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Foundation models in medical imaging: two reviews detail progress and challenges

By PulseAugur Editorial · [2 sources] ·

Two recent arXiv papers offer systematic reviews of foundation models (FMs) in medical image analysis. The first paper categorizes FMs into vision-only and vision-language models, analyzing their evolution, clinical applications, and persistent challenges like generalization and interpretability. The second paper specifically focuses on the adaptation mechanisms of FMs for medical imaging, finding that adaptation strategies are context-dependent and that evidence for clinical readiness, including external validation and calibration, remains limited. AI

IMPACT These reviews highlight the rapid progress and remaining challenges in applying foundation models to medical imaging, guiding future research towards more robust and clinically integrated AI solutions.

RANK_REASON Two arXiv papers provide systematic reviews on foundation models in medical image analysis, detailing their evolution, adaptation, and clinical readiness.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Foundation models in medical imaging: two reviews detail progress and challenges

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Two arXiv papers provide systematic reviews on foundation models in medical image analysis, detailing their evolution, adaptation, and clinical readiness.
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Praveenbalaji Rajendran, Mojtaba Safari, Wenfeng He, Mingzhe Hu, Shansong Wang, Jun Zhou, Xiaofeng Yang ·

    Foundation Models in Medical Image Analysis: A Systematic Review and Meta-Analysis

    arXiv:2510.16973v2 Announce Type: replace-cross Abstract: Recent advancements in foundation models (FMs) have catalyzed a paradigm shift in medical image analysis. Unlike traditional task-specific artificial intelligence (AI) models, FMs leverage large-scale datasets to learn gen…

  2. arXiv cs.CV TIER_1 English(EN) · Karma Phuntsho, Abdullah, Kyungmi Lee, Ickjai Lee, Euijoon Ahn ·

    Adaptation of Foundation Models in Medical Image Analysis: Mechanisms, Evidence, and Deployment Considerations

    arXiv:2603.14271v2 Announce Type: replace Abstract: Foundation models, pretrained on diverse data, objectives, or modalities for reuse across downstream tasks, are increasingly applied to medical image analysis. Their performance depends not only on pretraining but also on how th…

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