A visual-language foundation model for computational pathology
Open accessAbstract
The accelerated adoption of digital pathology and advances in deep learning have enabled the development of robust models for various pathology tasks across a diverse array of diseases and patient cohorts. However, model training is often difficult due to label scarcity in the medical domain, and a model’s usage is limited by the specific task and disease for which it is trained. Additionally, most models in histopathology leverage only image data, a stark contrast to how humans teach each other and reason about histopathologic entities. We introduce CONtrastive learning from Captions for Histopathology (CONCH), a visual-language foundation model developed using diverse sources of histopathology images, biomedical text and, notably, over 1.17 million image–caption pairs through task-agnostic pretraining. Evaluated on a suite of 14 diverse benchmarks, CONCH can be transferred to a wide range of downstream tasks involving histopathology images and/or text, achieving state-of-the-art performance on histology image classification, segmentation, captioning, and text-to-image and image-to-text retrieval. CONCH represents a substantial leap over concurrent visual-language pretrained systems for histopathology, with the potential to directly facilitate a wide array of machine learning-based workflows requiring minimal or no further supervised fine-tuning. Developed using diverse sources of histopathology images, biomedical text and over 1.17 million image–caption pairs, evaluated on a suite of 14 diverse benchmarks, a visual-language foundation model achieves state-of-the-art performance on a wide array of clinically relevant pathology tasks.
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Retrieving a similar WSI via slide search or pathology reports through cross-modal report search typically requires specialized algorithms to bridge the gap between fine-grained patch embeddings and slide-level information, introducing hurdles to clinical adoption.
“Similarly, given a diagnostically challenging WSI, retrieving a similar WSI via slide search5,46–53 or pathology reports through cross-modal report search10,54–56 typically requires specialized algorithms to bridge the gap between fine-grained patch embeddings and slide-level information, introducing hurdles to clinical adoption.”
From A multimodal whole-slide foundation model for pathology (2025) · cites this paper as [10]
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredCONCHv1.5 was chosen for feature extraction because it was pretrained on histology regions with diverse stains and tissue types, including FFPE, frozen tissue and IHC, yielding region features robust against diverse tissue processing protocols.
“The choice of CONCHv1.5 for feature extraction was due to the fact that the model was pretrained on histology regions with diverse stains and tissue types, including FFPE, frozen tissue and IHC, thereby yielding region features that are robust against diverse tissue processing protocols.”
From A multimodal whole-slide foundation model for pathology (2025) · cites this paper as [10]
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredIncreasing the patch size from 256 × 256 pixels to 512 × 512 pixels reduces the sequence length by four without impacting representation quality, leveraging the robustness of patch-level foundation models to higher resolutions.
“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 [10]
The Methods (WSI processing; page unavailable) support 256 × 256 pixels as a conventional tile size for slide-level classification. They do not report increasing that size to 512 × 512, a fourfold reduction in sequence length, or preserved representation quality at the higher resolution. The paper instead says tiles were resized to 224 × 224 for feature extraction. (LLM, conf=0.84)
Experimental — accuracy not yet measuredCONCH can create effective representations for non-H&E-stained images, such as immunohistochemistry and special stains, without relying on large public histology slide collections.
“enabling it to create effective representations for non–H&E (hematoxylin and eosin)-stained images, such as immunohistochemistry and special stains, without relying on large public histology slide collections such as The Cancer Genome Atlas, Pancreatic Cancer AI Platform, and Genotype-Tissue Expression.”
From Multimodal Large Language Models in Health Care: Applications, Challenges, and Future Outlook. (2024) · cites this paper as [141]
The Methods (Dataset curation; page unavailable) say CONCH’s pretraining data included immunohistochemistry and special-stain images, but explicitly state that downstream evaluation covered only human histopathology and H&E tasks. The Methods (Pretraining unimodal encoders; page unavailable) describe an in-house slide collection for image-encoder pretraining. Thus the paper supports inclusion of non-H&E images and pretraining without the named public slide collections, but does not establish that CONCH creates effective representations for non-H&E images. (LLM, conf=0.87)
Experimental — accuracy not yet measuredCONCH is a vision-language M-LLM specifically designed for computational histopathology.
“CONCH is a vision-language M-LLM specifically designed for computational histopathology.”
From Multimodal Large Language Models in Health Care: Applications, Challenges, and Future Outlook. (2024) · cites this paper as [141]
The Abstract (page unavailable) identifies CONCH as a visual-language foundation model developed for histopathology. Methods > Visual-language pretraining (page unavailable) describes its image encoder and GPT-style text encoder and multimodal decoder. These passages support its visual-language design and pathology focus, but do not establish the more specific classification of CONCH as a multimodal large language model (M-LLM). (LLM, conf=0.82)
Experimental — accuracy not yet measured
Weakly-supervised deep learning models can be trained to perform various downstream computational pathology tasks such as tumor subtyping and prognosis without any region or pixel-level annotations.
“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 [34]
The Results section (‘Few-shot classification with task-specific supervised learning’; no page supplied) says the authors trained weakly supervised slide-level classification models using slide-level labels, and the paper evaluates tumor subtyping. This supports training without region- or pixel-level labels for those classification tasks. The passages do not establish prognosis as a task performed by those weakly supervised models. (LLM, conf=0.84)
Experimental — accuracy not yet measuredCoCa is a state-of-the-art visual-language foundation model pretraining method and is used for both stages of vision-language alignment.
“Following the success of previous studies10, we use CoCa95, a state-of-the-art visual-language foundation model pretraining method, for both stages of vision-language alignment.”
From A multimodal whole-slide foundation model for pathology (2025) · cites this paper as [10]
Contrastive vision language models in pathology, such as PLIP and CONCH, align text and images at the patch-level.
“Contrastive vision language models in pathology, such as PLIP12 and CONCH13, align text and images at the patch-level.”
From Generating dermatopathology reports from gigapixel whole slide images with HistoGPT (2025) · cites this paper as [13]
Contrastive vision language models in pathology are zero-shot learners.
“They are zero-shot learners, i.e., they can solve downstream tasks for which they have not been trained (e.g., cancer subtyping).”
From Generating dermatopathology reports from gigapixel whole slide images with HistoGPT (2025) · cites this paper as [13]
CONCH is pretrained on the largest histopathology-specific vision-language data set.
“It is pretrained on the largest histopathology-specific vision-language data set”
From Multimodal Large Language Models in Health Care: Applications, Challenges, and Future Outlook. (2024) · cites this paper as [141]
PLIP and CONCH were pretrained on paired pathology images and text descriptions using contrastive learning methods.
“PLIP [194] and CONCH [195] were pretrained on paired pathology images and text descriptions using contrastive learning methods.”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [195]
Several studies have developed visual-language foundation models using paired histopathology images and captions.
“Several studies have developed visual–language foundation models using paired histopathology images and captions11,12.”
From A visual–omics foundation model to bridge histopathology with spatial transcriptomics (2025) · cites this paper as [11]
Existing dual-modality foundation models in computational biology primarily combine images with textual descriptions, proving their utility in histopathology annotation and analysis.
“Existing dual-modality foundation models in computational biology11,12 primarily combine images with textual descriptions, proving their utility in histopathology annotation and analysis.”
From A visual–omics foundation model to bridge histopathology with spatial transcriptomics (2025) · cites this paper as [11]
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Yottixel – An Image Search Engine for Large Archives of Histopathology Whole Slide Images
Kalra, S., Tizhoosh, H. R., Choi, C., Shah, S., Diamandis, P., Campbell, C. J. V., & Pantanowitz, L. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101757 · Cited by 139 · see citing works
Fast and scalable search of whole-slide images via self-supervised deep learning
Chen, C., Lu, M. Y., Williamson, D. F. K., Chen, T. Y., Schaumberg, A. J., & Mahmood, F. (2022) · Nature Biomedical Engineering · doi:10.1038/s41551-022-00929-8 · Cited by 130 · see citing works
Self-citationVisual Language Pretrained Multiple Instance Zero-Shot Transfer for Histopathology Images
Lu, M. Y., Chen, B., Zhang, A., Williamson, D. F. K., Chen, R. J., Ding, T., Le, L. P., Chuang, Y.-S., & Mahmood, F. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01893 · Cited by 122 · see citing works
Self-citationCLIP-Adapter: Better Vision-Language Models with Feature AdaptersPreprint
Gao, P., Geng, S., Zhang, R., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.04544 · Cited by 113 · see citing works
Zhang, S., Xu, Y., Usuyama, N., Xu, H., Bagga, J., Tinn, R., Preston, S., Rao, R. P. N., Wei, M., Valluri, N., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2303.00915 · Cited by 102 · see citing works
Da, Q., Huang, X., Li, Z., Zuo, Y., Zhang, C., Liu, J., Chen, W., Li, J., Xu, D., Hu, Z., et al. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102485 · Cited by 98 · see citing works
Pan-cancer computational histopathology reveals mutations, tumor composition and prognosisPreprint
Fu, Y., Jung, A. W., Viñas, R., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/813543 · Cited by 95 · see citing works
Huang, Z., Shao, W., Han, Z., Alkashash, A. M., la Sancha, C. D., Parwani, A. V., Nitta, H., Hou, Y., Wang, T., Salama, P., et al. (2023) · npj Precision Oncology · doi:10.1038/s41698-023-00352-5 · Cited by 90 · see citing works
Self-citationSelf-supervised attention-based deep learning for pan-cancer mutation prediction from histopathology
Saldanha, O. L., Loeffler, C. M. L., Niehues, J., van Treeck, M., Seraphin, T. P., Hewitt, K. J., Çifçi, D., Veldhuizen, G. P., Ramesh, S., Pearson, A. T., et al. (2023) · npj Precision Oncology · doi:10.1038/s41698-023-00365-0 · Cited by 86 · see citing works
Pan-cancer image-based detection of clinically actionable genetic alterationsPreprint
Kather, J. N., Heij, L. R., Grabsch, H. I., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/833756 · Cited by 80 · see citing works
Multiple Instance Captioning: Learning Representations from Histopathology Textbooks and Articles
Gamper, J., & Rajpoot, N. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01628 · Cited by 75 · see citing works
The Digital Brain Tumour Atlas, an open histopathology resource
Roetzer-Pejrimovsky, T., Moser, A., Atli, B., Vogel, C., Mercea, P. A., Prihoda, R., Gelpí, E., Haberler, C., Höftberger, R., Hainfellner, J. A., et al. (2022) · Scientific Data · doi:10.1038/s41597-022-01157-0 · Cited by 70 · see citing works
Vision-Language Transformer for Interpretable Pathology Visual Question Answering
Naseem, U., Khushi, M., & Kim, J. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3163751 · Cited by 65 · see citing works
Schaumberg, A. J., Juarez-Nicanor, W. C., Choudhury, S. J., Guerra, L., Pritt, B. S., Pozuelo, M. P., Sánchez, R. S., Ho, K., Zahra, N., Sener, B. D., et al. (2020) · Modern Pathology · doi:10.1038/s41379-020-0540-1 · Cited by 55 · see citing works
Towards Visual Question Answering on Pathology Images
He, X., Cai, Z., Wei, W., Zhang, Y., Mou, L., Xing, E. P., & Xie, P. (2021) · Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2021.acl-short.90 · Cited by 55 · see citing works
Quilt-1M: One Million Image-Text Pairs for Histopathology
Ikezogwo, W. O., Seyfioglu, M. S., Ghezloo, F., Geva, D. S. C., Mohammed, F. S., Anand, P. K., Krishna, R., & Shapiro, L. (2023) · arXiv · doi:10.48550/arxiv.2306.11207 · Cited by 55 · see citing works
Self-Learning for Weakly Supervised Gleason Grading of Local Patterns
Silva-Rodríguez, J., Colomer, A., Dolz, J., & Naranjo, V. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3061457 · Cited by 45 · see citing works
Zhu, L., Shi, H., Wei, H., Wang, C., Shi, S., Zhang, F., Yan, R., Liu, Y., He, T., Wang, L., et al. (2022) · EBioMedicine · doi:10.1016/j.ebiom.2022.104426 · Cited by 39 · see citing works
FLAVA: A Foundational Language And Vision Alignment ModelPreprint
Singh, A., Hu, R., Goswami, V., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2112.04482 · Cited by 36 · see citing works
Uni-Perceiver v2: A Generalist Model for Large-Scale Vision and Vision-Language Tasks
Li, H., Zhu, J., Jiang, X., Zhu, X., Li, H., Yuan, C., Wang, X., Qiao, Y., Wang, X., Wang, W., et al. (2023) · 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/cvpr52729.2023.00264 · Cited by 30 · see citing works
WSSS4LUAD: Grand Challenge on Weakly-supervised Tissue Semantic Segmentation for Lung AdenocarcinomaPreprint
Han, C., Pan, X., Yan, L., Lin, H., Li, B., Yao, S., Lv, S., Shi, Z., Mai, J., Lin, J., et al. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2204.06455 · Cited by 27 · see citing works
Yottixel -- An Image Search Engine for Large Archives of Histopathology Whole Slide ImagesPreprint
Kalra, S., Choi, C. Q., Shah, S., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.08748 · Cited by 19 · see citing works
PathNarratives: Data annotation for pathological human-AI collaborative diagnosis
Zhang, H., He, Y., Wu, X., Huang, P., Qin, W., Wang, F., Ye, J., Huang, X., Liao, Y., Chen, H., et al. (2023) · Frontiers in Medicine · doi:10.3389/fmed.2022.1070072 · Cited by 19 · see citing works
Riasatian, A., Babaie, M., Maleki, D., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.07903 · Cited by 12 · see citing works
Visual Language Pretrained Multiple Instance Zero-Shot Transfer for Histopathology ImagesPreprint
Lu, M. Y., Chen, B., Zhang, J. A., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2306.07831 · Cited by 10 · see citing works
Self-citationHuo, X., Ong, K. H., Lau, K. W., Golé, L., Tan, C. L., Zhang, C., Zhang, Y., Zhu, X., Li, L., Han, H., et al. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4172090 · Cited by 6 · see citing works
When is a Foundation Model a Foundation ModelPreprint
Alfasly, S., Nejat, P., Hemati, S., Khan, J., Lahr, I., Alsaafin, A., Shafique, A., Comfere, N. I., Murphree, D. H., Meroueh, C., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2309.11510 · Cited by 6 · see citing works
The Digital Brain Tumour Atlas, an open histopathology resource
Roetzer-Pejrimovsky, T., Moser, A. C., Atli, B., Vogel, C., Mercea, P. A., Prihoda, R., Gelpí, E., Haberler, C., Höftberger, R., Hainfellner, J. A., et al. (2022) · EBRAINS · doi:10.25493/wq48-zgx · Cited by 5 · see citing works
Uni-Perceiver v2: A Generalist Model for Large-Scale Vision and Vision-Language TasksPreprint
Li, H., Zhu, J., Jiang, X., et al. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2211.09808 · Cited by 3 · see citing works
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Sorted by citations
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
The TRIPOD-LLM reporting guideline for studies using large language models
Gallifant, J., Afshar, M., Ameen, S., Aphinyanaphongs, Y., Chen, S., Cacciamani, G., Demner-Fushman, D., Dligach, D., Daneshjou, R., Fernandes, C., et al. (2025) · Nature Medicine · doi:10.1038/s41591-024-03425-5 · Cited by 615 · see citing works
“Of note, this guideline was developed with text-only LLMs in mind; however, advances in multimodal models incorporating LLMs, such as vision-language models 47 , are now rapidly emerging — illustrating the need for rapid, nimble approaches for reporting guidelines.”
A multimodal generative AI copilot for human pathology
Lu, M. Y., Chen, B., Williamson, D. F. K., Chen, R. J., Zhao, M., Chow, A. K., Ikemura, K., Kim, A., Pouli, D., Patel, A., et al. (2024) · Nature · doi:10.1038/s41586-024-07618-3 · Cited by 461 · see citing works
Self-citationMultimodal Large Language Models in Health Care: Applications, Challenges, and Future Outlook.
AlSaad, R., Abd-alrazaq, A., Boughorbel, S., Ahmed, A., Renault, M.-A., Damseh, R., & Sheikh, J. (2024) · Journal of medical Internet research · doi:10.2196/59505 · Cited by 349 · see citing works
“In this section, we present a use case that demonstrates the practical application of M-LLMs in health care using the Contrastive Learning From Captions for Histopathology (CONCH) model [141].”
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
“A key feature of a foundation model is its ability to perform downstream tasks without further training, that is, zero-shot learning capability 15,16 .”
InfluentialVision-language models for medical report generation and visual question answering: a review
Hartsock, I., & Rasool, G. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1430984 · Cited by 257 · 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
“The text encoder architecture is a decoder-only transformer identical to the architecture used by the original CLIP paper 23 and has an input context length of 77 tokens.”
MethodA generalist medical language model for disease diagnosis assistance
Liu, X., Liu, H., Yang, G., Jiang, Z., Cui, S., Zhang, Z., Wang, H., Tao, L., Sun, Y., Song, Z., et al. (2025) · Nature Medicine · doi:10.1038/s41591-024-03416-6 · Cited by 183 · see citing works
“VLMs have shown promise in fields such as pathology, radiology and echocardiography 43–46 .”
A multimodal whole-slide foundation model for pathology
Ding, T., Wagner, S. J., Song, A. H., Chen, R. J., Lu, M. Y., Zhang, A., Vaidya, A. J., Jaume, G., Shaban, M., Kim, A., et al. (2025) · Nature Medicine · doi:10.1038/s41591-025-03982-3 · Cited by 171 · see citing works
“…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 resolutions 9,10,87 .”
InfluentialSelf-citationFoundation 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
“CONCH [195] utilized an equal-weighted combination of the image-text contrastive loss and the cap-tioning loss following [196].”
A visual–omics foundation model to bridge histopathology with spatial transcriptomics
Chen, W., Zhang, P., Tran, T. N., Xiao, Y., Li, S., Shah, V. V., Cheng, H., Brannan, K. W., Youker, K., Lai, L., et al. (2025) · Nature Methods · doi:10.1038/s41592-025-02707-1 · Cited by 118 · see citing works
“However, several biomedical multimodal foundation models were efficiently trained on million-scale datasets by removing duplicates and noise 11,12,67 , a strategy we used to optimize training efficiency.”
Digital twins as global learning health and disease models for preventive and personalized medicine
Li, X., Loscalzo, J., Mahmud, A. K. M. F., Aly, D. M., Rzhetsky, A., Žitnik, M., & Benson, M. (2025) · Genome Medicine · doi:10.1186/s13073-025-01435-7 · Cited by 124 · 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
Marra, A., Morganti, S., Pareja, F. G., Campanella, G. S. V., Bibeau, F., Fuchs, T. J., Loda, M. F., Parwani, A. V., Scarpa, A., Reis-Filho, J. S., et al. (2025) · Annals of Oncology · doi:10.1016/j.annonc.2025.03.006 · Cited by 108 · see citing works
Self-citationA Comprehensive Survey of Foundation Models in Medicine
Khan, W., Leem, S., See, K. B., Wong, J. K., Zhang, S., & Fang, R. (2025) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2025.3531360 · Cited by 107 · see citing works
Yang, 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
Machine learning methods for histopathological image analysis: updates in 2024
Komura, D., Ochi, M., & Ishikawa, S. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.12.033 · Cited by 76 · see citing works
Artificial intelligence in digital pathology — time for a reality check
Aggarwal, A., Bharadwaj, S., Corredor, G., Pathak, T. B., Badve, S., & Madabhushi, A. (2025) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-025-00991-6 · Cited by 78 · see citing works
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
“Following previous studies 11,33 , we used Recall@K as the metric for cross-modal retrieval.”
Has multimodal learning delivered universal intelligence in healthcare? A comprehensive survey
Lin, Q., Zhu, Y., Mei, X., Huang, L., Ma, J., He, K., Peng, Z., Cambria, E., & Feng, M. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102795 · Cited by 76 · see citing works
Morphological Prototyping for Unsupervised Slide Representation Learning in Computational Pathology
Song, A. H., Chen, R. J., Ding, T., Williamson, D. F. K., Jaume, G., & Mahmood, F. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.01099 · Cited by 79 · see citing works
Self-citationChallenges in AI-driven Biomedical Multimodal Data Fusion and Analysis
Liu, J., Cen, X., Yi, C., Wang, F.-A., Ding, J., Cheng, J., Wu, Q., Gai, B., Zhou, Y., He, R., et al. (2025) · Genomics, Proteomics & Bioinformatics · doi:10.1093/gpbjnl/qzaf011 · Cited by 75 · see citing works
“PLIP [194] and CONCH [195] were pretrained on paired pathology images and text descriptions using contrastive learning meth-ods.”
Benchmarking foundation models as feature extractors for weakly supervised computational pathology
Neidlinger, P., El Nahhas, O. S. M., Muti, H. S., Lenz, T., Hoffmeister, M., Brenner, H., van Treeck, M., Langer, R., Dislich, B., Behrens, H. M., et al. (2025) · Nature Biomedical Engineering · doi:10.1038/s41551-025-01516-3 · Cited by 80 · see citing works
GrandQC: A comprehensive solution to quality control problem in digital pathology
Weng, Z., Seper, A., Pryalukhin, A., Mairinger, F. D., Wickenhauser, C., Bauer, M., Glamann, L., Bläker, H., Lingscheidt, T., Hulla, W., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-54769-y · Cited by 67 · see citing works
“In our study, we chosenot to use state-of-the-art foundation encoders like UNI, Prov-Gigapath, or CONCH 26 – 28 for model development due to their current limitations in applying to multi-class semantic tissue segmentation.”
Pantanowitz, L., Pearce, T. M., Abukhiran, I. M., Hanna, M. G., Wheeler, S., Soong, T. R., Tafti, A. P., Pantanowitz, J., Lu, M. Y., Mahmood, F., et al. (2024) · Modern Pathology · doi:10.1016/j.modpat.2024.100680 · Cited by 70 · see citing works
Self-citationDemographic 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
Combining spatial transcriptomics with tissue morphology
Chelebian, E., Avenel, C., & Wählby, C. (2025) · Nature Communications · doi:10.1038/s41467-025-58989-8 · Cited by 66 · see citing works
“Rapid advancements in 2024 have led to the development of models like CONCH 86 , CTrans-Path 87 , H-optimus-0 88 , Kaiko 89 , Phikon 90 , Prov-GigaPath 91 , UNI 92 , or Virchow 93 , showcasing state-of-the-art representation learning.”
A pathologist–AI collaboration framework for enhancing diagnostic accuracies and efficiencies
Huang, Z., Yang, E., Shen, J., Gratzinger, D. A., Eyerer, F., Liang, B., Nirschl, J., Bingham, D. B., Dussaq, A., Kunder, C. A., et al. (2024) · Nature Biomedical Engineering · doi:10.1038/s41551-024-01223-5 · Cited by 62 · see citing works
A multimodal knowledge-enhanced whole-slide pathology foundation model
Xu, Y., Wang, Y., Zhou, F., Ma, J., Jin, C., Yang, S., Li, J., Zhang, Z., Zhao, C., Zhou, H., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-66220-x · Cited by 74 · see citing works
“8 and Supplementary Table 3, the involvement of pathology reports and gene expression data can bring extra performance gains.”
InfluentialA Multimodal Knowledge-enhanced Whole-slide Pathology Foundation Model
Xu, Y., Wang, Y., Zhou, F., Ma, J., Jin, C., Yang, S., Li, J., Zhang, Z., Zhao, C., Zhou, H., et al. (2024) · arXiv · doi:10.48550/arxiv.2407.15362 · Cited by 60 · see citing works
“8 and Supplementary Table 3, the involvement of pathology reports and gene expression data can bring extra performance gains.”
InfluentialLarge Language Models and Large Multimodal Models in Medical Imaging: A Primer for Physicians
Bradshaw, T., Tie, X., Warner, J., Hu, J., Li, Q., & Li, X. (2025) · Journal of Nuclear Medicine · doi:10.2967/jnumed.124.268072 · Cited by 58 · 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
“Moreover, despite the intensive study on the diagnostic performance of medical FMs 1,3 , currently, the influence of FMs on disease management remains underexplored.”
Li, Z., Li, Y., Xiang, J., Wang, X., Yang, S., Zhang, X., Eweje, F. R., Chen, Y. M., Luo, X., Li, Y., et al. (2026) · Nature Medicine · doi:10.1038/s41591-025-04060-4 · Cited by 61 · see citing works
Transparency of medical artificial intelligence systems
Kim, C., Gadgil, S. U., & Lee, S. (2025) · Nature Reviews Bioengineering · doi:10.1038/s44222-025-00363-w · Cited by 58 · see citing works
“LLMs can also be fine-tuned on medical data, such as clinical notes 171 , articles indexed in PubMed 172 and question–answer pairs from professional medical licensing exams 173,174 .”
Transcriptomics-Guided Slide Representation Learning in Computational Pathology
Jaume, G., Oldenburg, L., Vaidya, A., Chen, R. J., Williamson, D. F. K., Peeters, T., Song, A. H., & Mahmood, F. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.00920 · Cited by 51 · see citing works
Self-citationGenerative Artificial Intelligence in Anatomic Pathology
Brodsky, V., Ullah, E., Bychkov, A., Song, A. H., Walk, E. E., Louis, P., Rasool, G., Singh, R. S., Mahmood, F., Bui, M. M., et al. (2025) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2024-0215-ra · Cited by 48 · see citing works
Self-citationROSIE: AI generation of multiplex immunofluorescence staining from histopathology images
Wu, E., Bieniosek, M. F., Wu, Z., Thakkar, N., Charville, G. W., Makky, A., Schürch, C. M., Huyghe, J. R., Peters, U., Li, C. I., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-62346-0 · Cited by 47 · see citing works
Deep learning quantifies pathologists’ visual patterns for whole slide image diagnosis
Nan, T., Zheng, S., Qiao, S., Quan, H., Gao, X., Niu, J., Zheng, B., Guo, C., Zhang, Y., Wang, X., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-60307-1 · Cited by 48 · see citing works
“To comprehensively evaluate the difference in classi fi cation performance between PEAN and other baselines, we selected two image encoders that can be decoupled from the subsequent network: ResNet50 42 pre-trained on ImageNet 43 and “ CONCH ” 44 pre-trained on pathological images.”
InfluentialNovae: a graph-based foundation model for spatial transcriptomics data
Blampey, Q., Benkirane, H., Bercovici, N., Mulder, K., Gessain, G., Ginhoux, F., André, F., & Cournède, P.-H. (2025) · Nature Methods · doi:10.1038/s41592-025-02899-6 · Cited by 50 · see citing works
“For instance, domain D2030, identified by multimodal Novae, is enriched in inflammatory monocytes (a distinction not captured by CONCH alone; Supplementary Fig.”
InfluentialBian, Y., Li, J., Ye, C., Jia, X., & Yang, Q. (2025) · Chinese Medical Journal · doi:10.1097/cm9.0000000000003489 · Cited by 48 · see citing works
“In histopathology, Lu et al [99] introduced a vision-language foundation model named CONCH, which was trained on over 1.17 million histopathology images paired with corresponding captions.”
InfluentialRedlich, J.-P., Feuerhake, F., Weis, J., Schaadt, N. S., Teuber‐Hanselmann, S., Buck, C., Luttmann, S., Eberle, A., Nikolin, S., Appenzeller, A., et al. (2024) · npj Imaging · doi:10.1038/s44303-024-00020-8 · Cited by 47 · see citing works
“The emergence of foundationmodelsforcomputationalpathology 123,124 ,developedusinglarge amountsofhistopathologicaldata,maybeanimprovementoverthecurrent relianceontheResNetCNNarchitectureandtheImageNetdatasetforpre-training.”
Unlocking the power of spatial omics with AI
Coleman, K., Schroeder, A. R., & Li, M. (2024) · Nature Methods · doi:10.1038/s41592-024-02363-x · Cited by 44 · see citing works
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Patritumab deruxtecan in HR+HER2− advanced breast cancer: a phase 2 trial
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“Two different models were employed to extract representations from the patches: the same model used in the Daisy clinical trial and the CONCH model 40 .”
Patkar, S., Chen, A., Basnet, A., Bixby, A., Rajendran, R., Chernet, R., Faso, S., Kumar, P. A., Desai, D., El-Zammar, O., et al. (2024) · npj Precision Oncology · doi:10.1038/s41698-024-00765-w · Cited by 41 · see citing works
Generating dermatopathology reports from gigapixel whole slide images with HistoGPT
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“Many vision language models in pathology are currently based on contrastive learning, such as PLIP and CONCH, where the embedding distance is minimized between positive pairs and maximized for negative pairs during training.”
InfluentialCohort-Individual Cooperative Learning for Multimodal Cancer Survival Analysis
Zhou, H., Zhou, F., & Chen, H. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3455931 · Cited by 33 · see citing works
“To prove the robustness of our framework, we compare the proposed framework with main competitors, including MCAT [29], SurvPath [30], MOTCat [32], and CMTA [33] by using latest pathology foundation models, including HIPT [16], PLIP [52], UNI [54], and CONCH [53].”
Imaging 3D cell cultures with optical microscopy
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Spatial architecture of development and disease
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“Over the past decade, AI has become an invaluable tool in medical diagnostics, particularly in radiology and microscopic pathology 172,173 .”
Hou, J., Zhang, R., Xie, Y., Li, C., & Qin, W. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02257-y · Cited by 38 · see citing works
“Apre-trainedvisual-languagefoundationmodel,CONCH 24 ,wasusedtoencodetext,resultingin clinical embedding X C 2 R n c × d c , where d c is the dimension of each clinic embedding.”
Harnessing Text Insights With Visual Alignment for Medical Image Segmentation
Zeng, Q., Luo, H., Lu, Z., Xie, Y., Wang, Z., Zhang, Y., & Xia, Y. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3601359 · Cited by 30 · see citing works
“In the medical domain, modality-specific VLMs have been developed for applications such as pathology image analysis [44], [45] and Chest X-Ray diagnosis [13], [14], [46], [47].”
Kludt, C., Wang, Y., Ahmad, W., Bychkov, A., Fukuoka, J., Gaisa, N. T., Kühnel, M., Jonigk, D. D., Pryalukhin, A., Mairinger, F. D., et al. (2024) · Cell Reports Medicine · doi:10.1016/j.xcrm.2024.101697 · Cited by 29 · see citing works
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“4) [59].”
Application of artificial intelligence and digital tools in cancer pathology
Shaktah, L., Carrero, Z. I., Hewitt, K. J., Gustav, M., Cecchini, M. J., Foersch, S., Berezowska, S., & Kather, J. N. (2025) · The Lancet Digital Health · doi:10.1016/j.landig.2025.100933 · Cited by 31 · see citing works
Xu, H., Wang, M., Shi, D., Qin, H., Zhang, Y., Liu, Z., Madabhushi, A., Gao, P., Cong, F., & Lu, C. Y. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103456 · Cited by 33 · see citing works
“After that, we identified ImageNet pre-trained ResNet50 [23] and five recent histological foundation models: CTransPath [10], PathoDuet [11], PLIP [12], CONCH [13], and UNI [14], for conduct-163 ing patch-level embedding (see Fig.”
The Use of Artificial Intelligence for Cancer Therapeutic Decision-Making
Elemento, O., Khozin, S., & Sternberg, C. N. (2025) · NEJM AI · doi:10.1056/aira2401164 · Cited by 28 · see citing works
Beyond Biomarkers: Machine Learning-Driven Multiomics for Personalized Medicine in Gastric Cancer
Ma, D., Fan, C., Sano, T., Kawabata, K., Nishikubo, H., Imanishi, D., Sakuma, T., Maruo, K., Yamamoto, Y., Matsuoka, T., et al. (2025) · Journal of Personalized Medicine · doi:10.3390/jpm15050166 · Cited by 30 · see citing works
Multistain Pretraining for Slide Representation Learning in Pathology
Jaume, G., Vaidya, A., Zhang, A., H. Song, A., J. Chen, R., Sahai, S., Mo, D., Madrigal, E., Phi Le, L., & Mahmood, F. (2025) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-73414-4_2 · Cited by 27 · see citing works
Self-citationCisternino, F., Ometto, S., Chatterjee, S., Giacopuzzi, E., Levine, A. P., & Glastonbury, C. A. (2024) · Nature Communications · doi:10.1038/s41467-024-50317-w · Cited by 26 · see citing works
Mahbod, A., Saeidi, N., Hatamikia, S., & Woitek, R. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110571 · Cited by 33 · see citing works
“Vision/Language 1024 CONCH [30] Med.”
STPath: a generative foundation model for integrating spatial transcriptomics and whole-slide images
Huang, T., Liu, T., Babadi, M., Ying, R., & Jin, W. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02020-3 · Cited by 28 · see citing works
“Additionally,ourresultsdemonstratethatthe predictedbiomarkerexpressionsindicatemutationstatusaccuratelywith-outfurther fi ne-tuning (e.g., TP53 in breast cancer reaches AUC = 0.73).”
InfluentialHuang, Q., Wu, S., Ou, Z., & Gao, Y. (2025) · Intelligent Oncology · doi:10.1016/j.intonc.2025.03.004 · Cited by 27 · see citing works
A large model for non-invasive and personalized management of breast cancer from multiparametric MRI
Luo, L., Wu, M., Li, M., Xin, Y., Wang, Q., Vardhanabhuti, V., Chu, C. W., Li, Z., Zhou, J., Rajpurkar, P., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-58798-z · Cited by 28 · see citing works
SlideChat: A Large Vision-Language Assistant for Whole-Slide Pathology Image Understanding
Chen, Y., Wang, G., Ji, Y., Li, Y., Ye, J., Li, T., Hu, M., Yu, R., Qiao, Y., & He, J. (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/cvpr52734.2025.00484 · Cited by 26 · see citing works
“Unless otherwise specified, SlideChat is configured with the patch-level encoder CONCH [20], the slide-level encoder LongNet [12], and utilizes the Qwen2.”
High-Parameter Spatial Multi-Omics through Histology-Anchored Integration
Liu, Y.-H., Wang, C.-Y., Wang, Z.-K., Chen, L., Li, Z., Song, J.-N., Zou, Q., Gao, R., Qian, B.-Z., Feng, X., et al. (2025) · bioRxiv · doi:10.1038/s41592-025-02926-6 · Cited by 32 · see citing works
“Notably, incorporating other advanced histology image foundation models, such as CONCH 71 , Phikon 72 and Gigapath 73 , could also yield competitive feature representation, as demonstrated in Extended Data Fig.”
Sun, 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 · see citing works
“At the patch level, these include CLIP [39]-based models like CONCH [34] and PathGen-CLIP [47], DINOv2 [37]-based models like Virchow2 [65] and UNI [12], and LMMs like PathAsst [48], PathGen-LLaVA [47], Quilt-LLaVA [42], and PathChat [35], which support tasks such as multi-turn conversations.”
Data-Centric Foundation Models in Computational Healthcare: A Survey
Zhang, Y., Gao, J. G., Tan, Z., Zhou, L., Ding, K., Zhou, M., Zhang, S., & Wang, D. (2026) · ACM Computing Surveys · doi:10.1145/3800677 · Cited by 42 · see citing works
Zhang, Y., Gao, Z., He, K., Li, C., & Mao, R. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103027 · Cited by 23 · see citing works
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 24 · see citing works
MSCPT: Few-Shot Whole Slide Image Classification With Multi-Scale and Context-Focused Prompt Tuning
Han, M., Qu, L., Yang, D., Zhang, X., Wang, X., & Zhang, L. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3564976 · Cited by 24 · see citing works
“Recent advances in pathology-specific VLMs [20], [34], [35], [36] have significantly enhanced their ability to capture domain-specific pathological knowledge, motivating the development of MSCPT to better leverage detailed and effective prior knowledge.”
InfluentialZhang, X.-M., Gao, T.-H., Cai, Q.-Y., Xia, J.-B., Sun, Y., Yang, J. M., Li, W., Zhang, S.-X.-M., Lou, H.-R., Yu, X., et al. (2026) · Military Medical Research · doi:10.1186/s40779-025-00680-6 · Cited by 32 · see citing works
“The contrastive learning from captions for histopathology (CONCH) [210] model extends this approach by utilizing large-scale paired pathology image-text data, improving WSI-level classification and retrieval while ensuring scalability across institutions.”
A deep learning–based digital biopsy for predicting early recurrence in gastric cancer
Ding, P., Chen, S. X., Guo, H. H., Yang, S., Wang, X., Han, X., Yang, J., Wu, H., Wu, J., Tian, Y., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-71347-6 · Cited by 25 · see citing works
“The comprehensive clinicopathological pro fi les are detailed in Supplementary Table 24, with clinical outcome reports referenced in prior publications 34 .”
Ji, X., Salmon, R. M., Mulliqi, N., Khan, U., Wang, Y., Blilie, A., Olsson, H., Pedersen, B. G., Sørensen, K. D., Ulhøi, B. P., et al. (2025) · Modern Pathology · doi:10.1016/j.modpat.2025.100715 · Cited by 24 · see citing works
Ophthalmological Question Answering and Reasoning Using OpenAI o1 vs Other Large Language Models
Srinivasan, S., Ai, X., Zou, M., Zou, K., Kim, H., Lo, T. W. S., Pushpanathan, K., Yang, G. D., Goh, J. H. L., Kong, Y., et al. (2025) · JAMA Ophthalmology · doi:10.1001/jamaophthalmol.2025.2413 · Cited by 21 · see citing works
The TRIPOD-LLM Statement: A Targeted Guideline For Reporting Large Language Models UsePreprint
Gallifant, J., Afshar, M., Ameen, S., Aphinyanaphongs, Y., Chen, S., Cacciamani, G. E., Demner‐Fushman, D., Dligach, D., Daneshjou, R., Fernandes, C. O., et al. (2024) · medRxiv · doi:10.1101/2024.07.24.24310930 · Cited by 21 · see citing works
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 27 · see citing works
Histo-Genomic Knowledge Association for Cancer Prognosis From Histopathology Whole Slide Images
Wang, Z., Zhang, Y., Xu, Y., Imoto, S., Chen, H., & Song, J. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3526816 · Cited by 20 · see citing works
“Recent studies have focused on using multi-modal data for training large pathology models, with PLIP [35] and CONCH [36] being two representative methods using histopathology images and biomedical texts for pre-training.”
Integrating language into medical visual recognition and reasoning: A survey
Lu, Y., & Wang, A. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103514 · Cited by 26 · see citing works
Virtual staining for pathology: Challenges, limitations and perspectives
Lin, W., Hu, Y., Zhu, R., Wang, B., & Wang, L. (2025) · Intelligent Oncology · doi:10.1016/j.intonc.2025.03.005 · Cited by 20 · see citing works
Sun, Y., Wen, X., Zhang, Y., Jin, L., Yang, C., Zhang, Q., Jiang, M. C., Xu, Z., Guo, W., Su, J., et al. (2025) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2025.108870 · Cited by 23 · see citing works
Multimodal integration strategies for clinical application in oncology
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 23 · see citing works
A systematic review of multi-modal large language models on domain-specific applications
Li, S., Wong, K. W., Wang, G., & Duong, T.-T. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11398-1 · Cited by 21 · see citing works
“Notably, the inclusion of prompts leads to a significant boost in AUC scores for both CONCH and SOTA, underscoring the importance of in-context learning in improv - ing the model’s ability to generalise and extract relevant features from input data.”
InfluentialMcKenzie, M. E., Irac, S. E., Chen, Z., Moradi, A., Jenner, A. L., Nguyen, Q. H., & Rashidieh, B. (2026) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2026.01.002 · Cited by 22 · see citing works
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 · see citing works
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 · see citing works
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 19 · see citing works
Aligning knowledge concepts to whole slide images for precise histopathology image analysis
Zhao, W., Guo, Z., Fan, Y., Jiang, Y., Yeung, M. C. F., & Yu, L. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01411-2 · Cited by 19 · see citing works
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 19 · see citing works
“AtTVGH, PICTURE attained 0.924, performing comparably to CONCH (0.919, P =0.427) and better than CTransPath (0.896, P =0.672).”
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 21 · see citing works
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 21 · see citing works
Zhang, Z., Zhang, H., Zeng, T., Yang, G., Shi, Z., & Gao, Z. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103536 · Cited by 17 · see citing works
A perspective for adapting generalist AI to specialized medical AI applications and their challenges
Wang, Z., Wang, H., Danek, B. P., Li, Y., Mack, C. D., Arbuckle, L., Biswal, D., Poon, H., Wang, Y., Rajpurkar, P., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01789-7 · Cited by 25 · see citing works
FOCUS: 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 18 · see citing works
“In this field of CPath, an ever-increasing number of foundation models have been proposed to improve diagnostic accuracy and prognostic assessment fundamentally by leveraging vast amounts of pathology data [8, 19, 29, 30, 41, 43, 48, 50].”
InfluentialAI 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 · see citing works
Self-citationArtificial 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 · see citing works
Toward general text-guided multimodal brain MRI synthesis for diagnosis and medical image analysis
Wang, Y., Xiong, H., Sun, K., Bai, S., Dai, L., Ding, Z., Liu, J., Wang, Q., Liu, Q., & Shen, D. G. (2025) · Cell Reports Medicine · doi:10.1016/j.xcrm.2025.102182 · Cited by 16 · see citing works
“Additionally, some models 24–27 adopt contrastive language-image pre-training (CLIP) strategies 28,29 to utilize aligned text and image embed-dings to tackle downstream tasks.”
Large-vocabulary forensic pathological analyses via prototypical cross-modal contrastive learning
Shen, C., Lian, C.-F., Zhang, W., Wang, F., Zhang, J., Fan, S., Wei, X., Wang, G., Li, K., Mu, H., et al. (2024) · Nature Communications · doi:10.1038/s41467-025-62060-x · Cited by 17 · see citing works
“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.”
Deep learning for digital pathology: A critical overview of methodological framework
Rad, M. S., Huang, J., Hosseini, M. M., Choudhary, R., Siezen, H., Akabari, R. R., Jamaspishvili, T., El-Zammar, O. A., Patel, P. G., Carello, S. J., et al. (2025) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2025.100514 · Cited by 22 · see citing works
- Authors
- Lu, M. Y., Chen, B., Williamson, D. F. K., Chen, R. J., Liang, I., Ding, T., Jaume, G., Odintsov, I., Le, L. P., Gerber, G., Parwani, A. V., Zhang, A., & Mahmood, F.
- Affiliations
- Broad Institute (US)
- Brigham and Women's Hospital (US)
- Harvard University (US)
- The Ohio State University Wexner Medical Center (US)
- Corresponding author
- Mahmood, Faisal
- ORCID
- Venue
- Nature Medicine
- Published
- 2024-03
- Volume
- 30
- Issue
- 3
- Pages
- 863-874
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- PMC
- PMC11384335
- OpenAlex
- W4392947532
- Semantic Scholar
- 0746410e1250576cc1b9621ef602e4bceb1c07ca
- Cited by
- 966
- References
- 79
- Citations by year
- 2026: 491 · 2025: 388 · 2024: 87
- FWCI
- 135.39
- 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, R35GM138216)
- National Institute of Diabetes and Digestive and Kidney Diseases (P30DK034854)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- histopathology, digital pathology, contrastive learning, image-caption pairs, biomedical text, histology image classification, image captioning, text-to-image retrieval, image-to-text retrieval, transfer learning, limited labeled data
- MeSH terms
- Machine Learning, Humans, Language, Workflow
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