mlsanityData to 14 Oct 2026

Foundation Models in Medical Imaging: A Review and Outlook

arXiv 2506.09095 ↗First posted 10 Jun 2025eess.IV cs.AI cs.CVcited by 8

Not on the map. Why?

Abstract (arXiv)

Foundation models (FMs) are changing the way medical images are analyzed by learning from large collections of unlabeled data. Instead of relying on manually annotated examples, FMs are pre-trained to learn general-purpose visual features that can later be adapted to specific clinical tasks with little additional supervision. In this review, we examine how FMs are being developed and applied in pathology, radiology, and ophthalmology, drawing on evidence from over 150 studies. We explain the core components of FM pipelines, including model architectures, self-supervised learning methods, and strategies for downstream adaptation. We also review how FMs are being used in each imaging domain and compare design choices across applications. Finally, we discuss key challenges and open questions to guide future research.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 144 of its 210 references that are in the map. First posted on arXiv 10 Jun 2025.

Builds on

144 of 210 references.

References · 144

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  3. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  4. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  5. 2020Language Models are Few-Shot Learnerscited by 32,419
  6. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  7. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  8. 2023GPT-4 Technical Reportcited by 18,396
  9. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  10. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  11. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
  12. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
38 more
  1. 2023Segment Anythingcited by 8,234
  2. 2023DINOv2: Learning Robust Visual Features without Supervisioncited by 7,974
  3. 2023Visual Instruction Tuningcited by 7,869
  4. 2018Representation Learning with Contrastive Predictive Codingcited by 7,777
  5. 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,513
  6. 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,403
  7. 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,968
  8. 2020Scaling Laws for Neural Language Modelscited by 6,586
  9. 2020Bootstrap your own latent: A new approach to self-supervised Learningcited by 4,750
  10. 2022Flamingo: a Visual Language Model for Few-Shot Learningcited by 4,386
  11. 2019Parameter-Efficient Transfer Learning for NLPcited by 4,236
  12. 2021Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervisioncited by 3,864
  13. 2021The Power of Scale for Parameter-Efficient Prompt Tuningcited by 3,795
  14. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,780
  15. 2020Unsupervised Learning of Visual Features by Contrasting Cluster Assignmentscited by 2,857
  16. 2021BEiT: BERT Pre-Training of Image Transformerscited by 2,375
  17. 2021Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutionscited by 1,962
  18. 2022Visual Prompt Tuningcited by 1,681
  19. 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,558
  20. 2022Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?cited by 1,434
  21. 2019CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparisoncited by 1,423
  22. 2023LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Daycited by 1,305
  23. 2023Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecturecited by 1,039
  24. 2021UNETR: Transformers for 3D Medical Image Segmentationcited by 959
  25. 2021CvT: Introducing Convolutions to Vision Transformerscited by 947
  26. 2021iBOT: Image BERT Pre-Training with Online Tokenizercited by 903
  27. 2023A Comprehensive Survey of Continual Learning: Theory, Method and Applicationcited by 872
  28. 2020Efficient Transformers: A Surveycited by 790
  29. 2020Contrastive Learning of Medical Visual Representations from Paired Images and Textcited by 641
  30. 2022MedCLIP: Contrastive Learning from Unpaired Medical Images and Textcited by 570
  31. 2023BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairscited by 520
  32. 2023Med-Flamingo: a Multimodal Medical Few-shot Learnercited by 392
  33. 2021Attention Bottlenecks for Multimodal Fusioncited by 361
  34. 2022Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learningcited by 359
  35. 2022Making the Most of Text Semantics to Improve Biomedical Vision--Language Processingcited by 339
  36. 2021XCiT: Cross-Covariance Image Transformerscited by 338
  37. 2023PMC-VQA: Visual Instruction Tuning for Medical Visual Question Answeringcited by 338
  38. 2023Segment Anything Model for Medical Image Analysis: an Experimental Studycited by 291

50 most cited of 144 All 144 (JSON)

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 5.

8 papers.

  1. 2025RAU: Reference-based Anatomical Understanding with Vision Language Modelscited by 3
  2. 2025Diffusion-Driven Generation of Minimally Preprocessed Brain MRIcited by 2
  3. 2025Evaluating Fundus-Specific Foundation Models for Diabetic Macular Edema Detectioncited by 0
  4. 2026Foundation Models for Medical Imaging: Status, Challenges, and Directionscited by 0
  5. 2026From Kellgren-Lawrence to Calcium Pyrophosphate Crystal Deposition: A Soft-Labelling Framework for Knee Osteoarthritis Assessmencited by 0
  6. 2026Beyond Natural-Image Foundation Models: Benchmarking Satellite Pretraining for Ophthalmic Image Analysiscited by 0
  7. 2026Mammography Foundation Models for Opportunistic Prediction of Major Adverse Cardiovascular Eventscited by 0
  8. 2026Image-Based Breast Implant Detection for Mammography Dataset Curation and Near-Real-Time Deployment: Comparing Foundation Models and Task-Specific Convolutional Modelscited by 0