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

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.

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.

The four-pillar taxonomy provides a critical lens to deconstruct the FM development pipeline. Our analysis reveals distinct maturity levels across pillars: while architectural innovation and training strategies are advanced, rigorous QA and adaptation for real-world heterogeneity (e.g., portable vs. 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 of stratified evaluation.

CXR-FMs offer a path toward more generalizable and data-efficient diagnostic AI. However, successful clinical translation is contingent upon coordinated advances across all four pillars of the proposed framework, moving beyond architectural scaling to prioritize data diversity, robustness auditing, and seamless workflow integration. This taxonomy provides researchers and clinicians with an actionable framework to develop and evaluate models whose impact will be determined by demonstrated fairness, reliability, and utility in diverse clinical environments.