ArticleJournal of imaging informatics in medicine2026
Do Accuracy Gains Reflect Genuine Visual Understanding? A Multi-Model Evaluation of Vision-Language Models in Thoracic Imaging.
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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Abstract
Diagnostic accuracy of vision-language models (VLMs) on radiology benchmarks is rising rapidly, but whether higher multiple-choice accuracy reflects genuine visual understanding, or merely the selection of a plausible option from a closed set, remains unclear. We retrospectively evaluated 13 VLMs, comprising paired latest and preceding versions from five proprietary families (GPT, Gemini, Claude, Grok, Mistral), one standalone, and two open-weights models, on 100 challenging thoracic cases (chest radiograph, CT, MR, PET) reformatted as single-best-answer multiple-choice questions, under image-input (primary) and description-input (non-visual) conditions, against 10 readers including three thoracic radiologists. For the top-performing family, all rationales accompanying correct and incorrect answers were graded for reasoning errors (hallucination, missing critical finding, wrong detail) by two thoracic radiologists. Under image-input, Gemini 3.0 Pro achieved the highest Top-1 accuracy (57.0%), close in aggregate to the thoracic-reader mean (57.3%), and four of five latest proprietary models gained 16.0 to 19.6 points over their predecessors (all p < 0.01). However, only 55.7% of Gemini 3.0 Pro's rationales were free of errors: among correct answers, 80.7% were soundly reasoned, leaving nearly one in five correct diagnoses resting on hallucinated, missing, or incorrect findings; among incorrect answers, only 22.5% reflected sound reasoning, and 36.4% contained hallucinations. Across all 13 models, stated confidence systematically exceeded observed accuracy (ECE, 18.2 points), with the gap widening at higher confidence. Description-input raised accuracy further (Gemini 3.0 Pro, 79.7%) but relied on diagnosis-aware descriptions unavailable in practice. Even at the highest accuracy observed here, answer-level performance can overstate visual understanding, pointing to the need for more appropriate evaluation methods that assess whether a model's reasoning is faithful to the image, not only whether its answer is correct.
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