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