A foundation model for generalizable disease detection from retinal images
Open accessAbstract
Medical artificial intelligence (AI) offers great potential for recognizing signs of health conditions in retinal images and expediting the diagnosis of eye diseases and systemic disorders 1. However, the development of AI models requires substantial annotation and models are usually task-specific with limited generalizability to different clinical applications 2. Here, we present RETFound, a foundation model for retinal images that learns generalizable representations from unlabelled retinal images and provides a basis for label-efficient model adaptation in several applications. Specifically, RETFound is trained on 1.6 million unlabelled retinal images by means of self-supervised learning and then adapted to disease detection tasks with explicit labels. We show that adapted RETFound consistently outperforms several comparison models in the diagnosis and prognosis of sight-threatening eye diseases, as well as incident prediction of complex systemic disorders such as heart failure and myocardial infarction with fewer labelled data. RETFound provides a generalizable solution to improve model performance and alleviate the annotation workload of experts to enable broad clinical AI applications from retinal imaging.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Specialty-specific foundation models typically focus on single imaging modalities.
“Specialty-specific foundation models20,21,23 typically focus on single imaging modalities”
From A Multimodal Vision Foundation Model for Clinical Dermatology (2025) · cites this paper as [20]
PanDerm's scaling behavior aligns with recent foundation model trends.
“The scaling behavior observed in PanDerm’s performance aligns with recent foundation model trends20,22,23,59”
From A Multimodal Vision Foundation Model for Clinical Dermatology (2025) · cites this paper as [20]
Foundation models reduce the data requirements for specialized tasks, for example in predicting diseases from retinal photographs.
“Foundation models reduce the data requirements for specialized tasks, for example in predicting diseases from retinal photographs17.”
From Large language models and multimodal foundation models for precision oncology (2024) · cites this paper as [17]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333 · see citing works
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.74 · Cited by 24,513 · see citing works
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362 · see citing works
High-performance medicine: the convergence of human and artificial intelligence
Topol, E. J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0300-7 · Cited by 10,450 · see citing works
Self-citationThe UK Biobank resource with deep phenotyping and genomic data
Bycroft, C., Freeman, C., Petkova, D., Band, G., Elliott, L. T., Sharp, K., Motyer, A., Vukcevic, D., Delaneau, O., O’Connell, J., et al. (2018) · Nature · doi:10.1038/s41586-018-0579-z · Cited by 8,876 · see citing works
Self-citationMasked Autoencoders Are Scalable Vision Learners
He, K., Chen, X., Xie, S., Li, Y., Dollar, P., & Girshick, R. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01553 · Cited by 8,489 · cited 1× · see citing works
“Besides this label efficiency, SSL-based models perform better than supervised models when tested on new data from different domains 15,16 .”
MethodGulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., et al. (2016) · JAMA · doi:10.1001/jama.2016.17216 · Cited by 7,697 · see citing works
Emerging Properties in Self-Supervised Vision Transformers
Caron, M., Touvron, H., Misra, I., Jegou, H., Mairal, J., Bojanowski, P., & Joulin, A. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00951 · Cited by 5,638 · cited 1× · see citing works
“All task performances are evaluated by the classification metrics known as AUROC and AUPR, computed from the receiver operating characteristics and precision-recall curves of classifiers, respectively.”
MethodIdentifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning
Kermany, D. S., Goldbaum, M., Cai, W., Valentim, C. C. S., Liang, H., Baxter, S. L., McKeown, A., Yang, G., Wu, X., Yan, F., et al. (2018) · Cell · doi:10.1016/j.cell.2018.02.010 · Cited by 4,842 · see citing works
Large Language Models Encode Clinical Knowledge
Singhal, K., Azizi, S., Tu, T., Mahdavi, S. S., Wei, J., Chung, H. W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al. (2022) · arXiv · doi:10.48550/arxiv.2212.13138 · Cited by 4,083 · see citing works
Large language models encode clinical knowledge
Singhal, K., Azizi, S., Tu, T., Mahdavi, S. S., Wei, J., Chung, H. W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al. (2023) · Nature · doi:10.1038/s41586-023-06291-2 · Cited by 4,083 · see citing works
Rajpurkar, P., Chen, E., Banerjee, O., & Topol, E. J. (2022) · Nature Medicine · doi:10.1038/s41591-021-01614-0 · Cited by 3,212 · see citing works
Self-citationArtificial intelligence in healthcare
Yu, K.-H., Beam, A. L., & Kohane, I. S. (2018) · Nature Biomedical Engineering · doi:10.1038/s41551-018-0305-z · Cited by 2,817 · see citing works
On the Opportunities and Risks of Foundation Models
Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al. (2021) · arXiv · doi:10.48550/arxiv.2108.07258 · Cited by 2,676 · see citing works
Clinically applicable deep learning for diagnosis and referral in retinal disease
De Fauw, J., Ledsam, J. R., Romera-Paredes, B., Nikolov, S., Tomasev, N., Blackwell, S., Askham, H., Glorot, X., O’Donoghue, B., Visentin, D., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0107-6 · Cited by 2,639 · see citing works
Self-citationUnsupervised Visual Representation Learning by Context Prediction
Doersch, C., Gupta, A., & Efros, A. A. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.167 · Cited by 2,629 · see citing works
Unsupervised Learning of Visual Features by Contrasting Cluster Assignments
Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., & Joulin, A. (2020) · arXiv · doi:10.48550/arxiv.2006.09882 · Cited by 2,499 · see citing works
Self-Supervised Visual Feature Learning With Deep Neural Networks: A Survey
Jing, L., & Tian, Y. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.2992393 · Cited by 2,036 · see citing works
Foundation models for generalist medical artificial intelligence
Moor, M., Banerjee, O., Abad, Z. S. H., Krumholz, H. M., Leskovec, J., Topol, E. J., & Rajpurkar, P. (2023) · Nature · doi:10.1038/s41586-023-05881-4 · Cited by 2,028 · see citing works
Self-citationLiu, X., Faes, L., Kale, A. U., Wagner, S. K., Fu, D. J., Bruynseels, A., Mahendiran, T., Moraes, G., Shamdas, M., Kern, C., et al. (2019) · The Lancet Digital Health · doi:10.1016/s2589-7500(19)30123-2 · Cited by 1,883 · see citing works
Self-citationToward expert-level medical question answering with large language models
Singhal, K., Tu, T., Gottweis, J., Sayres, R., Wulczyn, E., Amin, M., Hou, L., Clark, K., Pfohl, S. R., Cole-Lewis, H., et al. (2025) · Nature Medicine · doi:10.1038/s41591-024-03423-7 · Cited by 1,734 · see citing works
An Empirical Study of Training Self-Supervised Vision Transformers
Chen, X., Xie, S., & He, K. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00950 · Cited by 1,648 · see citing works
FEEDBACK ON A PUBLICLY DISTRIBUTED IMAGE DATABASE: THE MESSIDOR DATABASE
Decencière, É., Zhang, X., Cazuguel, G., Laÿ, B., Cochener, B., Trone, C., Gain, P., Ordonez, R., Massin, P., Erginay, A., et al. (2014) · Image Analysis & Stereology · doi:10.5566/ias.1155 · Cited by 1,433 · see citing works
Preparing Medical Imaging Data for Machine Learning
Willemink, M. J., Koszek, W. A., Hardell, C., Wu, J., Fleischmann, D., Harvey, H., Folio, L. R., Summers, R. M., Rubin, D. L., & Lungren, M. P. (2020) · Radiology · doi:10.1148/radiol.2020192224 · Cited by 1,020 · see citing works
Transformer Interpretability Beyond Attention Visualization
Chefer, H., Gur, S., & Wolf, L. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00084 · Cited by 903 · see citing works
Wong, T. Y., & Mitchell, P. (2004) · New England Journal of Medicine · doi:10.1056/nejmra032865 · Cited by 703 · see citing works
Self-supervised learning in medicine and healthcare
Krishnan, R., Rajpurkar, P., & Topol, E. J. (2022) · Nature Biomedical Engineering · doi:10.1038/s41551-022-00914-1 · Cited by 605 · see citing works
Self-citationKrause, J., Gulshan, V., Rahimy, E., Karth, P., Widner, K., Corrado, G. S., Peng, L., & Webster, D. R. (2018) · Ophthalmology · doi:10.1016/j.ophtha.2018.01.034 · Cited by 564 · see citing works
Automated Analysis of Retinal Images for Detection of Referable Diabetic Retinopathy
Abràmoff, M. D., Folk, J. C., Han, D. P., Walker, J. D., Williams, D. F., Russell, S. R., Massin, P., Cochener, B., Gain, P., Tang, L., et al. (2013) · JAMA Ophthalmology · doi:10.1001/jamaophthalmol.2013.1743 · Cited by 474 · see citing works
Tiu, E., Talius, E., Patel, P., Langlotz, C. P., Ng, A. Y., & Rajpurkar, P. (2022) · Nature Biomedical Engineering · doi:10.1038/s41551-022-00936-9 · Cited by 442 · see citing works
Self-citationIDRiD: Diabetic Retinopathy – Segmentation and Grading Challenge
Porwal, P., Pachade, S., Kokare, M., Deshmukh, G., Son, J., Bae, W., Liu, L., Wang, J., Liu, X., Gao, L., et al. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.101561 · Cited by 377 · see citing works
Self-citationCen, L., Ji, J., Lin, J., Ju, S.-T., Lin, H.-J., Li, T.-P., Wang, Y., Yang, J.-F., Liu, Y.-F., Tan, S., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-25138-w · Cited by 351 · see citing works
Meta-analysis: Retinal Vessel Caliber and Risk for Coronary Heart Disease
McGeechan, K., Liew, G., Macaskill, P., Irwig, L., Klein, R., Klein, B. E. K., Wang, J. J., Mitchell, P., Vingerling, J. R., deJong, P. T. V. M., et al. (2009) · Annals of Internal Medicine · doi:10.7326/0003-4819-151-6-200909150-00005 · Cited by 342 · see citing works
Towards Expert-Level Medical Question Answering with Large Language Models
Singhal, K., Tu, T., Gottweis, J., Sayres, R., Wulczyn, E., Hou, L., Clark, K., Pfohl, S., Cole-Lewis, H., Neal, D., et al. (2023) · arXiv · doi:10.48550/arxiv.2305.09617 · Cited by 338 · see citing works
Retinal Vessel Diameters and Their Associations with Age and Blood Pressure
Wong, T. Y., Klein, R., Klein, B. E. K., Meuer, S. M., & Hubbard, L. D. (2003) · Investigative Ophthalmology & Visual Science · doi:10.1167/iovs.03-0079 · Cited by 322 · see citing works
Insights into Systemic Disease through Retinal Imaging-Based Oculomics
Wagner, S. K., Fu, D. J., Faes, L., Liu, X., Huemer, J. C., Khalid, H., Ferraz, D., Korot, E., Kelly, C. J., Balaskas, K., et al. (2020) · Translational Vision Science & Technology · doi:10.1167/tvst.9.2.6 · Cited by 312 · see citing works
Self-citationCheung, C. Y.-L., Xu, D., Cheng, C., Sabanayagam, C., Tham, Y. C., Yu, M., Rim, T. H., Chai, C. Y., Gopinath, B., Mitchell, P. L. R., et al. (2020) · Nature Biomedical Engineering · doi:10.1038/s41551-020-00626-4 · Cited by 306 · see citing works
Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging
Azizi, S., Culp, L., Freyberg, J., Mustafa, B., Baur, S., Kornblith, S., Chen, T., Tomasev, N., Mitrović, J., Strachan, P., et al. (2023) · Nature Biomedical Engineering · doi:10.1038/s41551-023-01049-7 · Cited by 250 · see citing works
A deep learning model for the detection of both advanced and early glaucoma using fundus photography
Ahn, J. M., Kim, S., Ahn, K.-S., Cho, S., Lee, K. B., & Kim, U. S. (2018) · PLoS ONE · doi:10.1371/journal.pone.0207982 · Cited by 248 · see citing works
Association of Retinal Nerve Fiber Layer Thinning With Current and Future Cognitive Decline
Ko, F., Muthy, Z. A., Gallacher, J., Sudlow, C. L. M., Rees, G. E., Yang, Q., Keane, P. A., Petzold, A., Khaw, P. T., Reisman, C. A., et al. (2018) · JAMA Neurology · doi:10.1001/jamaneurol.2018.1578 · Cited by 241 · see citing works
Self-citationAssociation of Retinal Neurodegeneration on Optical Coherence Tomography With Dementia
Mutlu, Ü., Colijn, J. M., Ikram, M. A., Bonnemaijer, P. W. M., Licher, S., Wolters, F. J., Tiemeier, H. W., Koudstaal, P. J., Klaver, C. C. W., & Ikram, M. K. (2018) · JAMA Neurology · doi:10.1001/jamaneurol.2018.1563 · Cited by 240 · see citing works
Sung, K. R., Wollstein, G., Bilonick, R. A., Townsend, K. A., Ishikawa, H., Kagemann, L. E., Noecker, R. J., Fujimoto, J. G., & Schuman, J. S. (2009) · Ophthalmology · doi:10.1016/j.ophtha.2009.01.004 · Cited by 228 · see citing works
OCTID: Optical coherence tomography image database
Gholami, P., Roy, P., Parthasarathy, M. K., & Lakshminarayanan, V. (2019) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2019.106532 · Cited by 195 · see citing works
Thomson, K. L., Yeo, J. M., Waddell, B., Cameron, J. R., & Pal, S. (2015) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1016/j.dadm.2015.03.001 · Cited by 193 · see citing works
Retinal vessel diameters and function in cardiovascular risk and disease
Hanssen, H., Streese, L., & Vilser, W. (2022) · Progress in Retinal and Eye Research · doi:10.1016/j.preteyeres.2022.101095 · Cited by 166 · see citing works
AutoMorph: Automated Retinal Vascular Morphology Quantification Via a Deep Learning Pipeline
Zhou, Y., Wagner, S. K., Chia, M. A., Zhao, A., Woodward-Court, P., Xu, M., Struyven, R., Alexander, D. C., & Keane, P. A. (2022) · Translational Vision Science & Technology · doi:10.1167/tvst.11.7.12 · Cited by 162 · see citing works
Self-citationSelf-Supervised Feature Learning via Exploiting Multi-Modal Data for Retinal Disease Diagnosis
Li, X., Jia, M. J., Islam, M. T., Yu, L., & Xing, L. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.3008871 · Cited by 150 · see citing works
Kovalyk, O., Morales‐Sánchez, J., Verdú‐Monedero, R., Sellés-Navarro, I., Palazón-Cabanes, A., & Sancho‐Gómez, J. (2022) · Scientific Data · doi:10.1038/s41597-022-01388-1 · Cited by 136 · see citing works
Predicting myocardial infarction through retinal scans and minimal personal information
Diaz‐Pinto, A., Ravikumar, N., Attar, R., Suinesiaputra, A., Zhao, Y., Levelt, E., Dall’Armellina, E., Lorenzi, M., Chen, Q., Keenan, T. D. L., et al. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-021-00427-7 · Cited by 105 · see citing works
Holmberg, O., Köhler, N., Martins, T. G. D. S., Siedlecki, J., Herold, T., Keidel, L. F., Asani, B., Schiefelbein, J. B., Priglinger, S. G., Kortuem, K., et al. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-00247-1 · Cited by 88 · see citing works
On the Opportunities and Risks of Foundation Models for Natural Language Processing in Radiology
Wiggins, W. F., & Tejani, A. S. (2022) · Radiology Artificial Intelligence · doi:10.1148/ryai.220119 · Cited by 87 · see citing works
Ayhan, M. S., Kuehlewein, L., Aliyeva, G., INHOFFEN, W., Ziemssen, F., & Berens, P. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101724 · Cited by 85 · see citing works
Self-citationRobust and Efficient Medical Imaging with Self-SupervisionPreprint
Azizi, S., Culp, L., Freyberg, J., Mustafa, B., Baur, S., Kornblith, S., Chen, T., MacWilliams, P., Mahdavi, S. S., Wulczyn, E., et al. (2022) · arXiv.org · doi:10.48550/arxiv.2205.09723 · Cited by 82 · see citing works
Impaired Retinal Vessel Dilation Predicts Mortality in End-Stage Renal Disease
Günthner, R., Hanssen, H., Hauser, C., Angermann, S., Lorenz, G., Kemmner, S., Matschkal, J., Braunisch, M. C., Küchle, C., Renders, L., et al. (2019) · Circulation Research · doi:10.1161/circresaha.118.314318 · Cited by 64 · see citing works
Wagner, S. K., Hughes, F., Cortina‐Borja, M., Pontikos, N., Struyven, R., Liu, X., Montgomery, H., Alexander, D. C., Topol, E. J., Petersen, S. E., et al. (2022) · BMJ Open · doi:10.1136/bmjopen-2021-058552 · Cited by 57 · see citing works
Self-citationBurlina, P., Paul, W., Liu, T. Y. A., & Bressler, N. M. (2021) · JAMA Ophthalmology · doi:10.1001/jamaophthalmol.2021.5557 · Cited by 37 · see citing works
Self-citationAyhan, M. S., Kuehlewein, L., Aliyeva, G., et al. (2019) · medRxiv · doi:10.1101/19002154 · Cited by 9 · see citing works
Self-citationAutoMorph: Automated Retinal Vascular Morphology Quantification via a Deep Learning PipelinePreprint
Zhou, Y., Wagner, S. K., Chia, M. A., et al. (2022) · medRxiv · doi:10.1101/2022.05.26.22274795 · Cited by 9 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 5
- [13]Chen, T., Kornblith, S., Swersky, K., Norouzi, M. & Hinton, G. Big self-supervised models are strong semi-supervised learners. In Proc. 34th International Conference on Neural Information Processing Systems (ed. Ranzato, M.) 22243–22255 (Neurips, 2020).
- [16]Chen, T., Kornblith, S., Norouzi, M. & Hinton, G. A simple framework for contrastive learning of visual representations. In Proc. 37th International Conference on Machine Learning (eds Iii, H. D. & Singh, A.) 1597–1607 (PMLR, 2020).
- [30]Truong, T., Mohammadi, S. & Lenga, M. How transferable are self-supervised features in medical image classification tasks? In Proc. 2021 Machine Learning for Health (eds Roy, S. et al.) 54–74 (PMLR, 2021)
- [47]Guo, C., Pleiss, G., Sun, Y. & Weinberger, K. Q. On calibration of modern neural networks. In Proc. 34th International Conference on Machine Learning (eds Precup, D. & Teh, Y. W.) Vol. 70, 1321–1330 (PMLR, 2017).
- [58]Dosovitskiy, A. et al. An image is worth 16×16 words: transformers for image recognition at scale. In Proc. 9th International Conference on Learning Representations (eds Hofmann, K. et al.) https://openreview.net/forum?id=YicbFdNTTy (ICLR 2021).
Cited by
Sorted by citations
A whole-slide foundation model for digital pathology from real-world data
Xu, H., Usuyama, N., Bagga, J., Zhang, S., Rao, R., Naumann, T., Wong, C., Gero, Z., González, J., Gu, Y., et al. (2024) · Nature · doi:10.1038/s41586-024-07441-w · Cited by 921 · see citing works
A pathology foundation model for cancer diagnosis and prognosis prediction
Wang, X., Zhao, J., Marostica, E., Yuan, W., Jin, J., Zhang, J., Li, R., Tang, H., Wang, K., Li, Y., et al. (2024) · Nature · doi:10.1038/s41586-024-07894-z · Cited by 580 · see citing works
A deep learning system for predicting time to progression of diabetic retinopathy
Dai, L., Sheng, B., Chen, T., Wu, Q., Liu, R., Cai, C., Wu, L., Yang, D., Hamzah, H., Liu, Y., et al. (2024) · Nature Medicine · doi:10.1038/s41591-023-02702-z · Cited by 339 · see citing works
Self-citationJoshi, G., Jain, A., Araveeti, S. R., Adhikari, S., Garg, H., & Bhandari, M. (2024) · Electronics · doi:10.3390/electronics13030498 · Cited by 294 · see citing works
On the challenges and perspectives of foundation models for medical image analysis
Zhang, S., & Metaxas, D. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102996 · Cited by 288 · see citing works
A Vision-Language Foundation Model for Precision Oncology
Xiang, J., Wang, X., Zhang, X., Xi, Y., Eweje, F., Chen, Y., Li, Y., Bergstrom, C., Gopaulchan, M., Kim, T., et al. (2025) · Nature · doi:10.1038/s41586-024-08378-w · Cited by 256 · see citing works
MA-SAM: Modality-agnostic SAM adaptation for 3D medical image segmentation
Chen, C., Miao, J., Wu, D., Zhong, A., Yan, Z., Kim, S., Hu, J. J., Liu, Z., Sun, L., Li, X., et al. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103310 · Cited by 246 · see citing works
Self-citationReview of multimodal machine learning approaches in healthcare
Krones, F., Marikkar, U., Parsons, G., Szmul, A., & Mahdi, A. (2025) · Information Fusion · doi:10.1016/j.inffus.2024.102690 · Cited by 235 · see citing works
Vision–language foundation model for echocardiogram interpretation
Christensen, M., Vukadinovic, M., Yuan, N., & Ouyang, D. (2024) · Nature Medicine · doi:10.1038/s41591-024-02959-y · Cited by 231 · see citing works
A guide to artificial intelligence for cancer researchers
Pérez-López, R., Laleh, N. G., Mahmood, F., & Kather, J. N. (2024) · Nature reviews. Cancer · doi:10.1038/s41568-024-00694-7 · Cited by 220 · see citing works
Foundation Model for Cancer Imaging Biomarkers
Pai, S., Bontempi, D., Hadzic, I., Prudente, V., Sokač, M., Chaunzwa, T. L., Bernatz, S., Hosny, A., Mak, R. H., Birkbak, N. J., et al. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00807-9 · Cited by 199 · see citing works
Transformers in single-cell omics: a review and new perspectives
Szałata, A., Hrovatin, K., Becker, S., Tejada-Lapuerta, A., Cui, H., Wang, B., & Theis, F. J. (2024) · Nature Methods · doi:10.1038/s41592-024-02353-z · Cited by 176 · see citing works
In-context learning enables multimodal large language models to classify cancer pathology images
Ferber, D., Wölflein, G., Wiest, I. C., Ligero, M., Sainath, S., Laleh, N. G., Nahhas, O. S. M. E., Müller‐Franzes, G. A., Jäger, D., Truhn, D., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-51465-9 · Cited by 167 · see citing works
SARATR-X: Toward Building a Foundation Model for SAR Target Recognition
Li, W., Yang, W., Hou, Y., Liu, L., Liu, Y., & Li, X. (2025) · IEEE Transactions on Image Processing · doi:10.1109/tip.2025.3531988 · Cited by 124 · see citing works
Generative artificial intelligence in medicine
Teo, Z. L., Thirunavukarasu, A. J., Elangovan, K., Cheng, H., Moova, P., Soetikno, B., Nielsen, C., Pollreisz, A., Ting, D. S. J., Morris, R. J. T., et al. (2025) · Nature Medicine · doi:10.1038/s41591-025-03983-2 · Cited by 119 · see citing works
Foundation Model for Advancing Healthcare: Challenges, Opportunities and Future Directions
He, Y., Huang, F., Jiang, X., Nie, Y., Wang, M., Wang, J., & Chen, H. (2024) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2024.3496744 · Cited by 130 · see citing works
Jiao, J., Zhou, J., Li, X., Xia, M., Huang, Y., Huang, L., Wang, N., Zhang, X., Zhou, S., Wang, Y., et al. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103202 · Cited by 119 · see citing works
Foundation Models in Radiology: What, How, Why, and Why Not
Paschali, M., Chen, Z., Blankemeier, L., Varma, M., Youssef, A., Blüthgen, C., Langlotz, C. P., Gatidis, S., & Chaudhari, A. S. (2025) · Radiology · doi:10.1148/radiol.240597 · Cited by 118 · see citing works
Silva-Rodríguez, J., Chakor, H., Kobbi, R., Dolz, J., & Ayed, I. B. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103357 · Cited by 121 · see citing works
The generative era of medical AI
Fahrner, J., Chen, E., Topol, E. J., & Rajpurkar, P. (2025) · Cell · doi:10.1016/j.cell.2025.05.018 · Cited by 118 · see citing works
Self-citationSwin-UMamba†: Adapting Mamba-Based Vision Foundation Models for Medical Image Segmentation
Liu, J., Yang, H., Zhou, H.-Y., Yu, L., Liang, Y. Q., Yu, Y., Zhang, S., Zheng, H., & Wang, S. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3508698 · Cited by 104 · see citing works
Data-driven modelling of neurodegenerative disease progression: thinking outside the black box
Young, A. L., Oxtoby, N. P., Garbarino, S., Fox, N. C., Barkhof, F., Schott, J. M., & Alexander, D. C. (2024) · Nature reviews. Neuroscience · doi:10.1038/s41583-023-00779-6 · Cited by 103 · see citing works
Self-citationA Multimodal Vision Foundation Model for Clinical Dermatology
Yan, S., Yu, Z., Primiero, C., Vico-Alonso, C., Wang, Z., Yang, L., Tschandl, P., Hu, M., Ju, L., Tan, G., et al. (2025) · Nature Medicine · doi:10.1038/s41591-025-03747-y · Cited by 117 · see citing works
“Specialty-specific foundation models 20,21,23 typically focus on single imaging modalities, while general biomedical models, despite their broad scope, struggle with domain-specific data scarcity and integrating heterogeneous modalities for comprehensive clinical analysis.”
InfluentialA semantic-enhanced multi-modal remote sensing foundation model for Earth observation
Wu, K., Zhang, Y., Ru, L., Dang, B., Lao, J., Yu, L., Luo, J., Zhu, Z., Sun, Y., Zhang, J., et al. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-01078-8 · Cited by 101 · see citing works
Retina Fundus Photograph-Based Artificial Intelligence Algorithms in Medicine: A Systematic Review
Grzybowski, A. E., Jin, K., Zhou, J., Pan, X., Wang, M., Ye, J., & Wong, T. Y. (2024) · Ophthalmology and Therapy · doi:10.1007/s40123-024-00981-4 · Cited by 109 · see citing works
Artificial intelligence in neurology: opportunities, challenges, and policy implications
Voigtlaender, S., Pawelczyk, J., Geiger, M., Vaios, E., Karschnia, P., Cudkowicz, M., Dietrich, J., Haraldsen, I., Feigin, V. L., Owolabi, M. O., et al. (2024) · Journal of Neurology · doi:10.1007/s00415-024-12220-8 · Cited by 106 · see citing works
Ghosh, S., Zhao, X., Alim, M., Brudno, M., & Bhat, M. (2024) · Gut · doi:10.1136/gutjnl-2023-331740 · Cited by 101 · see citing works
Oculomics: Current concepts and evidence
Zhu, Z., Wang, Y., Qi, Z., Hu, W., Zhang, X., Wagner, S. K., Wang, Y., Ran, A. R., Ong, J., Waisberg, E., et al. (2025) · Progress in Retinal and Eye Research · doi:10.1016/j.preteyeres.2025.101350 · Cited by 94 · see citing works
Self-citationYang, Z., Wei, T., Liang, Y., Yuan, X., Gao, R., Xia, Y., Zhou, J., Zhang, Y., & Yu, Z. (2025) · Nature Communications · doi:10.1038/s41467-025-57587-y · Cited by 97 · see citing works
Pretraining a foundation model for generalizable fluorescence microscopy-based image restoration
Ma, C., Tan, W., He, R., & Yan, B. (2024) · Nature Methods · doi:10.1038/s41592-024-02244-3 · Cited by 87 · see citing works
Antaki, F., Milad, D., Chia, M. A., Giguère, C., Touma, S., El‐Khoury, J., Keane, P. A., & Duval, R. (2023) · British Journal of Ophthalmology · doi:10.1136/bjo-2023-324438 · Cited by 83 · see citing works
Self-citationQiu, J., Wu, J., Wei, H., Shi, P., Zhang, M., Sun, Y., Li, L., Liu, H., Liu, H., Hou, S., et al. (2024) · NEJM AI · doi:10.1056/aioa2300221 · Cited by 85 · see citing works
Li, W., Yang, W., Liu, T., Hou, Y., Li, Y., Liu, Z., Liu, Y., & Liu, L. (2024) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2024.09.013 · Cited by 79 · see citing works
Self-citationMedLSAM: Localize and segment anything model for 3D CT images
Lei, W., Xu, W., Li, K., Zhang, X., & Zhang, S. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103370 · Cited by 90 · see citing works
Large language models and multimodal foundation models for precision oncology
Truhn, D., Eckardt, J.-N., Ferber, D., & Kather, J. N. (2024) · npj Precision Oncology · doi:10.1038/s41698-024-00573-2 · Cited by 79 · see citing works
“Foundation models reduce the data requirements for specialized tasks, for example in predicting diseases from retinal photographs 17 .”
A multimodal visual–language foundation model for computational ophthalmology
Shi, D., Zhang, W., Yang, J., Huang, S., Chen, X., Xu, P., Jin, K., Lin, S. C., Wei, J., Yusufu, M., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01772-2 · Cited by 94 · see citing works
The potential for large language models to transform cardiovascular medicine
Quer, G., & Topol, E. J. (2024) · The Lancet Digital Health · doi:10.1016/s2589-7500(24)00151-1 · Cited by 69 · see citing works
Self-citationA systematic review of generalization research in medical image classification
Matta, S. C., Lamard, M., Zhang, P., Guilcher, A. L., Borderie, L., Cochener, B., & Quellec, G. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.109256 · Cited by 75 · see citing works
SAM-Med3D: A Vision Foundation Model for General-Purpose Segmentation on Volumetric Medical Images
Wang, H., Guo, S., Ye, J., Deng, Z., Cheng, J., Li, T., Chen, J., Su, Y., Huang, Z., Shen, Y., et al. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3586694 · Cited by 67 · see citing works
Zhang, Z., Deng, C., & Paulus, Y. M. (2024) · Biomedicines · doi:10.3390/biomedicines12071405 · Cited by 65 · see citing works
Xu, P., Chen, X., Zhao, Z., & Shi, D. (2024) · British Journal of Ophthalmology · doi:10.1136/bjo-2023-325054 · Cited by 63 · see citing works
Zhu, Y., Salowe, R., Chow, C., Li, S., Bastani, O., & O’Brien, J. M. (2024) · Bioengineering · doi:10.3390/bioengineering11020122 · Cited by 63 · see citing works
Demographic bias of expert-level vision-language foundation models in medical imaging
Yang, Y., Liu, Y., Liu, X., Gulhane, A., Mastrodicasa, D., Wu, W., Wang, E. J., Sahani, D. V., & Patel, S. (2025) · Science Advances · doi:10.1126/sciadv.adq0305 · Cited by 67 · see citing works
Artificial intelligence in glaucoma: opportunities, challenges, and future directions
Huang, X., Islam, M. R., Prome, S. A., Ahmed, F., Kazami, E., Serhan, H. A. A., Abd-Alrazaq, A. A., & Yousefi, S. (2023) · BioMedical Engineering OnLine · doi:10.1186/s12938-023-01187-8 · Cited by 69 · see citing works
Huang, W., Li, C., Zhou, H.-Y., Yang, H., Liu, J., Liang, Y., Zheng, H., Zhang, S., & Wang, S. (2024) · Nature Communications · doi:10.1038/s41467-024-51749-0 · Cited by 62 · see citing works
Chew, E. Y., Burns, S. A., Abraham, A. G., Bakhoum, M. F., Beckman, J. A., Chui, T. Y. P., Finger, R. P., Frangi, A. F., Gottesman, R. F., Grant, M. B., et al. (2024) · Nature Reviews Cardiology · doi:10.1038/s41569-024-01060-8 · Cited by 61 · see citing works
Self-citationArtificial intelligence-enhanced retinal imaging as a biomarker for systemic diseases
Wang, J.-Y., Wang, Y.-X., Zeng, D., Zhu, Z., Li, D., Liu, Y., Sheng, B., Grzybowski, A., & Wong, T. Y. (2025) · Theranostics · doi:10.7150/thno.100786 · Cited by 59 · see citing works
An eyecare foundation model for clinical assistance: a randomized controlled trial
Wu, Y.-L., Qian, B., Li, T., Qin, Y., Guan, Z., Chen, T., Jia, Y., Zhang, P., Zeng, D., Moroi, S. E., et al. (2025) · Nature Medicine · doi:10.1038/s41591-025-03900-7 · Cited by 60 · see citing works
Self-citationA deep learning system for detecting silent brain infarction and predicting stroke risk
Jiang, N., Ji, H., Guan, Z., Pan, Y., Deng, C., Guo, Y., Liu, D., Chen, T., Wang, S., Wu, Y.-L., et al. (2025) · Nature Biomedical Engineering · doi:10.1038/s41551-025-01413-9 · Cited by 57 · see citing works
Self-citationAssessing the medical reasoning skills of GPT-4 in complex ophthalmology cases
Milad, D., Antaki, F., Milad, J., Farah, A., Khairy, T., Mikhail, D., Giguère, C., Touma, S., Bernstein, A. J., Szigiato, A.-A., et al. (2024) · British Journal of Ophthalmology · doi:10.1136/bjo-2023-325053 · Cited by 55 · see citing works
Data science opportunities of large language models for neuroscience and biomedicine
Bzdok, D., Thieme, A., Levkovskyy, O., Wren, P. B., Ray, T. S., & Reddy, S. (2024) · Neuron · doi:10.1016/j.neuron.2024.01.016 · Cited by 58 · see citing works
Foundation models in ophthalmology
Chia, M. A., Antaki, F., Zhou, Y., Turner, A. W., Lee, A., & Keane, P. A. (2024) · British Journal of Ophthalmology · doi:10.1136/bjo-2024-325459 · Cited by 55 · see citing works
Self-citationZhang, J., Lin, S., Cheng, T., Xu, Y., Lu, L., He, J., Yu, T., Peng, Y., Zhang, Y., Zou, H., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01109-5 · Cited by 51 · see citing works
TMO-Net: an explainable pretrained multi-omics model for multi-task learning in oncology
Wang, F.-A., Zhuang, Z., Gao, F., He, R., Zhang, S., Wang, L., Liu, J., & Li, Y. (2024) · Genome Biology · doi:10.1186/s13059-024-03293-9 · Cited by 51 · see citing works
Muse, E. D., & Topol, E. J. (2024) · Cell Metabolism · doi:10.1016/j.cmet.2024.02.002 · Cited by 50 · see citing works
Self-citationRecent advancements and applications of deep learning in heart failure: Α systematic review
Πετμεζάς, Γ., Papageorgiou, V. Ε., Vassilikos, V. P., Pagourelias, E. D., Tsaklidis, G. M., Katsaggelos, A. K., & Maglaveras, N. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108557 · Cited by 50 · see citing works
Rajendran, S., Pan, W., Sabuncu, M. R., Chen, Y., Zhou, J., & Wang, F. (2024) · Patterns · doi:10.1016/j.patter.2023.100913 · Cited by 47 · see citing works
Sun, K., Xue, S., Sun, F., Sun, H., Luo, Y., Wang, L., Wang, S., Guo, N., Liu, L., Zhao, T., et al. (2025) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2025.103265 · Cited by 61 · see citing works
Medical image classification with KAN-integrated transformers and dilated neighborhood attention
Manzari, O. N., Asgariandehkordi, H., Koleilat, T., Xiao, Y., & Rivaz, H. J. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.114045 · Cited by 46 · see citing works
BrainMass: Advancing Brain Network Analysis for Diagnosis With Large-Scale Self-Supervised Learning
Yang, Y., Ye, C., Su, G., Zhang, Z., Chang, Z., Chen, H., Chan, P., Yu, Y., & Ma, T. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3414476 · Cited by 45 · see citing works
Bian, Y., Li, J., Ye, C., Jia, X., & Yang, Q. (2025) · Chinese Medical Journal · doi:10.1097/cm9.0000000000003489 · Cited by 48 · see citing works
A benchmarking crisis in biomedical machine learning
Mahmood, F. (2025) · Nature Medicine · doi:10.1038/s41591-025-03637-3 · Cited by 44 · see citing works
A fully open AI foundation model applied to chest radiography
Ma, D., Pang, J., Gotway, M. B., & Liang, J. (2025) · Nature · doi:10.1038/s41586-025-09079-8 · Cited by 48 · see citing works
An Electrocardiogram Foundation Model Built on over 10 Million Recordings
Li, J., Aguirre, A. D., Junior, V. F. M., Jin, J., Liu, C., Zhong, L., Sun, C., Clifford, G. D., Westover, M. B., & Hong, S. (2025) · NEJM AI · doi:10.1056/aioa2401033 · Cited by 62 · see citing works
Application of Artificial Intelligence in Ophthalmology: An Updated Comprehensive Review
Hashemian, H., Pető, T., Ambrósio, R., Lengyel, I., Kafieh, R., Noori, A. M., & Khorrami‐Nejad, M. (2024) · Journal of Ophthalmic and Vision Research · doi:10.18502/jovr.v19i3.15893 · Cited by 43 · see citing works
Retinal imaging for the assessment of stroke risk: a systematic review
Girach, Z., Sarian, A., Maldonado-García, C., Ravikumar, N., Sergouniotis, P. I., Rothwell, P. M., Frangi, A. F., & Julian, T. H. (2024) · Journal of Neurology · doi:10.1007/s00415-023-12171-6 · Cited by 40 · see citing works
Self-citationThe AI revolution in glaucoma: Bridging challenges with opportunities
Li, F., Wang, D., Yang, Z., Zhang, Y., Jiang, J., Liu, X., Kong, K., Zhou, F., Tham, C. C. Y., Medeiros, F. A., et al. (2024) · Progress in Retinal and Eye Research · doi:10.1016/j.preteyeres.2024.101291 · Cited by 40 · see citing works
A Competition for the Diagnosis of Myopic Maculopathy by Artificial Intelligence Algorithms
Qian, B., Sheng, B., Chen, H., Wang, X., Li, T., Jin, Y., Guan, Z., Jiang, Z., Wu, Y.-L., Wang, J., et al. (2024) · JAMA Ophthalmology · doi:10.1001/jamaophthalmol.2024.3707 · Cited by 40 · see citing works
Self-citationOperational greenhouse-gas emissions of deep learning in digital pathology: a modelling study
Sadr, A. V., Bülow, R. D., von Stillfried, S. F., Schmitz, N. E. J., Pilva, P., Hölscher, D. L., Pilechiha, P., Schweiker, M., & Boor, P. (2023) · The Lancet Digital Health · doi:10.1016/s2589-7500(23)00219-4 · Cited by 43 · see citing works
Weakly Supervised Deep Learning in Radiology
Misera, L., Müller‐Franzes, G. A., Truhn, D., & Kather, J. N. (2024) · Radiology · doi:10.1148/radiol.232085 · Cited by 39 · see citing works
The urgent need to accelerate synthetic data privacy frameworks for medical research
Arora, A., Wagner, S. K., Carpenter, R., Jena, R., & Keane, P. A. (2024) · The Lancet Digital Health · doi:10.1016/s2589-7500(24)00196-1 · Cited by 39 · see citing works
Self-citationA data-efficient strategy for building high-performing medical foundation models
Sun, Y., Tan, W., Gu, Z., He, R., Chen, S., Pang, M., & Yan, B. (2025) · Nature Biomedical Engineering · doi:10.1038/s41551-025-01365-0 · Cited by 42 · see citing works
Yang, Q., Bee, Y. M., Lim, C. C., Sabanayagam, C., Yim-Lui Cheung, C., Wong, T. Y., Ting, D. S. W., Lim, L.-L., Li, H., He, M., et al. (2025) · eClinicalMedicine · doi:10.1016/j.eclinm.2025.103089 · Cited by 38 · see citing works
Self-citationA hybrid multi model artificial intelligence approach for glaucoma screening using fundus images
Sharma, P., Takahashi, N., Ninomiya, T., Sato, M., Miya, T., Tsuda, S., & Nakazawa, T. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01473-w · Cited by 44 · see citing works
Wang, X., Jiang, Y., Yang, S., Wang, F., Zhang, X., Wang, W., Chen, Y. M., Wu, X., Xiang, J., Li, Y., et al. (2025) · Journal of Clinical Oncology · doi:10.1200/jco-24-01501 · Cited by 43 · see citing works
Large models in medical imaging: Advances and prospects
Fang, M., Wang, Z., Pan, S., Feng, X., Zhao, Y., Hou, D., Wu, L., Xie, X., Zhang, X.-Y., Tian, J., et al. (2025) · Chinese Medical Journal · doi:10.1097/cm9.0000000000003699 · Cited by 39 · see citing works
Wang, M., Lin, T., Lin, A., Yu, K., Peng, Y., Wang, L., Chen, C., Zou, K., Liang, H., Chen, M., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-60577-9 · Cited by 53 · see citing works
Self-citationRET-CLIP: A Retinal Image Foundation Model Pre-trained with Clinical Diagnostic Reports
Du, J., Guo, J., Zhang, W., Yang, S., Liu, H., Li, H., & Wang, N. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72390-2_66 · Cited by 37 · see citing works
Foundation model of ECG diagnosis: Diagnostics and explanations of any form and rhythm on ECG
Tian, Y., Li, Z., Jin, Y., Wang, M., Wei, X., Zhao, L., Liu, Y., Liu, J., & Liu, C. (2024) · Cell Reports Medicine · doi:10.1016/j.xcrm.2024.101875 · Cited by 41 · see citing works
Multimodal artificial intelligence in ophthalmology: Applications, challenges, and future directions
Jin, K., Yu, T., & Grzybowski, A. E. (2025) · Survey of Ophthalmology · doi:10.1016/j.survophthal.2025.07.003 · Cited by 36 · see citing works
Vision-Language Models for Feature Detection of Macular Diseases on Optical Coherence Tomography
Antaki, F., Chopra, R., & Keane, P. A. (2024) · JAMA Ophthalmology · doi:10.1001/jamaophthalmol.2024.1165 · Cited by 35 · see citing works
Self-citationVela, S. O., Beyeler, M. J., Trofimova, O., Iuliani, I., Quiros, J. V., de Vries, V. A., Meloni, I., Elwakil, A., Hoogewoud, F., Liefers, B. J., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-52334-1 · Cited by 35 · see citing works
Artificial intelligence-driven transformative applications in disease diagnosis technology
Zhou, J., Park, S., Dong, S., Tang, X., & Wei, X. (2025) · Medical Review · doi:10.1515/mr-2024-0097 · Cited by 35 · see citing works
Acute Myocardial Infarction: Molecular Pathogenesis, Diagnosis, and Clinical Management
Zhu, M., Li, Y., Xu, Q., Wang, W., Liu, Y., & Liu, Y. (2025) · MedComm · doi:10.1002/mco2.70418 · Cited by 40 · see citing works
The Role of Artificial Intelligence in the Identification and Evaluation of Bone Fractures
Tieu, A., Kroen, E., Kadish, Y., Liu, Z., Patel, N. R., Zhou, A., Yilmaz, A., Lee, S., & Deyer, T. W. (2024) · Bioengineering · doi:10.3390/bioengineering11040338 · Cited by 36 · see citing works
Progress on early diagnosing Alzheimer’s disease
Chen, Y., Al‐Nusaif, M., Li, S., Tan, X., Yang, H., Cai, H., & Le, W. (2024) · Frontiers of Medicine · doi:10.1007/s11684-023-1047-1 · Cited by 34 · see citing works
Li, L. Y., Isaksen, A. A., Lebiecka-Johansen, B., Funck, K. L., Thambawita, V. L., Byberg, S., Andersen, T. H., Nørgaard, O., & Hulmán, Á. (2024) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztae068 · Cited by 34 · see citing works
Sönmez, S. C., Sevgi, M., Antaki, F., Huemer, J. C., & Keane, P. A. (2024) · British Journal of Ophthalmology · doi:10.1136/bjo-2024-325458 · Cited by 36 · see citing works
Self-citationArdic, N., & Dinc, R. (2025) · Health Informatics Journal · doi:10.1177/14604582251366141 · Cited by 32 · see citing works
Diffusion Models with Implicit Guidance for Medical Anomaly Detection
Bercea, C. I., Wiestler, B., Rueckert, D., & Schnabel, J. A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72120-5_20 · Cited by 31 · see citing works
An AI revolution is brewing in medicine. What will it look like?
Lenharo, M. (2023) · Nature · doi:10.1038/d41586-023-03302-0 · Cited by 30 · see citing works
Innovating robot-assisted surgery through large vision models
Min, Z., Lai, J., & Ren, H. (2025) · Nature Reviews Electrical Engineering · doi:10.1038/s44287-025-00166-6 · Cited by 31 · see citing works
Retinal Alterations Predict Early Prodromal Signs of Neurodegenerative Disease
Casciano, F., Zauli, E., Celeghini, C., Caruso, L., Gonelli, A., Zauli, G., & Pignatelli, A. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25031689 · Cited by 26 · see citing works
Artificial Intelligence for Optical Coherence Tomography in Glaucoma
Djulbegovic, M. B., Bair, H., Gonzalez, D. J. T., Ishikawa, H., Wollstein, G., & Schuman, J. S. (2025) · Translational Vision Science & Technology · doi:10.1167/tvst.14.1.27 · Cited by 29 · see citing works
A foundation model for continuous glucose monitoring data
Lutsker, G., Sapir, G., Shilo, S., Merino, J., Godneva, A., Greenfield, J. R., Samocha‐Bonet, D., Dhir, R., Gude, F., Mannor, S., et al. (2026) · Nature · doi:10.1038/s41586-025-09925-9 · Cited by 49 · see citing works
Contrastive Decoupling: Dynamic Regularization for Enhanced Fine-Grained Image Classification
Wang, Z., Li, T., Qin, Y., & Sheng, B. (2026) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2026.3661760 · Cited by 33 · see citing works
Advancing healthcare: the role and impact of AI and foundation models
Mahesh, N. (2024) · American Journal of Translational Research · doi:10.62347/wqwv9220 · Cited by 28 · see citing works
Multimodal foundation model and benchmark for comprehensive retinal OCT image analysis
Morano, J., Fazekas, B., Sükei, E., Fecso, R., Emre, T., Gumpinger, M., Faustmann, G., Oghbaie, M., Schmidt-Erfurth, U. M., & Bogunović, H. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01852-3 · Cited by 31 · see citing works
Improving Representation of High-Frequency Components for Medical Visual Foundation Models
Chu, Y., Zhang, Y., Han, Z., Yang, C., Zhou, L., Luo, G., Huang, C., & Gao, X. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3559402 · Cited by 28 · see citing works
Graph-Aware Multimodal Deep Learning for Classification of Diabetic Retinopathy Images
Zedadra, A., Zedadra, O., Salah-Salah, M. Y., & Guerrieri, A. (2025) · IEEE Access · doi:10.1109/access.2025.3564529 · Cited by 27 · see citing works
- Authors
- Zhou, Y., Chia, M. A., Wagner, S. K., Ayhan, M. S., Williamson, D. J., Struyven, R. R., Liu, T., Xu, M., Lozano, M. G., Woodward-Court, P., Kihara, Y., UK Biobank Eye & Vision Consortium, Allen, N., Gallacher, J. E. J., Littlejohns, T., Aslam, T., Bishop, P., Black, G., Sergouniotis, P., Atan, D., Dick, A. D., Williams, C., Barman, S., Barrett, J. H., Mackie, S., Braithwaite, T., Carare, R. O., Ennis, S., Gibson, J., Lotery, A. J., Self, J., Chakravarthy, U., Hogg, R. E., Paterson, E., Woodside, J., Peto, T., Mckay, G., Mcguinness, B., Foster, P. J., Balaskas, K., Khawaja, A. P., Pontikos, N., Rahi, J. S., Lascaratos, G., Patel, P. J., Chan, M., Chua, S. Y. L., Day, A., Desai, P., Egan, C., Fruttiger, M., Garway-Heath, D. F., Hardcastle, A., Khaw, S. P. T., Moore, T., Sivaprasad, S., Strouthidis, N., Thomas, D., Tufail, A., Viswanathan, A. C., Dhillon, B., Macgillivray, T., Sudlow, C., Vitart, V., Doney, A., Trucco, E., Guggeinheim, J. A., Morgan, J. E., Hammond, C. J., Williams, K., Hysi, P., Harding, S. P., Zheng, Y., Luben, R., Luthert, P., Sun, Z., McKibbin, M., O’Sullivan, E., Oram, R., Weedon, M., Owen, C. G., Rudnicka, A. R., Sattar, N., Steel, D., Stratton, I., Tapp, R., Yates, M. M., Petzold, A., Madhusudhan, S., Altmann, A., Lee, A. Y., Topol, E. J., Denniston, A. K., Alexander, D. C., & Keane, P. A.
- Affiliations
- Moorfields Eye Hospital NHS Foundation Trust (GB)
- University College London (GB)
- Universidade da Coruña (ES)
- University of Washington (US)
- University of Oxford (GB)
- University of Manchester (GB)
- University of Bristol (GB)
- Kingston University London (GB)
- University of Leeds (GB)
- St Thomas' Hospital (GB)
- University of Southampton (GB)
- Queen's University Belfast (GB)
- University of Edinburgh (GB)
- University of Dundee (GB)
- Cardiff University (GB)
- King's College London (GB)
- University of Liverpool (GB)
- Leeds Teaching Hospitals NHS Trust (GB)
- King's College Hospital NHS Foundation Trust (GB)
- University of Exeter (GB)
- University of London (GB)
- University of Glasgow (GB)
- Newcastle University (GB)
- Gloucestershire Hospitals NHS Foundation Trust (GB)
- St George's, University of London (GB)
- University of East Anglia (GB)
- UCL Queen Square Institute of Neurology (GB)
- Royal Liverpool University Hospital (GB)
- University Hospitals Birmingham NHS Foundation Trust (GB)
- Corresponding author
- Zhou, Yukun
- Keane, Pearse A.
- ORCID
- Zhou, Yukun
- Chia, Mark A.
- Wagner, Siegfried K.
- Ayhan, Murat S.
- Williamson, Dominic J.
- Struyven, Robbert R.
- Liu, Timing
- Xu, Moucheng
- Lozano, Mateo G.
- Woodward-Court, Peter
- Kihara, Yuka
- Allen, Naomi
- Gallacher, John E. J.
- Littlejohns, Thomas
- Aslam, Tariq
- Black, Graeme
- Sergouniotis, Panagiotis
- Atan, Denize
- Dick, Andrew D.
- Williams, Cathy
- Barman, Sarah
- Mackie, Sarah
- Braithwaite, Tasanee
- Carare, Roxana O.
- Ennis, Sarah
- Lotery, Andrew J.
- Self, Jay
- Chakravarthy, Usha
- Hogg, Ruth E.
- Paterson, Euan
- Woodside, Jayne
- Mckay, Gareth
- Mcguinness, Bernadette
- Foster, Paul J.
- Balaskas, Konstantinos
- Khawaja, Anthony P.
- Pontikos, Nikolas
- Rahi, Jugnoo S.
- Lascaratos, Gerassimos
- Patel, Praveen J.
- Chan, Michelle
- Chua, Sharon Y. L.
- Day, Alexander
- Desai, Parul
- Fruttiger, Marcus
- Hardcastle, Alison
- Moore, Tony
- Sivaprasad, Sobha
- Strouthidis, Nicholas
- Thomas, Dhanes
- Tufail, Adnan
- Viswanathan, Ananth C.
- Dhillon, Bal
- Macgillivray, Tom
- Sudlow, Cathie
- Trucco, Emanuele
- Williams, Katie
- Hysi, Pirro
- Harding, Simon P.
- Zheng, Yalin
- Luben, Robert
- Sun, Zihan
- McKibbin, Martin
- Oram, Richard
- Rudnicka, Alicja R.
- Sattar, Naveed
- Steel, David
- Stratton, Irene
- Tapp, Robyn
- Yates, Max M.
- Petzold, Axel
- Madhusudhan, Savita
- Altmann, Andre
- Lee, Aaron Y.
- Topol, Eric J.
- Denniston, Alastair K.
- Alexander, Daniel C.
- Keane, Pearse A.
- Venue
- Nature
- Published
- 2023-09-13
- Volume
- 622
- Issue
- 7981
- Pages
- 156-163
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC10550819
- OpenAlex
- W4386697749
- Cited by
- 1,014
- References
- 55
- Citations by year
- 2026: 388 · 2025: 389 · 2024: 214 · 2023: 21
- FWCI
- 166.84
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nature (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Discovery Research Portal (University of Dundee) (publishedVersion, cc-by)
- Pure Amsterdam UMC (publishedVersion, cc-by)
- Amsterdam UMC (VU Amsterdam) - Institutional Repository (publishedVersion, other-oa)
- Research Portal (Queen's University Belfast) (publishedVersion, cc-by)
- Explore Bristol Research (publishedVersion, cc-by)
- ORCA Online Research @Cardiff (Cardiff University) (acceptedVersion)
- ePrints Soton (University of Southampton) (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- University of Birmingham Research Portal (University of Birmingham) (submittedVersion, other-oa)
- RUC (Universidade Da Coruña) (submittedVersion, cc-by)
- RUNA. Repositorio Institucional del Sergas (Servicio Gallego de Salud (SERGAS)) (submittedVersion, cc-by)
- Newcastle University ePrints (Newcastle Univesity) (cc-by)
- Funders
- National Institute for Health and Care Research (NIHR205103, NF-SI-0515-10020, NIHR205096, NIHR205109)
- University College London Hospitals NHS Foundation Trust
- Moorfields Eye Hospital NHS Foundation Trust
- Moorfields Eye Charity (R190028A)
- Medical Research Council (MR/K003364/1, MR/TR000953/1, MC_UU_00007/10, MR/T019050/1)
- Engineering and Physical Sciences Research Council (EP/M020533/1, EP/V034537/1, M020533/1, M020533, EP/R014019/1, V034537, R014019)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
- Health Sciences › Medicine › Ophthalmology › Ophthalmology and Visual Health Research
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- RETFound, foundation models, fundus images, self-supervised learning, eye disease detection, heart failure, myocardial infarction, artificial intelligence in healthcare, annotation workload reduction
- MeSH terms
- Supervised Machine Learning, Artificial Intelligence, Eye Diseases, Heart Failure, Humans, Myocardial Infarction, Retina
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
