GRAND-ROUNDS

GRAND-ROUNDS (Graded Responses and Annotated Notes for Diagnostic Reasoning on UNstructured Data Sets) is a physician-annotated benchmark for validating LLM judges of open-ended clinical reasoning. It contains 5,250 scored response entries from 160 clinicians and 9 AI models across six tasks, drawn from seven published studies and graded by 11 physicians. Released with the paper Scaling Clinical Judgment to Evaluate Medical AI. Website: https://preceptron.net · Code: https://github.com/2v/PrecepTron · Models: https://huggingface.co/collections/tbuckley/preceptron-6a3194798cea4c5d6bc9713f

Benchmarks

Benchmark	Description	Records
management_reasoning	Grey Matters management cases (Goh et al. 2025): free-text management answers scored on case-specific rubrics	2,765
bi_triage	BIDMC emergency department cases (Brodeur et al. 2026): differential diagnoses at triage, evaluation, and admission scored with the Bond score	911
cpc_bond	NEJM clinicopathological conference (CPC) cases: differential diagnoses scored with the 0-5 Bond score	853
r_idea	NEJM Healer cases (Cabral et al. 2024): clinical reasoning documentation scored with the 10-point R-IDEA rubric	312
diagnostic_reasoning	Landmark diagnostic cases (Goh et al. 2024): structured diagnostic reasoning scored on the 19-point rubric	278
cpc_management	NEJM CPC cases: proposed diagnostic testing plans scored on the 0-2 testing-plan rubric	131

Common Fields

• benchmark: Source benchmark identifier
• case_id: Case identifier
• model: AI model or human participant group
• study: Source study citation key
• response: Free-text response from model or participant
• grade: JSON string — list of grader scores (grader, score, and benchmark-specific fields)
• final_diagnosis: Ground truth diagnosis (where applicable)

Fields present only in some benchmarks: question_number, question_text, max_score, run_number, participant_id, asked_together, aliquot, cannot_miss_diagnoses, cannot_miss_score, dataset, questions_raw, test_plan, case_vignette_multi.

case_vignette_multi (management_reasoning only) is a JSON string containing an ordered list of aliquots, each {"vignette": "<cumulative case text visible at this point>", "questions": ["q1", ...]}. Use it to recover the incremental-disclosure vignette that was visible when each question was asked; the flat case_vignette field remains the full concatenation of every aliquot for backward compatibility.

Case text availability

Full case text (case_vignette) is included for diagnostic_reasoning and management_reasoning, released here for the first time. The NEJM CPC and NEJM Healer case presentations are copyrighted by the publisher and the BIDMC emergency department cases contain protected patient information, so cpc_bond, cpc_management, r_idea, and bi_triage ship with responses, physician scores, and reference diagnoses only (is_case_released is False on those rows).

Citation

@article{buckley2026preceptron,
  title   = {Scaling Clinical Judgment to Evaluate Medical AI},
  author  = {Buckley, Thomas A. and Kanjee, Zahir and Brodeur, Peter G. and
             Crowe, Byron and Pettinato, Anthony M. and Shah, Aashna P. and
             Haimovich, Adrian D. and McCoy, Liam G. and Restrepo, Daniel and
             Goh, Ethan and Chen, Jonathan H. and Zwaan, Laura and
             Goodman, Katherine E. and Morgan, Daniel J. and
             Abdulnour, Raja-Elie E. and Rodman, Adam and Manrai, Arjun K.},
  year    = {2026},
  note    = {Preprint, forthcoming}
}