Foundation models for radiology: fundamentals, applications, opportunities, challenges, risks, and prospects.

Journal: Diagnostic and interventional radiology (Ankara, Turkey)
Published Date: Jul 8, 2025

Abstract

Foundation models (FMs) represent a significant evolution in artificial intelligence (AI), impacting diverse fields. Within radiology, this evolution offers greater adaptability, multimodal integration, and improved generalizability compared with traditional narrow AI. Utilizing large-scale pre-training and efficient fine-tuning, FMs can support diverse applications, including image interpretation, report generation, integrative diagnostics combining imaging with clinical/laboratory data, and synthetic data creation, holding significant promise for advancements in precision medicine. However, clinical translation of FMs faces several substantial challenges. Key concerns include the inherent opacity of model decision-making processes, environmental and social sustainability issues, risks to data privacy, complex ethical considerations, such as bias and fairness, and navigating the uncertainty of regulatory frameworks. Moreover, rigorous validation is essential to address inherent stochasticity and the risk of hallucination. This international collaborative effort provides a comprehensive overview of the fundamentals, applications, opportunities, challenges, and prospects of FMs, aiming to guide their responsible and effective adoption in radiology and healthcare.

Authors

  • Tugba Akinci D'Antonoli
    Department of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel, Basel, Switzerland.
  • Christian Bluethgen
    Stanford Center for Artificial Intelligence in Medicine and Imaging (AIMI), Stanford University, Sheffield, USA.
  • Renato Cuocolo
    Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy.
  • Michail E Klontzas
    Department of Medical Imaging, Heraklion University Hospital, Crete, 70110, Greece; Advanced Hybrid Imaging Systems, Institute of Computer Science, Foundation for Research and Technology (FORTH), N. Plastira 100, Vassilika Vouton 70013, Heraklion, Crete, Greece. Electronic address: miklontzas@ics.forth.gr.
  • Andrea Ponsiglione
    Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via Pansini 5, 80131, Naples, Italy.
  • Burak Kocak
    Department of Radiology, Istanbul Training and Research Hospital, Istanbul, Turkey. drburakkocak@gmail.com.

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