Foundation models for X-ray interpretation: a narrative review of current techniques and future perspectives in diagnostic imaging

作者：Isah Salim Ahmad, Rabiatu Bako Suleiman, Tian Yu, Rongbo Lin, Cailei Zhao, Jianxiang Liao, Benqing Wu, Yaoqin Xie, Xiaokun Liang, Haifeng Wang, Zhanqi Hu · 发表于：Quantitative Imaging in Medicine and Surgery · 年份：2026 · DOI：10.21037/qims-2025-1-2782 · 研究领域：COVID-19 diagnosis using AI、Artificial Intelligence in Healthcare and Education、Advanced Radiotherapy Techniques

Background and Objective: The scarcity of high-quality annotated data is a primary bottleneck in developing artificial intelligence (AI) for chest X-ray (CXR) interpretation. Foundation models (FMs), trained on broad datasets via self-supervision, present a transformative solution. This narrative review analyzes the current state of vision and vision-language foundation models (VLFMs) specifically for CXR, evaluating their potential to bridge research and clinical practice through a novel analytical framework. Methods: Following PRISMA 2020 guidelines, we conducted a narrative review of literature from 2021-2025. We introduced and applied a novel four-pillar analytical taxonomy to structure the field: (I) core model architecture; (II) training framework; (III) clinical application adaptation; and (IV) quality assurance (QA). This framework guided the synthesis of evidence from over 150 studies, enabling a structured analysis of model designs, training paradigms, adaptation strategies, and evaluation metrics. Key Content and Findings: stationary imaging, device/site shifts) are underdeveloped. We identify domain-specific challenges, including bias propagation from limited public datasets, label noise in report-mined supervision, and a significant gap between retrospective benchmark performance and prospective clinical utility. Evidence-based recommendations are provided for model selection, efficient adaptation via parameter-efficient fine-tuning (PEFT), and the implementation...