Performance vs Consistency: Evaluating a Foundation Model in Lung-RADS Screening

Abstract: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. Twelve radiologists independently evaluated each case in a multi-reader design, enabling quantification of inter-reader variability. Radiologists achieved a mean AUC of 0.90, with substantial variability across readers (range: 0.80-0.94). The native foundation model achieved an AUC of 0.70, failing to reach clinically relevant performance. In contrast, fine-tuning significantly improved performance to an AUC of 0.83, placing the model within the lower range of individual radiologists performance. These findings highlight a trade-off between peak accuracy and prediction consistency. Unlike radiologists, under fixed conditions, the model produces deterministic outputs, removing inter-run variability under identical inputs, in contrast to inter-reader variability observed among radiologists. This supports the role of fine-tuned foundation models potential complementary tools for clinical decision support, particularly in settings with limited expertise. However, evaluation is performed on a case-enriched cohort from NLST and does not account for real-world prevalence or external validation, limiting direct clinical generalization.

Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)

J.3; I.4.6; I.4.7; I.4.8; I.4.9; I.4.10; I.4.m; I.2.10; I.5.4

arXiv:2609.22281 [cs.CV]

(or arXiv:2609.22281v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2609.22281 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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