Foundation Models for Medical Imaging: Status, Challenges, and Directions

• URL: http://arxiv.org/abs/2602.15913v1
• Date: Tue, 17 Feb 2026 01:41:56 GMT
• Title: Foundation Models for Medical Imaging: Status, Challenges, and Directions
• Authors: Chuang Niu, Pengwei Wu, Bruno De Man, Ge Wang,
• Abstract summary: Foundation models (FMs) are rapidly reshaping medical imaging.<n>This review provides a technically grounded, clinically aware, and future-facing roadmap for developing FMs.
• Score: 9.491653984670775
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Foundation models (FMs) are rapidly reshaping medical imaging, shifting the field from narrowly trained, task-specific networks toward large, general-purpose models that can be adapted across modalities, anatomies, and clinical tasks. In this review, we synthesize the emerging landscape of medical imaging FMs along three major axes: principles of FM design, applications of FMs, and forward-looking challenges and opportunities. Taken together, this review provides a technically grounded, clinically aware, and future-facing roadmap for developing FMs that are not only powerful and versatile but also trustworthy and ready for responsible translation into clinical practice.

Related papers

• Foundation Models in Medical Image Analysis: A Systematic Review and Meta-Analysis [7.905460364844281]
  Foundations models (FMs) have revolutionized medical image analysis, demonstrating strong zero- and few-shot performance across diverse medical imaging tasks.<n>FMs leverage large corpora of labeled and unlabeled multimodal datasets to learn generalized representations.<n>Despite the rapid proliferation of FM research in medical imaging, the field remains fragmented.<n>This review article provides a comprehensive and structured analysis of FMs in medical image analysis.
  arXiv  Detail & Related papers  (2025-10-19T19:19:23Z)
• Does DINOv3 Set a New Medical Vision Standard? [67.33543059306938]
  This report investigates whether DINOv3 can serve as a powerful unified encoder for medical vision tasks without domain-specific pre-training.<n>We benchmark DINOv3 across common medical vision tasks, including 2D/3D classification and segmentation.<n>Remarkably, it can even outperform medical-specific foundation models like BiomedCLIP and CT-Net on several tasks.
  arXiv  Detail & Related papers  (2025-09-08T09:28:57Z)
• Foundation Models in Medical Imaging -- A Review and Outlook [23.135524334954177]
  Foundation models (FMs) are changing the way medical images are analyzed by learning from large collections of unlabeled data.<n>In this review, we examine how FMs are being developed and applied in pathology, radiology, and ophthalmology.
  arXiv  Detail & Related papers  (2025-06-10T12:14:05Z)
• Vision Foundation Models in Medical Image Analysis: Advances and Challenges [7.224426395050136]
  Vision Foundation Models (VFMs) have sparked significant advances in the field of medical image analysis.<n>This paper reviews the state-of-the-art research on the adaptation of VFMs to medical image segmentation.<n>We discuss the latest developments in adapter-based improvements, knowledge distillation techniques, and multi-scale contextual feature modeling.
  arXiv  Detail & Related papers  (2025-02-20T14:13:46Z)
• A Comprehensive Survey of Foundation Models in Medicine [8.879092631568263]
  Foundation models (FMs) are large-scale deep learning models trained on massive datasets.<n>We present a review of FMs in medicine, focusing on their evolution, learning strategies, flagship models, applications, and associated challenges.<n>We provide a detailed taxonomy of FM-enabled healthcare applications, spanning clinical natural language processing, medical computer vision, graph learning, and other biology- and omics- related tasks.
  arXiv  Detail & Related papers  (2024-06-15T20:04:06Z)
• Towards Large-Scale Training of Pathology Foundation Models [1.5861468117231254]
  We release and make publicly available the first batch of our pathology FMs trained on open-access TCGA whole slide images. The experimental evaluation shows that our models reach state-of-the-art performance on various patch-level downstream tasks. We present an open-source framework designed for the consistent evaluation of pathology FMs across various downstream tasks.
  arXiv  Detail & Related papers  (2024-03-24T21:34:36Z)
• fMRI-PTE: A Large-scale fMRI Pretrained Transformer Encoder for Multi-Subject Brain Activity Decoding [54.17776744076334]
  We propose fMRI-PTE, an innovative auto-encoder approach for fMRI pre-training. Our approach involves transforming fMRI signals into unified 2D representations, ensuring consistency in dimensions and preserving brain activity patterns. Our contributions encompass introducing fMRI-PTE, innovative data transformation, efficient training, a novel learning strategy, and the universal applicability of our approach.
  arXiv  Detail & Related papers  (2023-11-01T07:24:22Z)
• Towards Generalist Foundation Model for Radiology by Leveraging Web-scale 2D&3D Medical Data [66.9359934608229]
  This study aims to initiate the development of Radiology Foundation Model, termed as RadFM. To the best of our knowledge, this is the first large-scale, high-quality, medical visual-language dataset, with both 2D and 3D scans. We propose a new evaluation benchmark, RadBench, that comprises five tasks, including modality recognition, disease diagnosis, visual question answering, report generation and rationale diagnosis.
  arXiv  Detail & Related papers  (2023-08-04T17:00:38Z)
• Customizing General-Purpose Foundation Models for Medical Report Generation [64.31265734687182]
  The scarcity of labelled medical image-report pairs presents great challenges in the development of deep and large-scale neural networks. We propose customizing off-the-shelf general-purpose large-scale pre-trained models, i.e., foundation models (FMs) in computer vision and natural language processing.
  arXiv  Detail & Related papers  (2023-06-09T03:02:36Z)
• Artificial General Intelligence for Medical Imaging Analysis [92.3940918983821]
  Large-scale Artificial General Intelligence (AGI) models have achieved unprecedented success in a variety of general domain tasks. These models face notable challenges arising from the medical field's inherent complexities and unique characteristics. This review aims to offer insights into the future implications of AGI in medical imaging, healthcare, and beyond.
  arXiv  Detail & Related papers  (2023-06-08T18:04:13Z)
• Domain Generalization on Medical Imaging Classification using Episodic Training with Task Augmentation [62.49837463676111]
  We propose a novel scheme of episodic training with task augmentation on medical imaging classification. Motivated by the limited number of source domains in real-world medical deployment, we consider the unique task-level overfitting.
  arXiv  Detail & Related papers  (2021-06-13T03:56:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.