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Performance vs Consistency: Evaluating a Foundation Model in Lung-RADS Screening
Foundation models have recently demonstrated strong capabilities across a wide range of medical imaging tasks. However, their performance in structured clinical interpretation settings remains insufficiently explored. In lung cancer screening, interpretative variability persists despite standardized frameworks such as Lung-RADS. In this study, we evaluate MedGemma, a medical general-purpose foundation model derived from Gemini and its fine-tuned version adapted for lung cancer detection and diagnosis, compared against radiologists performing Lung-RADS v2022 assessment on the NLST dataset. Twel
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-13T02:43:46.000Z
First collected: 2026-09-24T12:12:29.144Z. This is not the publication date.