aceLarge Language Model Performance and Clinical Reasoning Tasks
Importance: Large language models (LLMs) are increasingly marketed for clinical use, yet their ability to replicate full-spectrum clinical reasoning remains uncertain. Existing evaluations often rely on multiple-choice examinations that do not reflect the complexity of patient care. Objectives: To evaluate the longitudinal clinical reasoning ability of state-of-the-art LLMs and to introduce a multidimensional, clinically meaningful benchmark for clinical-grade artificial intelligence (AI). Design, Setting, and P…
Across 29 standardized clinical vignettes, all 21 tested LLMs failed differential diagnosis in over 80% of cases, indicating they have not achieved the reasoning needed for safe clinical deployment.
Findings are limited to standardized vignettes scored by medical students rather than real-world patient care, constraining generalizability to clinical deployment.
Evidence
- Peer-reviewedJAMA Network Open2026-04-13
How should this claim be treated?
Truvace Impact Record TRV-2026-0145, v1: “Large Language Model Performance and Clinical Reasoning Tasks.” Truvace, 2026-07-13. /record/TRV-2026-0145 (accessed at citation time). sha256 1a034dc67fab0fd2…
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