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 towards 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-world evaluation and assessment of FMs (REAL-FM), a multi-dimensional framework assessing data, technical readiness, clinical value, workflow integration and responsible artificial intelligence. Using REAL-FM, we find that although FMs excel in pattern recognition they fall short on causal reasoning, domain robustness and safety. Clinical translation is hindered by scarce representative data for model training, unverified generalization beyond over-simplified benchmark settings and a lack of prospective outcome-based validation. This Perspective provides clinicians with a practical way to interpret FM claims, identify where these systems may safely support imaging workflows and recognize why human oversight remains indispensable. For developers, it defines the validation, workflow, safety and governance requirements that must be met before FMs can become clinically reliable tools. 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.
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Subjects
- Translational research
- Medical imaging
- Preclinical research
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This research was partially supported by Cancer Prevention and Research Institute of Texas grant RP240117. The fundingterpretation or manuscript preparation
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Authors and Affiliations
Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
Amgad Muneer, Kai Zhang, Ibraheem Hamdi, Muhammad Waqas & Jia Wu
Pediatric Surgical Research Laboratories, Massachusetts General Hospital, Boston, MA, USA
Rizwan Qureshi
Department of Computer Science, Salim Habib University, Karachi, Pakistan
Rizwan Qureshi
School of Computer Science and Digital Technologies, Aston Centre for Artificial Intelligence Research and Application, Aston University, Birmingham, UK
Shereen Fouad
School of Computing Data and Mathematical Sciences, University of Stirling, Stirling, UK
Hazrat Ali
School of Medicine and Health Sciences, George Washington University, Washington, DC, USA
Syed Muhammad Anwar
Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Hospital, Washington, DC, USA
Syed Muhammad Anwar
Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
Jia Wu
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Contributions
A.M., K.Z., R.Q., S.M.A. and J.W. conceived of the scope and central thesis of this Perspective. A.M. led the conceptual development of the manuscript, including the critical analysis of FMs, taxonomy of reasoning and discussion of causality, trustworthiness and deployment challenges in biomedical imaging. A.M., K.Z., I.H., R.Q. and S.M.A. contributed to the evaluation of current FM paradigms, limitations and emerging trends across imaging modalities. A.M., S.F., H.A., S.M.A. and M.W. provided domain expertise on clinical relevance, validation practices and translational considerations. J.W. supervised the overall direction of the work and provided strategic guidance on clinical and methodological framing. All authors contributed to the writing, critical revision and intellectual refinement of the manuscript and approved the final version for publication.
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REAL-FM scoring rubric and FM evaluation workbook, including criterion-level scores, reviewer agreement statistics, adjudicated final scores and written justifications
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Muneer, A., Zhang, K., Hamdi, I. et al. Foundation models in biomedical imaging: turning hype into reality.
Nat. Biomed. Eng10, 1557–1575 (2026). https://doi.org/10.1038/s41551-026-01762-z
Received:16 January 2026
Accepted:03 July 2026
Published:11 August 2026
Version of record:11 August 2026
Issue date:August 2026
DOI
:https://doi.org/10.1038/s41551-026-01762-z
