Healthcare Agent

The green agent evaluates AI models on 10 clinical reasoning tasks from Stanford MedAgentBench, testing capabilities in patient data queries, vital signs recording, laboratory analysis, medication management, and consultation ordering across standardized medical scenarios. The project also examines AI's ability to distinguish real pharmaceuticals from fabricated drug names, as explored in research titled "Drug or Pokemon?" This dual focus assesses both clinical workflow automation and AI safety in medical decision-making contexts.

The green agent evaluates whether a medical AI (purple agent) can correctly perform FHIR-based clinical reasoning tasks. These tasks fall into three categories: Query tasks: Retrieve and compute patient information from the FHIR server, such as identifying patients, calculating age, and extracting recent or averaged lab values. Write tasks: Create valid FHIR resources, including vital sign observations and consultation or lab service requests, with correct clinical structure and content. Conditional (protocol-driven) tasks: Apply clinical decision logic based on patient data (e.g., electrolyte levels or test recency) and, when criteria are met, generate appropriate medication orders or lab requests according to predefined medical protocols. Overall, the green agent checks data retrieval accuracy, clinical calculations, correct use of FHIR APIs, and adherence to clinical protocols, validating each task with task-specific grading logic.

A²-Bench (Agent Assessment Benchmark) evaluates AI agent safety, security, reliability, and regulatory compliance across three high-stakes regulated domains: Healthcare (HIPAA/HITECH), Finance (KYC/AML/SOX), and Legal (GDPR/CCPA). Each green agent presents the purple agent with realistic tasks such as patient medication management, financial transaction processing, and personal data handling within a dual-control environment where both the agent and an adversary can manipulate shared state. Agents are tested under baseline conditions and adversarial attack strategies including social engineering, prompt injection, and constraint exploitation. Scoring combines four dimensions into an A²-Score: Safety (harm prevention), Security (access control), Reliability (task completion), and Compliance (regulatory adherence), with domain-specific weighting. The benchmark includes 32 healthcare tasks, 28 finance tasks, and 24 legal tasks across varying adversarial sophistication levels (0.3–0.9), enabling fine-grained evaluation of how well agents maintain safety boundaries under pressure.

FHIR Agent Evaluator FHIR Agent Evaluator is a benchmark for evaluating medical LLM agents on realistic clinical tasks using FHIR (Fast Healthcare Interoperability Resources) data from MIMIC-IV-FHIR. It follows the Agent-to-Agent (A2A) protocol and evaluates agents operating in tool-augmented EHR environments. The benchmark combines and extends tasks from existing medical agent benchmarks and introduces novel evaluations: Retrieval tasks (1,335 tasks) from FHIR-AgentBench, covering patient record querying, temporal reasoning, and multi-step information gathering across FHIR resources Retrieval+Action tasks (156 tasks) adapted from MedAgentBench, including vitals recording, medication ordering with dosing protocols, referral ordering with SBAR documentation, and conditional laboratory ordering Drug interaction tasks (30 tasks) introducing medication conflict detection using FDA drug label data Agents interact with the environment via tools for FHIR GET/POST requests, medical code lookup, Python code execution, and FDA drug label access. Agents are evaluated using answer correctness (overall task correctness combining response and action validation), action correctness (FHIR POST validation), and F1 score (harmonic mean of retrieval precision and recall).

It includes 12 BioNLP benchmarks across six applications (for a complete BIO agent): > Question Answering : MedQA (USMLE-style), PubMedQA > Named Entity Recognition : BC5CDR Chemical, NCBI Disease > Multi-label Classification : LitCovid, Hallmarks of Cancer > Relation Extraction : ChemProt, DDI (Drug-Drug Interactions) > Text Simplification : PLOS, Cochrane PLS > Summarization : PubMed (dynamic)