Aug 2026· Journal of imaging informatics in medicine· 0 citations· 18 references
Medicine
TL;DR
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.
VLMs, particularly Gemini-3, exhibit zero-shot capabilities in identifying condylar changes and generating high-quality diagnostic reports, and demonstrate potential as preliminary, training-free screening aids in oral maxillofacial radiology.
Ke Chen, Andrew Zhang, Xian-Ju Xie et al.· International Dental Journal· 1 citation
The results suggest medical vision encoders carry a map of where organs usually lie, and little of the machinery for comparing structures within a particular patient, in addition to five architectural configurations and three medical foundation models, frozen and finetuned.
Vision-language models (VLMs) are increasingly applied to medical imaging, yet public benchmarks may reward memorization over perception: their images and questions can enter pretraining corpora, and many items remain answerable from question text alone. We present an automated, agent-driven pipeline that builds multip...
Bo Liu, Han Gu, Xiang-Rui Li et al.· Research Square· 0 citations
EyeVQA is introduced, a unified visual question answering benchmark for comprehensive evaluation of ophthalmic VLMs, and fourteen representative general-purpose, scientific, and medically specialized VLMs under a unified zero-shot protocol are benchmarked.
Gu-Jie Shao, Zi-Xun Xie, Xue-Chun Xing et al.· 0 citations
An evidence-grounded selective evaluation benchmark that evaluates Pathology classification and Abnormality identification together with label-aware lesion localization suggests that answer accuracy and abstention behavior can substantially overstate the reliability of current VLM when predictions are not verified agai...
B. Qu, W. Liu, M. Murrow et al.· medRxiv· 0 citations
These findings support continued benchmarking and cautious, specialist-supervised use of multimodal LLMs in retinal education and structured imaging question answering and indicate that retinal image interpretation remains a major limitation.
Xiao-Chen Gu, Yi-Zhou Yang, Xuan-Qiao Lin et al.· Frontiers in Medicine· 0 citations
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