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Towards a general-purpose foundation model for computational pathology
Open accessAbstract
Quantitative evaluation of tissue images is crucial for computational pathology (CPath) tasks, requiring the objective characterization of histopathological entities from whole-slide images (WSIs). The high resolution of WSIs and the variability of morphological features present significant challenges, complicating the large-scale annotation of data for high-performance applications. To address this challenge, current efforts have proposed the use of pretrained image encoders through transfer learning from natural image datasets or self-supervised learning on publicly available histopathology datasets, but have not been extensively developed and evaluated across diverse tissue types at scale. We introduce UNI, a general-purpose self-supervised model for pathology, pretrained using more than 100 million images from over 100,000 diagnostic H&E-stained WSIs (>77 TB of data) across 20 major tissue types. The model was evaluated on 34 representative CPath tasks of varying diagnostic difficulty. In addition to outperforming previous state-of-the-art models, we demonstrate new modeling capabilities in CPath such as resolution-agnostic tissue classification, slide classification using few-shot class prototypes, and disease subtyping generalization in classifying up to 108 cancer types in the OncoTree classification system. UNI advances unsupervised representation learning at scale in CPath in terms of both pretraining data and downstream evaluation, enabling data-efficient artificial intelligence models that can generalize and transfer to a wide range of diagnostically challenging tasks and clinical workflows in anatomic pathology. Pretrained using over 100,000 diagnostic histopathological slides across 20 major tissue types, a self-supervised model is shown to outperform existing baselines across various clinically relevant computational pathology tasks.
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“By increasing the patch size from the widely used 256 × 256 pixels, we effectively reduce the sequence length by four without impacting the representation quality due to higher resolution patch input, leveraging the robustness of the patch-level foundation models in generalizing to higher resolutions9,10,87.”
From A multimodal whole-slide foundation model for pathology (2025) · cites this paper as [9]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“To overcome these limitations, several research groups have alternatively proposed the use of weakly-supervised deep learning models, which can be trained to perform various downstream computational pathology tasks such as tumor subtyping and prognosis32–37 without any region or pixel-level annotations.”
From Predicting the tumor microenvironment composition and immunotherapy response in non-small cell lung cancer from digital histopathology images (2024) · cites this paper as [32]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“UNI [191], Prov-GigaPath [192], and Virchow [193] were pretrained solely on pathology image data to perform cancer classification, diagnosis, and prognosis.”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [191]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“The vision module is based on the patch encoder CTransPath17 for the small and medium models, and UNI18 for the large model.”
From Generating dermatopathology reports from gigapixel whole slide images with HistoGPT (2025) · cites this paper as [18]
The cited source says what the claim attributes to it.
In the citing paper
“We found out that the UNI foundation model32, when paired with multi-task AB-MIL, achieved the best predictive performance for the various TME signature prediction tasks.”
From Predicting the tumor microenvironment composition and immunotherapy response in non-small cell lung cancer from digital histopathology images (2024) · cites this paper as [32]
In the citing paper
“In fact GMA is widely used to assess the performance of pathology foundation models23,25,26.”
From A clinical benchmark of public self-supervised pathology foundation models (2025) · cites this paper as [23]
In the citing paper
“Self-supervised learning has become a widely used approach for pretraining foundation models on thousands to tens of thousands of images without annotations or labels, marking an important paradigm shift in the field.20,21”
From Use of artificial intelligence for liver diseases: A survey from the EASL congress 2024 (2024) · cites this paper as [20]
In the citing paper
“An increasing number of published studies show the potential of foundation models20,24; however, they are currently underrepresented in the field of liver diseases.4”
From Use of artificial intelligence for liver diseases: A survey from the EASL congress 2024 (2024) · cites this paper as [20]
In the citing paper
“we present Virchow, the largest foundation model for computational pathology to date.”
From A foundation model for clinical-grade computational pathology and rare cancers detection (2024) · cites this paper as [41]
In the citing paper
“These works demonstrate that image features produced with self-supervised learning of pathology images outperform image features trained on natural images and that performance improves with scale.”
From A foundation model for clinical-grade computational pathology and rare cancers detection (2024) · cites this paper as [41]
In the citing paper
“Despite the challenges in collecting large-scale datasets in the pathology domain, recent pioneering works have utilized datasets ranging from 30,000 to 400,000 WSIs to train foundation models ranging in size from 28 million to 307 million parameters35–42”
From A foundation model for clinical-grade computational pathology and rare cancers detection (2024) · cites this paper as [41]
In the citing paper
“Built on rich knowledge representations, these models have shown impressive performance across medical fields such as ophthalmology20, radiology21 and pathology22–25.”
From A Multimodal Vision Foundation Model for Clinical Dermatology (2025) · cites this paper as [23]
In the citing paper
“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 [23]
In the citing paper
“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 [23]
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A generalizable foundation model for analysis of human brain MRI
Tak, D., Garomsa, B. A., Zapaishchykova, A., Chaunzwa, T. L., Pardo, J. C., Ye, Z., Zielke, J., Ravipati, Y., Pai, S., Vajapeyam, S., et al. (2026) · Nature Neuroscience · doi:10.1038/s41593-026-02202-6 · Cited by 66
CAMIL: channel attention-based multiple instance learning for whole slide image classification
Mao, J., Xu, J., Tang, X., Liu, Y.-J., Zhao, H., Tian, G., & Yang, J. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf024 · Cited by 27
Systems Human Immunology and AI: Immune Setpoint and Immune Health
Lei, Y., & Tsang, J. S. (2025) · Annual Review of Immunology · doi:10.1146/annurev-immunol-090122-042631 · Cited by 27
Histopathology-based protein multiplex generation using deep learning
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“We further explored integrating features from the UNI computational pathology foundation model 43 to enhance the performance of HistoPlexer, marking one of the first attempts to apply pathology foundation models to generative tasks.”
Self-citationSun, Y., Si, Y., Zhu, C., Gong, X., Zhang, K., Chen, P., Zhang, Y., Shui, Z., Lin, T., & Yang, L. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.00969 · Cited by 26
“Prominent models include Lunit [26], UNI [12], Prov-GigaPath [58], and Virchow series [52, 65].”
Leveraging foundation and large language models in medical artificial intelligence
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Evaluating ChatGPT’s diagnostic potential for pathology images
Ding, L., Fan, L., Shen, M., Wang, Y., Sheng, K., Zou, Z., An, H., & Jiang, Z. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2024.1507203 · Cited by 25
“Chen and colleagues introduced UNI, a general-purpose self-supervised model capable of resolution-agnostic tissue classification and identifying up to 108 cancer types (30).”
Zhang, Y., Gao, Z., He, K., Li, C., & Mao, R. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103027 · Cited by 30
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Benchmarking pathology foundation models: Adaptation strategies and scenarios
Lee, J., Lim, J., Byeon, K., & Kwak, J. T. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110031 · Cited by 37
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“These patches are subsequently fed into deep neural networks for feature extraction [7], [8], [9].”
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“The UNI model [103] is a self-super-vised learning model trained on a large-scale pathology dataset, demonstrating outstanding performance across 34 computational pathology tasks, particularly in histo-pathological classification and disease subtype prediction.”
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“…by Multivariate Cox regression, which maintained the RSA model ’ s standing as an independent prognostic predictor for 5-year OS within both external cohorts (Set I: HR =2.677, 95%CI: 1.847 – 3.881, P < 0.001; Set II: HR= 2.701, 95%CI: 1.759 – 4.150, P <0.001) (Supplementary Tables 22 and 23).”
InfluentialDecoding pathology: the role of computational pathology in research and diagnostics
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“Finally, several human histopathology-specific foundation models have been created recently [97–99].”
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HIBRID: histology-based risk-stratification with deep learning and ctDNA in colorectal cancer
Loeffler, C. M. L., Bando, H., Sainath, S., Muti, H. S., Jiang, X., van Treeck, M., Reitsam, N. G., Carrero, Z. I., Meneghetti, A. R., Nishikawa, T., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-62910-8 · Cited by 26
“Moreover, weutilized a state-of-the-art foundation model for digital pathology analysis, UNI 49 .”
InfluentialMEDCO: Medical Education Copilots Based on a Multi-agent Framework
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STimage-1K4M: A histopathology image-gene expression dataset for spatial transcriptomics
Chen, J., Zhou, M., Wu, W., Zhang, J., Li, Y., & Li, D. (2024) · neural information processing systems · doi:10.52202/079017-1129 · Cited by 33
Weakly-supervised deep learning models enable HER2-low prediction from H &E stained slides
Valieris, R., Martins, L. V. C., Defelicibus, A., Bueno, A. P., Osório, C. A. B. D. T., Carraro, D. M., Dias‐Neto, E., Rosales, R., de Figueiredo, J. M. B., & da Silva, I. T. (2024) · Breast Cancer Research · doi:10.1186/s13058-024-01863-0 · Cited by 21
“The generated bag of tiles for the tumor and non-tumor groups were then used for feature extraction with the UNI model [28] (Fig.”
MethodRamesh, S., Dyer, E., Pomaville, M. M., Doytcheva, K., Dolezal, J. M., Kochanny, S. E., TerHaar, R., Mehrhoff, C. J., Patel, K., Brewer, J., et al. (2024) · npj Precision Oncology · doi:10.1038/s41698-024-00745-0 · Cited by 21
“UNI is a domain-speci fi c model that has been trained on over 100 million images from more than 100,000 diagnostic H&E-stained slides across 20 major tissue types 15 .”
MethodHMIL: Hierarchical Multi-Instance Learning for Fine-Grained Whole Slide Image Classification
Jin, C., Luo, L., Lin, H., Hou, J., & Chen, H. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3520602 · Cited by 25
“At the feature extraction stage, recent pathology foundation models which pretrained on extensive pathological datasets [59], [60], [61], offer potential for more representative feature extraction and semantic interpretation.”
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Zhang, B., Wan, Z., Luo, Y., Zhao, X., Samayoa, J. A., Zhao, W., & Wu, S. (2025) · Frontiers in Pharmacology · doi:10.3389/fphar.2025.1609079 · Cited by 28
Zhao, J., & Min, W. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf433 · Cited by 19
“Histological images are processed by a pretrained large model of pathological images (UNI) [16] to extract morphological features, projected into the same space.”
Brattoli, B., Mostafavi, M., Lee, T., Jung, W., Ryu, J., Park, S., Park, J., Pereira, S., Shin, S.-H., Choi, S., et al. (2024) · npj Precision Oncology · doi:10.1038/s41698-024-00770-z · Cited by 21
“Recent trends tend to call approaches with largetrainingsetfromdifferentdomains ‘ foundationalmodels ’ ,therefore,in this sense, our UIHC could be considered one 44 – 46 .”
Krasniqi, E., Filomeno, L., Arcuri, T., Ferretti, G., Gasparro, S., Fulvi, A., Roselli, A., D’Onofrio, L., Pizzuti, L., Barba, M., et al. (2025) · Biology Direct · doi:10.1186/s13062-025-00661-8 · Cited by 21
“Moreover, ongoing exploration and application of advanced DL architectures, including transformers, graph neural networks and foundation models [89–91], should be encouraged.”
Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline
Cheng, J., Fu, B., Ye, J., Wang, G., Li, T., Wang, H., Li, R., He, Y., Cheng, J., Li, J., et al. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.01941 · Cited by 19
“An ideal IMIS benchmark dataset should meet three core criteria: (1) Large-scale.”
Yan, R., Zhang, X., Jiang, Z., Wang, B., Bian, X., Ren, F., & Zhou, S. K. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3611531 · Cited by 20
“We also compare DINO with the state-of-the-art pathology foundation models PLIP [52] and UNI [53].”
Best Practices for the Safe Use of Large Language Models and Other Generative AI in Radiology
Yi, P. H., Haver, H. L., Jeudy, J. J., Kim, W., Kitamura, F., Oluyemi, E. T., Smith, A. D., Moy, L. P., & Parekh, V. S. (2025) · Radiology · doi:10.1148/radiol.241516 · Cited by 20
Robust and interpretable prediction of gene markers and cell types from spatial transcriptomics data
Tan, X., Mulay, O., Xie, J., MacDonald, S., Kim, T., Zhou, C., Xiong, Z., Tan, S. X., Ye, N., Reed, A. E. M., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-68487-0 · Cited by 18
Histopathological Image Classification with Cell Morphology Aware Deep Neural Networks
Ignatov, A., Yates, J., & Boeva, V. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00685 · Cited by 20
“We compared the performance of our model against CNN-based solutions previously proposed for this dataset [3,23,44]; a recent UNI [12] foundation vision transformer model pre-trained on 100K diagnostic H&E-stained WSIs; CTransPath [88] foundation trans-former model pre-trained with contrastive…”
ResultLipková, J., & Kather, J. N. (2024) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-024-00941-8 · Cited by 21
“FMs have a comprehensive understanding of the morphological patterns present in histology images, and therefore enable the training of more accurate predictive models using fewer training samples as compared to models pretrained on natural images 1 .”
EchoFM: Foundation Model for Generalizable Echocardiogram Analysis
Kim, S., Jin, P., Song, S., Chen, C., Li, Y., Ren, H., Li, X., Liu, T., & Li, Q. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3580713 · Cited by 35
“The versatility and robust capabilities of foundation models have driven active development and applications in various medical fields, including radiology [8], pathology [9], and genomics [10].”
Lu, Y., Liu, F., Yu, Y., Chen, B., Yu, W., Zou, Z., Li, K., Man, M., Ou, C., Wang, C., et al. (2025) · Cell Reports Medicine · doi:10.1016/j.xcrm.2025.102216 · Cited by 19
Self-citationAutomated cell annotation and classification on histopathology for spatial biomarker discovery
Li, Z., Mirjahanmardi, S. H., Sali, R., Eweje, F. R., Gopaulchan, M., Kloker, L. H., Zhang, X., Li, G., Jiang, Y., & Li, R. (2025) · Nature Communications · doi:10.1038/s41467-025-61349-1 · Cited by 21
Fuse, Y., Murphy, S. N., Ikari, H., Takahashi, A., Fuse, K., & Kawakami, E. (2025) · Allergology International · doi:10.1016/j.alit.2025.06.005 · Cited by 25
Multimodal Data Fusion for Whole-Slide Histopathology Image Classification
Song, Y., Roy, M., Zhong, M., Chen, L., Lin, M., & Zhang, R. (2025) · Journal of Healthcare Informatics Research · doi:10.1007/s41666-025-00212-w · Cited by 19
Advances in computational nephropathology
Hölscher, D. L., Bülow, R. D., Strauch, M., & Boor, P. (2025) · Kidney International · doi:10.1016/j.kint.2025.06.029 · Cited by 19
Uncertainty-aware ensemble of foundation models differentiates glioblastoma from its mimics
Zhao, J., Lin, S.-Y., Attias, R., Mathews, L., Engel, C., Larghero, G., Vremenko, D., Kao, T., Lee, T.-H., Wang, Y.-H., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-64249-6 · Cited by 21
“996,outperformingUNI (0.976, P <0.001) and performing comparably to Virchow2 (0.995, P =0.243); at BWH, it reached 0.987, signi fi cantly higher than UNI (0.977, P =0.025) and Virchow2(0.975, P =0.014);inVienna,itattained0.”
InfluentialFoundation models in drug discovery: Phenomenal growth today, transformative potential tomorrow?
Delile, J., Mukherjee, S., Mueller, J., Khalil, I. G., Евгеньевич, Ж. Л., & Meier, C. (2025) · Drug Discovery Today · doi:10.1016/j.drudis.2025.104518 · Cited by 23
Muneer, A., Waqas, M. M., Saad, M. B., Showkatian, E., Bandyopadhyay, R., Xu, H. M., Li, W., Chang, J., Liao, Z., Haymaker, C., et al. (2026) · Artificial Intelligence Review · doi:10.1007/s10462-026-11522-9 · Cited by 24
Hibou: A Family of Foundational Vision Transformers for Pathology
Nechaev, D., Pchelnikov, A., & Ivanova, E. (2024) · arXiv · doi:10.48550/arxiv.2406.05074 · Cited by 18
“As a more recent and advanced framework, DINOv2 has seen adoption in several notable studies, including Virchow, RudolfV, and Prov-Gigapath, among others [7, 8, 9, 10, 11, 12].”
BackgroundFOCUS: Knowledge-enhanced Adaptive Visual Compression for Few-shot Whole Slide Image Classification
Guo, Z., Xiong, C., Ma, J., Sun, Q., Feng, L., Wang, J., & Chen, H. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.01453 · Cited by 27
“These efforts involve vision-only foundation models such as GPFM [30], UNI [8], Prov-Gigapath [48], Virchow [41], and CHIEF [43], as well as multimodal models that integrate WSIs with pathology reports or genomic profiles, including CONCH [29], PLIP [19], and mSTAR [50].”
InfluentialSelf-citationAI in microbiome research: Where have we been, where are we going?
Gerber, G. K. (2024) · Cell Host & Microbe · doi:10.1016/j.chom.2024.07.021 · Cited by 17
Self-citationArtificial Intelligence for Head and Neck Squamous Cell Carcinoma: From Diagnosis to Treatment
Song, B., Yadav, I. S., Tsai, J., Madabhushi, A., & Kann, B. H. (2025) · American Society of Clinical Oncology Educational Book · doi:10.1200/edbk-25-472464 · Cited by 21
Artificial Intelligence in the Management of Heart Failure
Cheema, B. S., Hourmozdi, J., Kline, A. S., Ahmad, F. S., & Khera, R. (2025) · Journal of Cardiac Failure · doi:10.1016/j.cardfail.2025.02.020 · Cited by 16
Radio DINO: A foundation model for advanced radiomics and AI-driven medical imaging analysis
Zedda, L., Loddo, A., & Ruberto, C. D. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110583 · Cited by 23
Large-vocabulary forensic pathological analyses via prototypical cross-modal contrastive learning
Shen, C., Lian, C., Zhang, W., Wang, F., Zhang, J., Fan, S., Wei, X., Wang, G., Li, K., Mu, H., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-62060-x · Cited by 18
“Comparisons between our pro-totypical patch-level encoder and other state-of-the-art CPath foundation models (e.g., PLIP 46 , CONCH 29 , UNI 28 , and Prov-GigaPath 26 ) con fi rmeditssuperioraccuracyandgeneralizabilityinthespeci fi ctask of large-vocabulary forensic diagnosis.”
InfluentialEmerging AI approaches for cancer spatial omics
Noorbakhsh, J., pour, A. F., & Chuang, J. H. (2025) · GigaScience · doi:10.1093/gigascience/giaf128 · Cited by 19
“This architecture has been used in recent foundation models, including Virchow [ 25 ], UNI [ 30 ], and GPFM [ 31 ].”
Roetzer-Pejrimovsky, T., Nenning, K., Kiesel, B., Klughammer, J., Rajchl, M., Baumann, B., Langs, G., & Wöehrer, A. (2024) · GigaScience · doi:10.1093/gigascience/giae057 · Cited by 16
“While foundation models for computational pathology r epr esent a significant advancement, the full potential of their generalizability across differ ent or gans and disease types r equir es thor ough e v aluation [ 54– 56 ].”
Liu, B., Polack, M., Coudray, N., Claudio Quiros, A., Sakellaropoulos, T., Le, H., Karimkhan, A., Crobach, A. S. L. P., van Krieken, J. H. J. M., Yuan, K., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-57541-y · Cited by 25
“Survival-favoringHPCs(5,31,34, 26, 18, 27) were associated with enrichment in pathways of cell cycle, signaling, DNA repair, and growth (i.e. G2/M checkpoint, E2F targets, Myc targets, and mTORC1 signaling), while survival-unfavorable HPCs (12, 41, 38, 14, 24, 40, 20) linked to a depletion of those…”
InfluentialThe Role of the Clinical Laboratory in Diagnostic Stewardship and Population Health
Crawford, J. M., Shotorbani, K. R., & Swanson, K. (2025) · The Journal of Applied Laboratory Medicine · doi:10.1093/jalm/jfae113 · Cited by 15
Scaling up self-supervised learning for improved surgical foundation models
Jaspers, T. J. M., de Jong, R. L. P. D., Li, Y., Kusters, C. H. J., Bakker, F. H. A., van Jaarsveld, R. C., Kuiper, G. M., van Hillegersberg, R., Ruurda, J. P. H., Brinkman, W. M., et al. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103873 · Cited by 24
Validation of histopathology foundation models through whole slide image retrieval
Alfasly, S., Alabtah, G., Hemati, S., Kalari, K. R., García, J. J., & Tizhoosh, H. R. (2025) · Scientific Reports · doi:10.1038/s41598-025-88545-9 · Cited by 18
“UNI is a self-supervised vision encoder based on a Vision Transformer Large (ViT-L) architecture 6 .”
UniSAL: Unified Semi-supervised Active Learning for histopathological image classification
Zhong, L., Qian, K., Liao, X., Huang, Z., Liu, Y., Zhang, S., & Wang, G. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103542 · Cited by 16
Aggarwal, A., Jana, M., Singh, A., Dam, T., Maurya, H., Pathak, T. B., Oršulić, S., Yang, K., Chute, D. J., Bishop, J. A., et al. (2025) · European Journal of Cancer · doi:10.1016/j.ejca.2025.115390 · Cited by 15
Şeker, M., Niazi, M. K. K., Chen, W., Frankel, W. L., & Gürcan, M. N. (2025) · Cancers · doi:10.3390/cancers17071245 · Cited by 18
“Chen et al. (2024) presented UNI, a versatile self-supervised model tailored for pathology, pretrained on a massive dataset of over 100 million images extracted from more than 100,000 diagnostic H&E-stained WSIs (>77 TB of data), representing 20 major tissue types [38].”
Integrating multimodal cancer data using deep latent variable path modelling
Ing, A., Andrades, Á., Cosenza, M. R., & Korbel, J. O. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-01052-4 · Cited by 18
“In the past couple of years, a number of large-scale ‘foundation models’ have been trained on histological images from cancer 33,34 .”
Cheng, Y., Lama, N., Chen, M., Amidi, E., Ramzanpour, M., Rahman, M. S., Xiu, J., Helmstetter, A., Dickman, L. R., Ribeiro, J. R., et al. (2025) · Communications Medicine · doi:10.1038/s43856-025-01045-9 · Cited by 16
“Tofurthervalidateourapproach, weperformedadditionalexperimentsusingseveralnewlyreleasedpathol-ogyfoundationmodels — Virchow 61 , Virchow2 62 , UNI 63 , and H-Optimus-0 64 — asfeatureencoders.”
Unsupervised Foundation Model-Agnostic Slide-Level Representation Learning
Lenz, T., Neidlinger, P., Ligero, M., Wölflein, G., van Treeck, M., & Kather, J. N. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.02869 · Cited by 24
“We use fe n to refer to the n th FM, fe n ∈ { CTP , UNI , V2 , H0 } denoting CTransPath [41], UNI [4], Virchow2 [47], and H-optimus-0 [32], respectively.”
Doerfler, R., Chen, J., Kim, C., Smith, J. D., Harris, M. K., Singh, K. B., Isett, B. R., Dadey, R., Brown, D. D., Lee, A. V., et al. (2025) · Oral Oncology · doi:10.1016/j.oraloncology.2025.107742 · Cited by 19
Discovery of predictive biomarkers for cancer therapy through computational approaches
Wang, X., Nguyen, J., Nader, K. M., Miihkinen, M., Wall, P., Singhal, A., Bedard, P. L., Ideker, T., Aittokallio, T. A., & Haibe‐Kains, B. (2026) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-025-01109-8 · Cited by 24
Bridging modalities with AI: a review of AI advances in multimodal biomedical imaging
Doan, L. M. T., Shahhosseini, K., Verma, S., Marefat, A., Locicero, G., Verma, S., Angione, C., & Occhipinti, A. (2026) · Communications Engineering · doi:10.1038/s44172-026-00602-x · Cited by 18
“Integrationoftextwith single imaging modalities, such as histopathology 125,127,128 or radiology 129 – 131 , remains an active area of research and continues to yield important advances.”
Immunotherapy at all stages of hepatocellular carcinoma
Pinato, D. J., Fulgenzi, C. A. M., & D’Alessio, A. (2024) · Nature Medicine · doi:10.1038/s41591-024-02828-8 · Cited by 15
Angeloni, M., Rizzi, D., Schoen, S., Caputo, A., Merolla, F., Hartmann, A., Ferrazzi, F., & Fraggetta, F. (2025) · Genome Medicine · doi:10.1186/s13073-025-01484-y · Cited by 16
DeepSpot: Leveraging Spatial Context for Enhanced Spatial Transcriptomics Prediction from H&E ImagesPreprint
Nonchev, K., Dawo, S., Siliņa, K., Moch, H., Andani, S., Koelzer, V. H., & Rätsch, G. (2025) · medRxiv · doi:10.1101/2025.02.09.25321567 · Cited by 20
“While Jaume and Doucet et al. [14] utilized recent pathology foundation models (e.g., UNI [20], Phikon [21]), pre-trained on extensive H&E datasets, to extract tile spot features, they focused on predicting only 50 genes using either ridge regression or random forest.”
Deciphering cell to cell spatial relationship for pathology images using SpatialQPFs
Xiao, L. (2024) · Scientific Reports · doi:10.1038/s41598-024-81383-1 · Cited by 16
“Notably, recent advances in foundational models, such as UNI 51 and Gigapath 52 , are transforming digital pathology.”
Rethinking cancer of unknown primary: from diagnostic challenge to targeted treatment
Pouyiourou, M., Bochtler, T., Pauli, C., Moch, H., Brobeil, A., Pantel, K., Stenzinger, A., & Krämer, A. (2025) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-025-01060-8 · Cited by 14
“…pathology and are able to identify cancer types based on histological data derived from haematoxylin and eosin-stained whole-slide images 107,108 ; the reported predictive accuracy for several common cancer types is impressive, although it remains limited for rare cancer types, which might…”
Artificial Intelligence and Network Medicine: Path to Precision Medicine
Altucci, L., Badimón, L., Balligand, J., Baumbach, J., Catapano, A. L., Cheng, F., DeMeo, D. L., Gupta, R. M., Hacker, M., Liu, Y., et al. (2025) · NEJM AI · doi:10.1056/aira2401229 · Cited by 17
SurGen: 1020 H&E-stained whole-slide images with survival and genetic markers
Myles, C., Um, I. H., Marshall, C., Harris‐Birtill, D., & Harrison, D. J. (2025) · GigaScience · doi:10.1093/gigascience/giaf086 · Cited by 18
“For this study, we employed the UNI foundation model [ 46 ] for feature extraction from WSIs.”
InfluentialA lung CT vision foundation model facilitating disease diagnosis and medical imaging
Gao, Z., Zhang, G., Liang, H., Liu, J., Ma, L., Wang, T., Guo, Y., Chen, Y., Yan, Z., He, J., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-66620-z · Cited by 23
“For example, AI integrates the outcomes of lung CT examination with clinical symptoms, exposure history, and laboratory tests to quickly diagnose COVID-19 in patients 8,29,30 .”
Scaling medical AI across clinical contexts
Li, M. M., Reis, B. Y., Rodman, A. M., Cai, T., Dagan, N., Balicer, R. D., Loscalzo, J., Kohane, I. S., & Žitnik, M. (2026) · Nature Medicine · doi:10.1038/s41591-025-04184-7 · Cited by 24
Shamai, G., Cohen, S., Binenbaum, Y., Sabo, E., Cretu, A., Mayer, C., Barshack, I., Goldman, T., Bar-Sela, G., Polonia, A., et al. (2026) · The Lancet Oncology · doi:10.1016/s1470-2045(25)00727-2 · Cited by 23
Luan, H., Yang, K., Hu, T., Hu, J., Liu, S., Li, R., He, J., Yan, R., Guo, X., Qian, N., et al. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.107578 · Cited by 15
Distilling Foundation Models for Robust and Efficient Models in Digital Pathology
Filiot, A., Dop, N., Tchita, O., Riou, A., Dubois, R., Peeters, T., Valter, D., Scalbert, M., Saillard, C., Robin, G., et al. (2026) · Lecture Notes in Computer Science · doi:10.1007/978-3-032-04981-0_16 · Cited by 17
Mallya, M., Mirabadi, A. K., Farnell, D., Farahani, H., & Bashashati, A. (2025) · Discover Oncology · doi:10.1007/s12672-025-01973-x · Cited by 20
Moving the dial on diagnostics: an update from the Lancet Commission on diagnostics
Horton, S., Wilson, M. L., Cheung, A., DeStigter, K. K., Kohli, M., Sayed, S., Schroeder, L. F., Sullivan, R. J., Tan, B.-S., Alooh, M., et al. (2025) · The Lancet · doi:10.1016/s0140-6736(25)00804-9 · Cited by 13
Chest X-Ray Foundation Model With Global and Local Representations Integration
Yang, Z., Xu, X., Zhang, J., Wang, G., Kalra, M. K., & Yan, P. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3581907 · Cited by 17
“Studies have demonstrated that scaling foundation models in data and model sizes can achieve performance increases across a wide array of downstream tasks [7], [20], [28].”
InfluentialFmH2ST: foundation model-based spatial transcriptomics generation from histological images
Wang, Y., Wang, J., Xu, Y., Liu, N., Liu, B., Li, Y., & Yu, G. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf865 · Cited by 13
Self-citationTraining State-of-the-Art Pathology Foundation Models with Orders of Magnitude Less Data
Karasikov, M., van Doorn, J., Känzig, N., Cesur, M. E., Horlings, H. M., Berke, R., Tang, F., & Otálora, S. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04984-1_55 · Cited by 13
Sucre, A., Sánchez, X. C., Perez-Herrera, L. V., Vivanco, M. D., González, M. J. G., López‐Linares, K., Calvo, B., & Garín-Muga, A. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.10.038 · Cited by 17
“The MIL framework included a feature extractor where two encoders were tested: ResNet-50 general-purpose model [17], and UNI pathology foundation model [18]; then, an attention mechanism for slide-level aggregation; and finally, a fully connected layer to predict survival categories.”
Yuan, M., Jin, K., Yan, H., Schroeder, A. R., Luo, C., Yao, S., Dumoulin, B., Levinsohn, J. L., Luo, T., Clemenceau, J. R. V., et al. (2025) · Nature Cell Biology · doi:10.1038/s41556-025-01811-w · Cited by 16
Shao, X., Chen, H., Zhao, F., Magson, K., Chen, J., Li, P., Wang, J., & Sasaki, J. (2025) · Marine Pollution Bulletin · doi:10.1016/j.marpolbul.2025.119054 · Cited by 13
Self-citationHuang, Y., Zhao, W., Zhang, Z., Chen, Y., Fu, Y., Wu, F., Jiang, Y., Liang, L., Wang, S., & Yu, L. (2025) · Nature Communications · doi:10.1038/s41467-025-66300-y · Cited by 22
“To address this, future work could explore cross-modal distillation techniques or develop a vision-onlyadaptationmodule, enabling FLEX to generalize to a broader range of architectures.”
Panayides, A. S., Chen, H., Filipović, N., Geroski, T., Hou, J., Lekadir, K., Kostas Marias, A., Matsopoulos, G. K., Papanastasiou, G., Sarder, P., et al. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3649496 · Cited by 14
“Key advances include radiology foundation models [31], [46] trained on 2D and 3D scans, pathology foundation models [13], [32], [47], [48] for whole slide image analysis, and retinal foundation models [29] that enhance low-data disease detection.”
Learning neuroimaging models from health system-scale data
Lyu, Y., Harake, E. S., Chowdury, A., Banerjee, S., Gologorsky, R., Liu, S., Meißner, A.-K., Rao, A., Zhao, C., Kondepudi, A., et al. (2026) · Nature Biomedical Engineering · doi:10.1038/s41551-025-01608-0 · Cited by 16
“Ensuring algorithmic fairness is critical for medical AI models to achieve equitable performance across sensitive attributes and mitigate existing disparities 27 .”
Ten challenges and opportunities in computational immuno-oncology
Bao, R., Hutson, A. D., Madabhushi, A., Jönsson, V. D., Rosario, S. R., Barnholtz‐Sloan, J. S., Fertig, E. J., Marathe, H. G., Harris, L. N., Altreuter, J., et al. (2024) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2024-009721 · Cited by 14
“93 Furthermore, the development of CONCH 94 and UNI, 95 pathology generative AI models trained on histopathology slides from academic medical centers and industry collaborators, has demonstrated high accuracy in recognizing cell types and generating pathology reports that resemble expert opinions.”
Kilim, O., Olar, A., Biricz, A., Madaras, L., Pollner, P., Szállási, Z., Sztupinszki, Z. M., & Csabai, I. (2025) · npj Precision Oncology · doi:10.1038/s41698-025-00808-w · Cited by 13
“Ongoing work aims to overcome these issues with the developmentofopen-sourcefoundationmodels 23,42 anddomainadaptation protocols.”
Longitudinal Risk Prediction for Pediatric Glioma with Temporal Deep Learning
Tak, D., Garomsa, B. A., Zapaishchykova, A., Ye, Z., Vajapeyam, S., Mahootiha, M., Pardo, J. C. C., Smith, C., Familiar, A., Chaunzwa, T. L., et al. (2025) · NEJM AI · doi:10.1056/aioa2400703 · Cited by 15
A foundation model for generalized brain MRI analysisPreprint
Tak, D., Garomsa, B. A., Chaunzwa, T. L., Zapaishchykova, A., Pardo, J. C. C., Ye, Z., Zielke, J., Ravipati, Y., Vajapeyam, S., Mahootiha, M., et al. (2024) · medRxiv · doi:10.1101/2024.12.02.24317992 · Cited by 13
A generalizable deep learning system for cardiac MRI
Shad, R., Zakka, C., Kaur, D., Mathur, M., Fong, R., Cho, J., Filice, R. W., Mongan, J., Kallianos, K., Khandwala, N., et al. (2026) · Nature Biomedical Engineering · doi:10.1038/s41551-026-01637-3 · Cited by 16
Predicting Nottingham grade in breast cancer digital pathology using a foundation model
Kim, J. S., Lee, J. H., Yeon, Y., An, D., Kim, S. J., Noh, M., & Lee, S. (2025) · Breast Cancer Research · doi:10.1186/s13058-025-02019-4 · Cited by 18
Metadata
- Authors
- Chen, R. J., Ding, T., Lu, M. Y., Williamson, D. F. K., Jaume, G., Song, A. H., Chen, B., Zhang, A., Shao, D., Shaban, M., Williams, M., Oldenburg, L., Weishaupt, L. L., Wang, J. J., Vaidya, A., Le, L. P., Gerber, G., Sahai, S., Williams, W., & Mahmood, F.
- Affiliations
- Broad Institute (US)
- Brigham and Women's Hospital (US)
- Harvard University (US)
- Corresponding author
- Mahmood, Faisal
- ORCID
- Venue
- Nature Medicine
- Published
- 2024-03
- Volume
- 30
- Issue
- 3
- Pages
- 850-862
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- PMC
- PMC11403354
- OpenAlex
- W4392947521
- Semantic Scholar
- cac496ddebbff6bbba78242780cde01bd962bfc9
- Cited by
- 1,560
- References
- 170
- Citations by year
- 2026: 789 · 2025: 613 · 2024: 154
- FWCI
- 229.15
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Nature Medicine (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Human Genome Research Institute (T32HG002295)
- National Cancer Institute (T32CA251062)
- National Institute of General Medical Sciences (R35GM149270)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- computational pathology, whole slide images, self-supervised learning, foundation models, unsupervised feature learning, tissue classification, few-shot learning, cancer subtyping, histopathology, transfer learning
- MeSH terms
- Artificial Intelligence, Workflow
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