Foundation Models in Biomedical Imaging: Turning Hype into Reality
Amgad Muneer, Kai Zhang, Ibraheem Hamdi, Rizwan Qureshi, Muhammad Waqas, Shereen Fouad, Hazrat Ali, Syed Muhammad Anwar, Jia Wu
Why It Matters
What makes this one worth your time
Understanding the practical limitations and potential of foundation models in biomedical imaging is crucial for their effective integration into clinical workflows.
The paper introduces a framework to evaluate the real-world applicability of foundation models in biomedical imaging.
Summary
The paper discusses the potential and challenges of using foundation models in biomedical imaging, proposing a framework called REAL-FM to evaluate their real-world applicability and clinical value.
Key contributions
- Introduction of the REAL-FM framework for evaluating foundation models.
- Analysis of the limitations of foundation models in clinical translation.
- Discussion on the need for coordinated subspecialist AI systems.
Notable insights
- Foundation models excel in pattern recognition but struggle with causal reasoning and domain robustness.
- The REAL-FM framework assesses multiple dimensions including data readiness and clinical value.
Possible limitations
- Not stated in the abstract
Abstract
arXiv:2512.15808v2 Announce Type: replace-cross 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.