Vision Foundation Models in Radiology: A Scoping Review of Data, Methodology, Evaluation and Clinical Translation

2026-07-08 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Medical Imaging AI · Depth: Expert, quick

Summary

A PRISMAScR scoping review analyzed 67 peer-reviewed studies published between January 2017 and March 2026, focusing on Vision Foundation Models (VFMs) trained exclusively on radiological imaging data. The review mapped these studies across data scale and heterogeneity, architectural and pretraining scalability, and downstream transferability and generalization. Datasets primarily covered brain MRI, thoracoabdominal CT, and chest X-ray, with sizes ranging from fewer than 100,000 samples to multi-million-image cohorts. Transformer-based architectures and self-supervised pretraining, including masked image modeling, contrastive learning, and multi-stage approaches, predominated. Evaluation mainly focused on segmentation and classification, but cross-center, cross-scanner, anatomical, and modality-shift validation were inconsistently reported. While radiology-specific VFMs show promising transferability, clinical translation is constrained by limited data representativeness, heterogeneous benchmarks, incomplete reporting, and insufficient deployment-oriented evaluation, alongside uneven alignment with FUTURE-AI principles.

Key takeaway

For AI Scientists developing Vision Foundation Models for radiology, you should prioritize addressing data representativeness and establishing standardized, deployment-oriented evaluation benchmarks. Inconsistent reporting of cross-center and modality-shift validation currently hinders clinical translation, despite promising transferability. Focus on robust validation strategies and align development with principles like FUTURE-AI to accelerate real-world impact and overcome current limitations in heterogeneous benchmarks.

Key insights

Radiology-specific Vision Foundation Models show promising transferability but face significant hurdles for clinical translation.

Principles

• VFM development in radiology is heterogeneous.
• Data representativeness limits clinical translation.
• Consistent evaluation is crucial for VFMs.

Method

The review conducted a PRISMAScR scoping review of studies published between January 2017 and March 2026, mapping them across data, architecture, and transferability pillars.

In practice

• Focus VFM training on brain MRI, CT, chest X-ray.
• Utilize Transformer architectures.
• Implement self-supervised pretraining.

Topics

• Vision Foundation Models
• Radiological Imaging
• Clinical Translation
• Self-supervised Learning
• Transformer Architectures
• Medical Image Analysis

Best for: AI Scientist, Research Scientist, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.