Explainable opportunistic osteoporosis screening from chest X-rays: a retrospective comparison of foundation models.

ABSTRACT: We evaluated foundation models for opportunistic osteoporosis screening from chest X-rays using a novel explainability framework. DINOv2 with low-rank adaptation achieved the best performance (AUC 0.93) while demonstrating clear clinical reasoning. Our findings highlight that explainability should be prioritized alongside accuracy in medical AI, enhancing trust in clinical deployment.

Purpose

Deep learning models show promise for opportunistic osteoporosis screening from chest X-rays but have traditionally relied on convolutional neural networks with limited explainability. This study introduces a quantitative framework for explainability evaluation and systematically compares diverse foundation models to identify an optimal balance between performance and explainability.

Methods

SUBMITTER: Kim J 

PROVIDER: S-EPMC12847081 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature