Foundation Models in Biomedical Imaging: Turning Hype into Reality

TL;DR

Foundation models are transforming biomedical imaging by enabling multi-task systems, but face challenges like data scarcity, interpretability, and clinical validation, requiring careful evaluation and specialized AI systems.

Contribution

The paper introduces REAL-FM, a comprehensive framework for assessing foundation models' readiness and impact in real-world biomedical clinical settings.

Findings

FMs excel in pattern recognition but lack causal reasoning and robustness.

Clinical translation is limited by scarce representative data and lack of prospective validation.

Evaluation reveals gaps between benchmark success and clinical applicability.

Abstract

Foundation models (FMs) are driving a prominent shift in biomedical imaging from task-specific models to unified backbone models for diverse tasks. This opens an avenue to integrate imaging, pathology, clinical records, and genomics data into a composite system. However, this vision contrasts sharply with modern medicine's trajectory toward more granular sub-specialization. This tension, coupled with data scarcity, domain heterogeneity, and limited interpretability, creates a gap between benchmark success and real-world clinical value. We argue that the immediate role of FMs lies in augmenting, not replacing, clinical expertise. To separate hype from reality, we introduce REAL-FM (Real-world Evaluation and Assessment of Foundation Models), a multi-dimensional framework for assessing data, technical readiness, clinical value, workflow integration, and responsible AI. Using REAL-FM, we find that while FMs excel in pattern recognition, they fall short in causal reasoning, domain robustness, and safety. Clinical translation is hindered by scarce representative data for model training, unverified generalization beyond oversimplified benchmark settings, and a lack of prospective outcome-based validation. We further examine FM reasoning paradigms, including sequential logic, spatial understanding, and symbolic domain knowledge. We envision that the path forward lies not in a monolithic medical oracle, but in coordinated subspecialist AI systems that are transparent, safe, and clinically grounded.