Brief context
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Original paper
The executive brief below is grounded in the source paper and linked back to the arXiv abstract.
As AI agents become increasingly capable of complex, long-horizon reasoning, rigorous and holistic evaluation is essential for measuring progress toward real-world healthcare applications. We introduce HealthAgentBench, a suite of 54 agentic healthcare tasks across 7 categories each with its unique environment. The benchmark suite spans diverse workflows throughout the patient journey and a broad range of modalities. Each task is designed to replicate an end-to-end clinical workflow: given minimal instructions, an agent must explore raw healthcare data, operate within a complex environment, and execute multi-step solutions that go beyond naive prompting. A final task success rate is reported to provide a single, interpretable metric for HealthAgentBench overall performance for each agent. Evaluating frontier agents on HealthAgentBench, we find that overall task success rate remains low, underscoring the difficulty of the suite. The strongest and the most cost effective agent, Codex GPT-5.5, achieves only approximately 42% success rate. Beyond aggregate performance, HealthAgentBench reveals nuanced strengths and weaknesses across task categories. Frontier agents show promise in automatically developing research modeling pipelines over EHR data, but medical imaging remains especially challenging, particularly for Claude Code models, while Codex GPT-5.5 shows emerging capability. Tasks that combine large search spaces with compositional reasoning requirements remain difficult for all current agents. Together, these results suggest that HealthAgentBench provides a challenging and realistic benchmark with substantial room for future progress. We release our benchmark at https://github.com/microsoft/HealthAgentBench.
Executive brief
A short business-reader brief that explains why the paper matters now and what to watch or do next.
Why this is worth your attention
HealthAgentBench is useful because it moves healthcare-agent evaluation from polished demos to messy, executable work: agents must inspect records, images, tables, and clinical text, then complete multi-step tasks under scoring rules. The business takeaway is sobering but actionable: the best agent reaches only about 42% overall, yet some structured EHR and data-engineering tasks look much closer to supervised automation than imaging or open-ended clinical reasoning.
- The paper’s clearest message is not that healthcare agents are ready; it is that realistic healthcare workflows still break them. A top score around 42%, with imaging far below text-heavy tasks, is a strong reason to demand task-level evidence before buying broad automation claims.
- The nearer-term business opportunity looks like structured EHR work—format conversion, event modeling, and constrained data engineering—where agents can write code, inspect tables, and verify outputs. That points to supervised automation in analytics, data quality, trial ops, and research pipelines before unsupervised clinical decision support.
- For operational workflows, the bottleneck is often not whether the model can recognize a problem, but whether it can find the right place to look across messy enterprise data. Ask vendors what indexing, decomposition, retrieval, and audit mechanisms sit around the model, because those may matter more than the model name.
- The imaging results suggest current agents are improvising around poor perceptual grounding by generating views, crops, and tiles rather than truly operating over medical images natively. Progress here will likely come from better multimodal interfaces, image tooling, and workflow integration—not simply more fluent reasoning text.
- The evidence is stronger than a demo because tasks are executable and scored, but it is still a containerized benchmark with strict pass/fail gates. Those gates are useful for comparison, yet they can understate partial productivity and overstate how cleanly results transfer into live clinical systems.
Evidence ledger
The strongest claims in the brief, along with the confidence and citation depth behind them.
HealthAgentBench is a unified executable benchmark covering 54 healthcare agent tasks across 7 categories.
The best evaluated agent achieves only about 42% mean success across the full suite.
Medical imaging remains a major weakness for current frontier agents relative to text-heavy healthcare tasks.
Search-space navigation and task decomposition are major practical bottlenecks in EHR-style workflows.
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