score

Beyond the Leaderboard: Rethinking Medical Benchmarks for Large Language Models — Ma et al. (2025) (arXiv:2508.04325, 2025)

What this evaluates

This framework audits medical LLM benchmarks across five lifecycle phases using 46 medically tailored criteria to assess clinical relevance, data integrity, safety-critical capabilities, validity, and governance.

Datasets

• (no dataset; pure metric skill)

Metrics

• score (primary) — range: percent
  • Count of satisfied criteria out of 46 total across five phases (Design, Dataset Construction, Technical Implementation, Benchmark Validity, Documentation/Governance), reported as a percentage.

Input / output format

Input: Documentation and design details of a medical LLM benchmark to be audited.

Output: Binary or categorical assessment for each of the 46 MedCheck criteria.

Scoring recipe

def compute_score(benchmark):
    satisfied = sum(1 for c in criteria if benchmark.meets(c))
    return (satisfied / 46) * 100

Common pitfalls

• Scoring relies on qualitative expert judgment rather than automated computation, risking subjectivity.
• The score evaluates benchmark design quality, not model inference performance, so it cannot replace standard accuracy metrics.

Evidence (verbatim from paper)

This phase is the most underdeveloped in our analysis (average score: 52.4%), revealing a profound gap between current practice and the requirements for reliable medical AI.

Citation

@misc{ma2025beyond,
  title={Beyond the Leaderboard: Rethinking Medical Benchmarks for Large Language Models},
  author={Ma et al. (2025)},
  year={2025},
  note={arXiv:2508.04325}
}

• arXiv: 2508.04325