Can LLMs Score Medical Diagnoses and Clinical Reasoning as well as Expert Panels?
Summary: arXiv:2604.14892v2 Announce Type: replace-cross
Abstract: Evaluating medical AI systems using expert clinician panels is costly and slow, motivating the use of large language models (LLMs) as alternative adjudicators. Here, we evaluate an LLM jury composed of three frontier AI models scoring 3333 diagnoses on 300 real-world middle-income country (MIC) hospital cases. Model performance was benchmarked against expert clinician panel and independent human re-scoring panel evaluations.
Both LLM and clinician-generated diagnoses are scored across four dimensions: diagnosis, differential diagnosis, clinical reasoning, and negative treatment risk. For each of these, we assess:
- Scoring difference
- Inter-rater agreement
- Scoring stability
- Severe safety errors
- The effect of post-hoc calibration
Our findings indicate several important conclusions:
- Systematic Scoring Differences: The uncalibrated LLM jury scores are systematically lower than clinician panel scores.
- Ordinal Agreement: The LLM Jury preserves ordinal agreement and exhibits better concordance with the primary expert panels than the human expert re-score panels do.
- Severe Error Probability: The probability of severe errors is lower in LLM models compared to the human expert re-score panels.
- Expert Panel Ranking Agreement: The LLM Jury shows excellent agreement with primary expert panels’ rankings.
- Error Identification: The LLM jury combined with AI model diagnoses can be used to identify ward diagnoses at high risk of error, enabling targeted expert review and improved panel efficiency.
- No Self-Preference Bias: LLM jury models show no self-preference bias. They did not score diagnoses generated by their own underlying model or models from the same vendor more (or less) favorably than those generated by other models.
- Calibration Improvement: LLM jury calibration using isotonic regression improves alignment with human expert panel evaluations.
Together, these results provide compelling evidence that a calibrated, multi-model LLM jury can serve as a trustworthy and reliable proxy for expert clinician evaluation in medical AI benchmarking. The implications of this study suggest that LLMs could potentially streamline the evaluation process in clinical settings, offering a cost-effective and efficient alternative to traditional expert panels.
As the field of medical AI continues to evolve, the integration of LLMs into diagnostic processes could enhance the accuracy and reliability of medical evaluations, ultimately benefiting patient care and outcomes.