GAPS: A Clinically Grounded, Automated Benchmark for Evaluating AI Clinicians

• URL: http://arxiv.org/abs/2510.13734v1
• Date: Wed, 15 Oct 2025 16:40:28 GMT
• Title: GAPS: A Clinically Grounded, Automated Benchmark for Evaluating AI Clinicians
• Abstract summary: Current benchmarks for AI clinician systems fail to capture the depth, robustness, and safety required for real-world clinical practice.<n>We introduce the GAPS framework, a multidimensional paradigm for evaluating textbfGrounding (cognitive depth), textbfAdequacy (answer completeness), textbfPerturbation (robustness), and textbfSafety.<n>We develop a fully automated, guideline-anchored pipeline to construct a GAPS-aligned benchmark end-to-end.
• Score: 32.33432636089606
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current benchmarks for AI clinician systems, often based on multiple-choice exams or manual rubrics, fail to capture the depth, robustness, and safety required for real-world clinical practice. To address this, we introduce the GAPS framework, a multidimensional paradigm for evaluating \textbf{G}rounding (cognitive depth), \textbf{A}dequacy (answer completeness), \textbf{P}erturbation (robustness), and \textbf{S}afety. Critically, we developed a fully automated, guideline-anchored pipeline to construct a GAPS-aligned benchmark end-to-end, overcoming the scalability and subjectivity limitations of prior work. Our pipeline assembles an evidence neighborhood, creates dual graph and tree representations, and automatically generates questions across G-levels. Rubrics are synthesized by a DeepResearch agent that mimics GRADE-consistent, PICO-driven evidence review in a ReAct loop. Scoring is performed by an ensemble of large language model (LLM) judges. Validation confirmed our automated questions are high-quality and align with clinician judgment. Evaluating state-of-the-art models on the benchmark revealed key failure modes: performance degrades sharply with increased reasoning depth (G-axis), models struggle with answer completeness (A-axis), and they are highly vulnerable to adversarial perturbations (P-axis) as well as certain safety issues (S-axis). This automated, clinically-grounded approach provides a reproducible and scalable method for rigorously evaluating AI clinician systems and guiding their development toward safer, more reliable clinical practice.

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