Clinical BenchmarkThe short answer
MedQA and MedMCQA both score medical question answering, but that shared description does not make their percentages interchangeable. A comparison needs a record of the task each system actually received. This guide develops a practical comparison contract: a small set of named conditions that keeps a score attached to its denominator, information sources and output rules.
Start with a result identity
A useful result name is longer than a dataset acronym. For MedQA, specify the language subset, the answer-option variant and the split. Our dossier centers the English USMLE four-option test set. For MedMCQA, identify whether the run uses validation or test and preserve the downloaded file revision. These fields determine what was measured before model quality enters the discussion.
Treat the identity as part of the result table rather than supplementary prose. If one field is missing, label the comparison unresolved. That is more informative than silently assuming that every publication used the most familiar version of a dataset. An unresolved field identifies the next document or artifact a reviewer needs.
Hold the information boundary constant
The original MedQA work combines retrieval with answer selection, and the MedMCQA baseline study varies the supplied context source. Those designs make external information part of the tested system. A model with retrieved material receives a different information environment from a model answering from its parameters alone, even when both eventually return one option letter.
In your comparison contract, record corpus identity, retrieval method, number of passages and whether retrieval can reach benchmark questions or answers. A claim about the overall system can legitimately include retrieval. A claim about the base model needs to acknowledge that another component may explain the observed difference.
Preserve the answer space and parser
The MedQA repository distinguishes a four-option version. Changing option count changes the available distractors, not merely the arithmetic chance baseline. It is tempting to rescale a five-option score to a four-option score, but that calculation cannot reconstruct which distractor was removed or how a particular model used it.
The output parser deserves the same attention. Specify whether the system must emit one letter, whether explanatory text is accepted, and what happens when multiple letters appear. Keep the raw response as well as the parsed prediction. Otherwise a formatting change may look like a reasoning improvement, with no way to inspect the difference.
Separate a score difference from a task difference
Use side-by-side accuracy only after the conditions align. If they do not, name the comparison you actually have: different source populations, different context, or different output constraints. Cross-benchmark results can reveal complementary evidence, but their numerical gap does not estimate how much harder one benchmark is for every system.
A helpful report contains a conditions table beside the scores. Readers can then distinguish a controlled model comparison from a survey of published numbers. Both can be useful. Their conclusions differ because only the controlled comparison holds the relevant measurement choices fixed.
Finish with a bounded conclusion
Write the final sentence around the observed answer-selection task. For example, describe agreement with reference choices under the frozen protocol. Do not substitute a claim about diagnostic practice, patient benefit or autonomous clinical work unless the study actually evaluated that outcome. A well-specified exam run is useful evidence without carrying every possible healthcare claim.
Archive the contract with dataset identifiers, system configuration, prompts, raw predictions and scoring code. The original papers and repositories establish the benchmark definitions; the contract is our editorial tool for making a particular comparison auditable. Another evaluator should be able to identify the same task without guessing from the model name.
References & further reading
These original sources support the methods discussed here. Our suggested planning steps are editorial guidance, not an endorsement by the source authors.
- MedQA: What Disease Does This Patient Have? ↗Di Jin and colleagues. Original MedQA definitions, split statistics and historical retrieval baselines.
- MedQA author repository ↗MedQA authors. Official data access, four-option variant and repository license.
- MedMCQA: A Large-scale Multi-Subject Multi-Choice Dataset ↗Ankit Pal, Logesh Kumar Umapathi and Malaikannan Sankarasubbu. Original exam-based construction, exact split statistics and retrieval comparison.
- MedMCQA author repository ↗MedMCQA authors. Original access and submission instructions. README split-table headings disagree with exact paper statistics.
Published by Arcophos. Educational material, not clinical advice or a claim of regulatory compliance. Read our editorial method.