orders. This design makes the benchmark instantly translatable to reside EHR methods.
How Are Models Evaluated?
- Metric: Task success price (SR), measured with strict go@1 to mirror real-world security necessities.
- Models Tested: 12 main LLMs together with GPT-4o, Claude 3.5 Sonnet, Gemini 2.0, DeepSeek-V3, Qwen2.5, and Llama 3.3.
- Agent Orchestrator: A baseline orchestration setup with 9 FHIR capabilities, restricted to eight interplay rounds per job.
Which Models Performed Best?
- Claude 3.5 Sonnet v2: Best total with 69.67% success, particularly sturdy in retrieval duties (85.33%).
- GPT-4o: 64.0% success, exhibiting balanced retrieval and motion efficiency.
- DeepSeek-V3: 62.67% success, main amongst open-weight fashions.
- Observation: Most fashions excelled at question duties however struggled with action-based duties requiring protected multi-step execution.
What Errors Did Models Make?
Two dominant failure patterns emerged:
- Instruction adherence failures — invalid API calls or incorrect JSON formatting.
- Output mismatch — offering full sentences when structured numerical values had been required.
These errors spotlight gaps in precision and reliability, each important in medical deployment.
Summary
MedAgentBench establishes the primary large-scale benchmark for evaluating LLM brokers in practical EHR settings, pairing 300 clinician-authored duties with a FHIR-compliant atmosphere and 100 affected person profiles. Results present sturdy potential however restricted reliability—Claude 3.5 Sonnet v2 leads at 69.67%—highlighting the hole between question success and protected motion execution. While constrained by single-institution information and EHR-focused scope, MedAgentBench supplies an open, reproducible framework to drive the subsequent technology of reliable healthcare AI brokers
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