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Yes: an AI model can produce a disease-related prediction while its attention overlay points somewhere different from the area a radiologist would identify. An IIIT Hyderabad study audited chest X-ray overlays and found that the model with the best box-overlap result was not the one radiologists rated highest. The distinction matters: a plausible highlight is not, by itself, proof that a model located disease as a radiologist would.
What the IIIT Hyderabad study examined
The Language Technologies Research Centre team at IIIT Hyderabad, led by Prof. Parameswari Krishnamurthy with Dr. Syed Faizan as principal investigator, asked whether vision-language models’ attention overlays on chest X-rays match regions radiologists would identify as disease locations. The institution reports the study under the title “How Well Do Chest X-Ray VLM Attention Overlays Match Radiologist Boxes? A Cross-Model Audit and Radiologist Reader Study.” IIIT Hyderabad’s account describes an audit of four models on thousands of publicly available chest X-rays, followed by a reader study in which two radiologists assessed anonymized overlays. Hyderabad Mail also reports that the audit used three public datasets, but the reports do not identify those datasets or give an exact image count. Hyderabad Mail’s coverage
- MAIRA-2
- MedGemma-4B
- LLaVA-Med-1.5
- LLaVA-1.5
This is a study of localization overlays on chest X-rays, not a general evaluation of whether medical AI can diagnose disease. The reports do not establish that the researchers tested patient outcomes, prospective clinical use, or clinical safety.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsA disease prediction and an attention overlay answer different questions
A model’s diagnostic output is its prediction about what may be present in an image. An attention overlay is a visual representation of image regions associated with a model’s processing or output. Whether that display matches a radiologist’s disease-location annotation is a separate question from whether the model’s prediction is correct.
#1 Best Overall
As Dr. Faizan put it in a quotation reproduced by IIIT Hyderabad: “An AI model may appear to highlight the correct part of an image, but that does not necessarily mean it has identified the disease in the same way a radiologist would.” The institutional account presents this as an explanation of the study’s question, not as a claim that overlays reveal a model’s complete reasoning.
Overlap scores and radiologist ratings gave different rankings
The reported overlap audit ranked MAIRA-2 ahead of the other models, with MedGemma next and the two LLaVA models behind. But in the reader study, the radiologists rated MedGemma higher than MAIRA-2. IIIT Hyderabad and Hyderabad Mail report this difference.
Rank #2
These results reflect two distinct ways of judging an overlay:
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- Box overlap: how closely an overlay aligns with reference boxes drawn around a relevant area.
- Reader assessment: how radiologists judge the overlay when they inspect it.
A tightly focused highlight can align well with a reference box, while a broader surrounding region may help a reader assess the extent of a finding. The institution offers this as one reason the radiologists’ assessment may differ from the overlap ranking. The results therefore do not support a single, universal model ranking across both measures.
Removing diagnostic information reduced reported localization performance
The institutional report says localization performance fell when diagnostic information was removed. The researchers interpreted that result as a reason to question whether apparently image-based localization may partly rely on anatomical expectations associated with a diagnosis. The report and independent coverage describe the finding, but do not explain precisely how diagnostic information was removed or provide an effect size. It should not be treated as proof of a specific internal mechanism.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the findings do—and do not—show
The results are a caution against treating a visually convincing heatmap or a strong overlap score as conclusive evidence that an AI system has localized disease in the same way a radiologist has. The study summary raises questions about the relationship between overlays and radiologist judgments; it does not establish that the overlays are faithful explanations of the models’ reasoning or that they improve clinical decisions.
The accessible accounts are institutional and secondary news reports, rather than the full paper. They do not provide the dataset names, exact sample counts, overlap metric, confidence intervals, per-model numerical results, overlay-generation details, or the reader-study protocol. IIIT Hyderabad reports acceptance at MICCAI 2026’s iMIMIC satellite event; proceedings and DOI details are not established in the reports cited here. See the institutional account and Telangana Today’s report for the event announcement.
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