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

Clinical Snapshot

Key Findings

• P Value: Not applicable for narrative review

• Effect Size: Not quantified - qualitative assessment only

• Primary Outcome: Foundation models offer enhanced adaptability, multimodal integration, and improved generalizability compared to traditional narrow AI in radiology

• Nnt Or Sensitivity: Not reported - review focuses on conceptual framework rather than diagnostic performance metrics

• Confidence Interval: Not applicable for narrative review

Clinical Application

Implementation challenges significant, requiring substantial infrastructure, regulatory compliance, and validation studies Relevant to Australian radiology practice, though TGA regulatory pathways for AI medical devices still evolving. RANZCR and ACRRM guidelines would need updating to incorporate foundation model considerations Radiology departments considering AI implementation, particularly in academic and large healthcare systems

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.

References

1. 1.Akinci D'Antonoli, T., Bluethgen, C., Cuocolo, R., Klontzas, M. E., Ponsiglione, A., & Kocak, B. (2026). Foundation models for radiology: fundamentals, applications, opportunities, challenges, risks, and prospects. Diagnostic and Interventional Radiology, Article 253445. https://doi.org/10.4274/dir.2025.253445