ArticleJournal of imaging informatics in medicine2026
How Robust Are Radiology Teaching-Case Benchmarks for Multimodal Large Language Models? An Audit of Exposure Opportunity, Inference Configuration, and Text-Only Solvability Across Eighteen Models.
Article in Journal of imaging informatics in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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6 authors.
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Abstract
Multimodal large language models (MLLMs) are benchmarked on radiology multiple-choice questions. We audited how training-data exposure opportunity, image resolution, inference configuration, and text-only solvability affected interpretation of a teaching-case benchmark. Eighteen MLLMs and 2 original reference readers answered 260 questions from 51 AuntMinnie cases; 5 additional readers completed them under a unified interface. Analyses included Holm-adjusted McNemar tests, case-cluster bootstrapping, model-cutoff matching, a case-random-intercept logistic model, cross-cohort configuration re-runs, and text-only ablation. Model accuracy ranged from 61.5% to 80.8%, and the 7 readers ranged from 66.5% to 84.2%. No model exceeded all readers; 17 fell within the reader range and 1 below. Eight models were significantly less accurate than Reader 1 after reader-specific Holm adjustment (6 when all 36 comparisons were adjusted together); none differed significantly from Reader 2. A cohort/publication-period association remained after item adjustment (odds ratio, 4.67; 95% Laplace interval, 1.82-11.98), but 3 within-cohort cutoff comparisons showed no exposure-consistent advantage. Resolution changes were within 3 percentage points in both cohorts, and the configuration-by-version interaction was not significant. Among 5 post hoc selected models, mean image contribution was 6.2 percentage points in the original cohort and 4.0 in the supplementary cohort. The benchmark did not support a single human-model ranking, a training-exposure explanation for the cohort difference, or a configuration-dependent GPT version effect. Public teaching-case benchmarks should report provenance, individual-reader calibration, multiplicity control, inference settings, and image-free performance and should not be interpreted as evidence of clinical diagnostic readiness.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.