A Multimodal Biomedical Foundation Model Trained from Fifteen Million Image–Text Pairs
Abstract
BACKGROUND: Biomedical data are inherently multimodal, comprising physical measurements and natural-language narratives. A generalist biomedical artificial intelligence (AI) model needs to simultaneously process different modalities of data, including text and images. Therefore, training an effective generalist biomedical model requires high-quality multimodal data, such as parallel image-text pairs. METHODS: Here, we present PMC-15M, a novel dataset that is two orders of magnitude larger than existing biomedical multimodal datasets, such as MIMIC-CXR, and spans a diverse range of biomedical image types. PMC-15M contains 15 million biomedical image-text pairs collected from 4.4 million scientific articles. Based on PMC-15M, we have pretrained BiomedCLIP, a multimodal foundation model, with domain-specific adaptations tailored to biomedical vision-language processing. RESULTS: We conducted extensive experiments and ablation studies on standard biomedical imaging tasks from retrieval to classification to visual question answering (VQA). BiomedCLIP achieved new state-of-the-art results in a wide range of standard datasets, substantially outperforming prior approaches. Intriguingly, by large-scale pretraining on diverse biomedical image types, BiomedCLIP even outperforms state-of-the-art radiology-specific models, such as BioViL, in radiology-specific tasks such as Radiological Society of North America (RSNA) pneumonia detection. CONCLUSIONS: BiomedCLIP is a fully open-access foundation model that achieves state-of-the-art performance on various biomedical tasks, paving the way for transformative multimodal biomedical discovery and applications. We release our models at aka.ms/biomedclip to facilitate future research in multimodal biomedical AI.
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PanDerm surpassed vision-language models such as CLIP, MONET and BiomedCLIP in benchmark evaluations.
“Furthermore, PanDerm also surpassed vision-language models such as CLIP36, MONET39 and biomedical-specific CLIP (BiomedCLIP)40 in benchmark evaluations (Supplementary Table 1)”
From A Multimodal Vision Foundation Model for Clinical Dermatology (2025) · cites this paper as [40]
CLIP-large outperformed biomedical-specific and dermatology-specific CLIP models.
“CLIP-large outperformed biomedical-specific and dermatology-specific CLIP models”
From A Multimodal Vision Foundation Model for Clinical Dermatology (2025) · cites this paper as [40]
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Frascarelli, C., Venetis, K., Marra, A., Concardi, A., D’Ercole, M., Mangione, E., Negrelli, M., Porta, F. M., Keswani, S., Curigliano, G., et al. (2025) · npj Breast Cancer · doi:10.1038/s41523-025-00857-1 · Cited by 10 · see citing works
“The latter, image-text aligned models, have shown superior generalization capabilities in recent studies, outperforming vision-only approaches in the prediction of clinical biomarkers and subtypes 34 .”
Multi-Resolution Pathology-Language Pre-training Model with Text-Guided Visual Representation
Albastaki, S., Sohail, A., Ganapathi, I. I., Alawode, B., Khan, A. A., Javed, S., Werghi, N., Bennamoun, M., & Mahmood, A. (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/cvpr52734.2025.02413 · Cited by 8 · see citing works
Yun, H., Maeng, J., Kang, E., & Suk, H. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04981-0_15 · Cited by 8 · see citing works
A multimodal uncertainty-aware AI system optimizes ovarian cancer risk assessment workflow
Wang, X., Lv, X., Wang, J., Zou, L., Chen, Z., Zhao, R., Zhao, L., Zhao, M., Zhang, X., Zhang, B., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01986-4 · Cited by 8 · see citing works
“12) to a large biomedical foundation model (BiomedCLIP 33 ).”
Artificial intelligence in genitourinary pathology
Patel, A., Parwani, A. V., & Satturwar, S. P. (2025) · Histopathology · doi:10.1111/his.70020 · Cited by 9 · see citing works
Leveraging Multimodal Models for Enhanced Neuroimaging Diagnostics in Alzheimer’s Disease
Chiumento, F., & Liu, M. (2024) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata62323.2024.10825149 · Cited by 7 · see citing works
On the Robustness of Medical Vision-Language Models: Are They Truly Generalizable?
Imam, R., Marew, R., & Yaqub, M. K. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-98688-8_17 · Cited by 6 · see citing works
An Open-Source Clinical Case Dataset for Medical Image Classification and Multimodal AI Applications
Offidani, M. A. N., Roffet, F., Galtier, M. C. G., Massiris, M., & Delrieux, C. A. (2025) · Data · doi:10.3390/data10080123 · Cited by 7 · see citing works
A survey on electronic health record driven multimodal representation learning
Dou, Y., Liu, Y., Zou, H., Zeng, W., Xu, K., & Peng, S. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103810 · Cited by 7 · see citing works
Patrício, C., Rio-Torto, I., Cardoso, J. S., Teixeira, L. F., & Neves, J. C. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.111145 · Cited by 6 · see citing works
A vision–language pretrained transformer for versatile clinical respiratory disease applications
Ma, L., Liang, H., He, Y., Wang, W., Yan, Z., Li, W., Wang, R., Li, Y., Lizhu, Y., Liu, Y., et al. (2025) · Nature Biomedical Engineering · doi:10.1038/s41551-025-01544-z · Cited by 7 · see citing works
“Detailed ablation results can be found in Supplementary Tables 10–12.”
Multimodal analysis of whole slide images in colorectal cancer
Jonnagaddala, J., Shulajkovska, M., Gradišek, A., Jue, T. R., Zhou, Q., Guo, Y., Chayeb, J. M. E., Li, R., Lipková, J., Kather, J. N., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02095-y · Cited by 6 · see citing works
“Examples of visual-language foundation models in medicine include CONCH 20 , PLIP 48 , BioMedCLIP 49 , OpenAICLIP 50 , Prov-GigaPath 51 , and MUSK 52 .”
Aljuhani, H. L., Dahab, M. Y., & Alsenani, Y. S. (2025) · Sensors · doi:10.3390/s25247596 · Cited by 6 · see citing works
“Iteven surpassed radiology-specialized models (BioViL) for some tasks [14,39].”
A multimodal vision–language model for generalizable annotation-free pathology localization
Yang, H., Zhou, H.-Y., Liu, J., Huang, W., Li, C., Li, Z., Gao, Y., Liu, Q., Liang, Y., Yang, Q., et al. (2026) · Nature Biomedical Engineering · doi:10.1038/s41551-025-01574-7 · Cited by 9 · see citing works
“We compared AFLoc with several state-of-the-art methods, including CLIP [42], BiomedCLIP [43], and three foundation models specialized for histopathology: PLIP [44], QuiltNet [41], and CONCH [45].”
Self-citationSpiegler, P., Koleilat, T., Harirpoush, A., Miller, C., Rivaz, H. J., Kersten‐Oertel, M., & Xiao, Y. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00104 · Cited by 6 · see citing works
Yao, M. S., Chae, A., Saraiya, P. V., Kahn, C. E., Witschey, W. R. T., Gee, J. C., Sagreiya, H., & Bastani, O. (2025) · Communications Medicine · doi:10.1038/s43856-025-01061-9 · Cited by 7 · see citing works
“43 ; Zhang et al. 44 ; Zhang et al. 45 ; Zhang et al. 46 ; and Singhal et al. 47 introduce a number of performant models for medical tasks; however, these models again assume access to a relatively complete picture of the patient ’ s clinical status and past medical history, which is rarely the…”
EchoWorld: Learning Motion-Aware World Models for Echocardiography Probe Guidance
Yue, Y., Wang, Y., Jiang, H.-J., Liu, P., Song, S.-J., & Huang, G. (2025) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr52734.2025.02421 · Cited by 24 · see citing works
Self-citationFoundation Models for Volumetric Medical Imaging: Opportunities, Challenges, and Future Directions
Ghosh, T., Sheikhi, F., Guo, J., Singh, Y., Younis, K., Kuanar, S. P., Faghani, S., Farina, E. M. J. D. M., Huo, Y., & Maleki, F. (2026) · Electronics · doi:10.3390/electronics15061245 · Cited by 5 · see citing works
Performance of o1 pro and GPT-4 in Self-Assessment Questions for Nephrology Board Renewal
Noda, R., Yuasa, C., Kitano, F., Ichikawa, D., & Shibagaki, Y. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1702668 · Cited by 5 · see citing works
Polis, B., Zawadzka-Fabijan, A., Fabijan, R., Kosińska, R., Nowosławska, E., & Fabijan, A. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14103282 · Cited by 5 · see citing works
“Unlike general-purpose models such as ChatGPT-4o, BiomedCLIP employs a more logical and hierarchical classification scheme, which allows it to achieve a higher accuracy when differentiating between MCGR and PSF systems [42].”
A Foundation Model for Intensive Care: Unlocking Generalization across Tasks and Domains at ScalePreprint
Burger, M., Chopard, D., Lichtner, G., Londschien, M., Sergeev, F., Fuchs, M., Yèche, H., Kuznetsova, R., Faltys, M., Gerdes, E., et al. (2025) · medRxiv · doi:10.1101/2025.07.25.25331635 · Cited by 7 · see citing works
Alam, H. M. T., Srivastav, D., Mohamed Selim, A., Kadir, M. A., Shuvo, M. M. H., & Sonntag, D. (2025) · Companion Proceedings of the 17th ACM SIGCHI Symposium on Engineering Interactive Computing Systems · doi:10.1145/3731406.3731970 · Cited by 4 · see citing works
Ibrahim, Y., Sohail, A., Javed, S., Al-Marzouqi, H., Deriche, M. A., & Werghi, N. (2025) · Proceedings - International Conference on Image Processing · doi:10.1109/icip55913.2025.11084572 · Cited by 4 · see citing works
RadIR: A Scalable Framework for Multi-grained Medical Image Retrieval via Radiology Report Mining
Zhang, T., Zhao, Z., Wu, C., Zhou, X., Zhang, Y., Wang, Y., & Xie, W. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04971-1_48 · Cited by 4 · see citing works
Self-citationLi, Q., Wang, S., Safari, M., Hu, X. Y., Qiu, R. L. J., Roper, J. R., Lin, M., Lin, J. Y., Torres, M. A., & Yang, X. (2025) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ae0be6 · Cited by 4 · see citing works
Self-citationHealth system learning enables generalist neuroimaging models
Kondepudi, A., Rao, A. T., Zhao, C., Lyu, Y., Harake, E. S., Banerjee, S., Ogle, J. D., Joshi, R. S., Meißner, A.-K., Hou, X., et al. (2026) · Nature Medicine · doi:10.1038/s41591-026-04497-1 · Cited by 8 · see citing works
“Per-task and per-category results are in Supplementary Tables 3 and 4, respectively.”
Chen, K., Kuo, M., Lee, C., Liao, H.-C., Tsai, D., Lin, S.-A., Hsiang, C.-W., Chang, C.-K., Ko, K., Hsu, Y., et al. (2025) · Journal of Medical Systems · doi:10.1007/s10916-025-02263-3 · Cited by 4 · see citing works
Shu, K., Jia, Y., Zhang, Z., & Gao, J. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10822532 · Cited by 4 · see citing works
Self-citationGazeSearch: Radiology Findings Search Benchmark
Pham, T.-T., Nguyen, T.-P., Ikebe, Y., Awasthi, A., Deng, Z., Wu, C. C., Nguyen, H. D., & Le, N. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00020 · Cited by 4 · see citing works
Xia, H., Zhang, Y., Jia, H., Chen, Y., Xu, J., & Li, S. (2025) · Multimedia Systems · doi:10.1007/s00530-025-01848-9 · Cited by 3 · see citing works
“In addition to the baseline comparisons in Table 2, we further conducted a detailed comparison with recent state-of-the-art medical multimodal models, including BioMedCLIP [42].”
Blood Pressure Assisted Cerebral Microbleed Segmentation via Meta-matching
Kwon, J., Kim, J., Kim, T.-H., Seo, S. W., Cho, H.-H., & Park, H. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04927-8_8 · Cited by 3 · see citing works
BrainSMM: Lifespan Brain Segmentation Model With Metadata-Driven Prompt Learning
Teng, L., Zhao, Z., Wang, Y., Shi, F., & Shen, D. G. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3616586 · Cited by 3 · see citing works
“Specifically, we utilize the AutoTokenizer from HuggingFace [28] to convert input words of each metadata prompt into token IDs.”
Self-citationFrom Embeddings to Accuracy: Comparing Foundation Models for Radiographic Classification
Li, X., Merkow, J., Codella, N., Santamaría-Pang, A., Sangani, N., Ersoy, A., Burt, C., Garrett, J. W., Bruce, R. J., Warner, J., et al. (2025) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-025-01747-5 · Cited by 4 · see citing works
“The normalized images were then passed through seven distinct pre-trained foundation models— DenseNet-121 [20], BiomedCLIP [15], Med-Flamingo [21], MedImageInsight [19], MedSigLIP [22], Rad-DINO [23], and CXR-Foundation[18]—to extract high-dimensional feature embeddings as shown in Phase 1 of Fig.”
Self-citationFoundation models: Insights and implications for gastrointestinal cancer
Shi, L., Huang, R., Zhao, L.-L., & Guo, A.-J. (2025) · World Journal of Gastroenterology · doi:10.3748/wjg.v31.i47.112921 · Cited by 3 · see citing works
“However, the emergence of open-source VFMs, including MedSAM and Bio-medCLIP, has enhanced accessibility to these advanced tools[71,74].”
Park, Y. W., Kang, M., Ryu, H., Han, K., Sim, Y., Park, J. E., Chang, J. H., Kim, S. H., Lee, S.-K., Park, S. H., et al. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02581-x · Cited by 3 · see citing works
TeF-SAM: Prototype memory for medical lesion segmentation with text-free inference
Liang, J., Liu, X., Zang, Z., Wang, X., Qi, Y., & Li, X. (2026) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2026.102816 · Cited by 3 · see citing works
Nantha, O., Sathanarugsawait, B., & Praneetpolgrang, P. (2025) · Applied Sciences · doi:10.3390/app15094766 · Cited by 3 · see citing works
Meneghetti, A. R., Ligero, M., Kühn, J., Löck, S., Carrero, Z. I., Pérez-López, R., Bressem, K. K., Brinker, T. J., Pearson, A. T., Truhn, D., et al. (2025) · BMC Artificial Intelligence · doi:10.1186/s44398-025-00003-8 · Cited by 3 · see citing works
“Different pretrained feature extractors were considered to obtain slice-level representations of the patient: i) ImageNet-pretrained ResNet50, ii) ImageNet-pretrained SwinViT [28], and iii) the PMC-15 pretrained vision encoder extracted from the BioMedClip FM [13].”
VasTSD: Learning 3D Vascular Tree-state Space Diffusion Model for Angiography Synthesis
Wang, Z., Yi, R., Wen, X., Zhu, C., & Xu, K. (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/cvpr52734.2025.01463 · Cited by 3 · see citing works
Shin, Y., Lee, Y., Jang, H., Son, G., Kim, H., & Hwang, D. (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/cvpr52734.2025.02879 · Cited by 3 · see citing works
Leveraging Multi-Text Joint Prompts in SAM for Robust Medical Image Segmentation
Zhang, X., Zhao, H., Zhang, L., & Xiong, Y. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3607023 · Cited by 3 · see citing works
LesionDiffusion: Towards Text-Controlled General Lesion Synthesis
Lei, W., Tian, H., Dai, L., Chen, H., & Zhang, X. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04971-1_31 · Cited by 2 · see citing works
Localization Lens for Improving Medical Vision-Language Models
Farooq, H., Taj, M., Nasim, M., & Mahmood, A. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-05114-1_33 · Cited by 2 · see citing works
“Many approaches adopt contrastive learning frameworks, such as CLIP, where image and text encoders are trained jointly on large-scale datasets [4, 21, 29].”
Yamamoto, K., & Kikuchi, T. (2025) · Bioengineering · doi:10.3390/bioengineering12101093 · Cited by 2 · see citing works
Subgroup Performance Analysis in Hidden Stratifications
Bissoto, A. E., Hoang, T.-D., Flühmann, T., Sun, S., Baumgartner, C. F., & Koch, L. M. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-05185-1_57 · Cited by 3 · see citing works
“The external model is the pretrained CLIP [16] for all scenarios, and we included BiomedCLIP [21] for our real-world CheXpertPlus experiments.”
Cutting-edge AI technologies in skin cancer applications
Diao, Y., Chen, X., Huang, Z., Tan, Q., Yang, M., Yu, H., Xu, Y., & Hu, X. (2026) · Cancer Letters · doi:10.1016/j.canlet.2026.218256 · Cited by 2 · see citing works
Self-citationKhovanova, D., Vasilev, Y., Vladzymyrskyy, A. V., Omelyanskaya, O. V., Pamova, A. P., & Arzamasov, K. M. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1674835 · Cited by 2 · see citing works
“These models can process various biomedical images of different modalities and microscopic images (27, 28).”
Cheema, M. N., Nazir, A., Harmanci, A. O., Harmanci, A. O., Cheema, Y., Masood, S., & Khokhar, F. A. (2026) · The Visual Computer · doi:10.1007/s00371-026-04385-2 · Cited by 2 · see citing works
A foundation model for multi-task cross-distribution restoration of fluorescence microscopy images
Lu, Q., Liu, X., Feng, Q., Zeng, S., & Cheng, S. (2026) · Nature Communications · doi:10.1038/s41467-026-70307-4 · Cited by 2 · see citing works
“We adopted the text encoder from BiomedCLIP 26 as the text embedder, which had been pre-trained on diverse biomedical datasets, including generic biomedical illustrations, radiography, digital pathology, and microscopy.”
Advances in predicting omics profiles from imaging data
Beachum, A. H., Xiao, X., Zhou, Y., Li, Q. Y., Xiao, G., & Xu, L. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag090 · Cited by 2 · see citing works
“Moving beyond captioning, pathology-specific foundation models have been applied to a wide range of computational pathology (CPath) tasks, including tissue classification, mutation prediction, disease sub-typing, tumor grading, and more [30–36].”
Hybrid fusion framework for multimodal data integration
Wang, D., Liu, W., Nie, X., Liu, Y., Zhang, X., & Zhou, W. (2026) · Array · doi:10.1016/j.array.2026.100736 · Cited by 2 · see citing works
A foundation generative model for breast ultrasound image analysis
Yu, H., Li, Y., Zhang, N., Niu, Z., Gong, X., Luo, Y., Ye, H., He, S., Wu, Q., Qin, W., et al. (2026) · Nature Biomedical Engineering · doi:10.1038/s41551-026-01639-1 · Cited by 5 · see citing works
Self-citationMerMED-FM: Multimodal, Multi-Disease Medical Imaging Foundation Model
Zhou, Y., Quek, C. W. N., Zhou, J., Wang, Y., Bai, Y., Gutiérrez, L., Ke, Y., Yao, J., Teo, Z. L., Ting, D. S. J., et al. (2026) · The Lancet Digital Health · doi:10.1016/j.landig.2026.101007 · Cited by 2 · see citing works
Self-citationNie, Y., He, S., Bie, Y., Wang, Y., Chen, Z., Yang, S., Cai, Z., Wu, L., Wang, H., Wang, X., et al. (2026) · Nature Biomedical Engineering · doi:10.1038/s41551-026-01764-x · Cited by 13 · see citing works
Self-citationChen, Q., Yao, X., Ye, H., & Hong, Y. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3609739 · Cited by 2 · see citing works
UltraSeP: Sequence-aware pre-training for echocardiography probe movement guidance
Jiang, H., Wang, T., Sun, Z., Wang, Y., Yang, Y., Sun, Y., Jia, N., Li, M., Luo, S., Song, S., et al. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.112600 · Cited by 2 · see citing works
“Ad-ditionally, we evaluate medical vision models such as BioMedCLIP [32] and LVM-Med [33], as well as general vision models like DeiT [34] and DINOv2 [35].”
Self-citationFoundation models in medical image analysis: A systematic review and quantitative analysis
Rajendran, P., Safari, M., He, W., Hu, M., Shah, K. D., Wang, S., Zhou, J., & Yang, X. (2026) · Medical Image Analysis · doi:10.1016/j.media.2026.104293 · Cited by 3 · see citing works
Self-citationEfficient Bilinear Attention-based Fusion for Medical Visual Question Answering
Zhang, Z., Wang, J., Qin, Z., Zhu, R., & Gong, X. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11228425 · Cited by 2 · see citing works
Nhu, D., Liu, J., Chang, R. S., Thom, D., Chen, Z. Z., Nazem-Zadeh, M., Anderson, A., Barnard, S., French, J. A., Kwan, P., et al. (2025) · medRxiv · doi:10.1101/2025.03.12.25323644 · Cited by 2 · see citing works
“Foundation models have been shown to be powerful pre-trained models that demonstrate effective transfer learning, particularly for tasks with limited data [25].”
PRIDE: Pediatric Radiological Image Diagnosis Engine Guided by Medical Domain Knowledge
Zhao, Y., Pang, S., Wang, Z., He, W., Wang, Y., Zhao, R.-W., Feng, R., & Zhang, X. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10822246 · Cited by 1 · see citing works
Self-citationBenchmarking Radiology Report Generation From Noisy Free-Texts
Yuan, Y., Zheng, Y., & Qu, L. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3569428 · Cited by 1 · see citing works
Confounder-Aware Medical Data Selection for Fine-Tuning Pretrained Vision Models
Ji, A., Kang, Q., Xu, W., Wang, C., Li, K., & Lao, Q. (2025) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi60581.2025.10980785 · Cited by 1 · see citing works
Task-specific prompting SAM for multi-task gastric cancer diagnosis in endoscopic images
Wang, B., Lei, C., Guo, Y., Hong, K., Kan, X., Ouyang, Y., Li, J., & Li, R. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128329 · Cited by 2 · see citing works
Ye, J., Wang, Y., Kong, W., Wu, G., & Liu, W. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-0020-8_37 · Cited by 1 · see citing works
Self-citationHuang, Y., Zhang, W., Huang, P., Fu, Y., Yang, R., & Yu, L. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04978-0_4 · Cited by 1 · see citing works
AdFair-CLIP: Adversarial Fair Contrastive Language-Image Pre-training for Chest X-Rays
Yi, C., Xiong, Z., Qi, Q., Wei, X., Bathla, G., Lin, C., Mortazavi, B. J., & Yang, T. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04978-0_2 · Cited by 1 · see citing works
BaMCo: Balanced Multimodal Contrastive Learning for Knowledge-Driven Medical VQA
Yazıcı, Z. A., & Ekenel, H. K. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04981-0_8 · Cited by 1 · see citing works
Guo, J., Wang, K., Tan, G., Li, G., Zhang, X., Chen, J., Hu, J., Liang, Y., & Jiang, B. (2025) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2025.3612381 · Cited by 1 · see citing works
Li, T., Li, K., Zhao, Z., Sun, Q., Zhang, X., Yao, Z., Zhou, J., & Hu, B. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.108151 · Cited by 1 · see citing works
- Authors
- Zhang, S., Xu, Y., Usuyama, N., Xu, H., Bagga, J., Tinn, R., Preston, S., Rao, R., Wei, M., Valluri, N., Wong, C., Tupini, A., Wang, Y., Mazzola, M., Shukla, S., Liden, L., Gao, J., Crabtree, A., Piening, B., Bifulco, C., Lungren, M. P., Naumann, T., Wang, S., & Poon, H.
- Affiliations
- Microsoft (United States) (US)
- University of Washington (US)
- Microsoft Research, Redmond, WA
- Providence Portland Medical Center (US)
- ORCID
- Zhang, Sheng
- Xu, Yanbo
- Usuyama, Naoto
- Xu, Hanwen
- Bagga, Jaspreet
- Tinn, Robert
- Preston, Sam
- Rao, Rajesh
- Wei, Mu
- Valluri, Naveen
- Wong, Cliff
- Tupini, Andrea
- Wang, Yu
- Mazzola, Matt
- Shukla, Swadheen
- Liden, Lars
- Gao, Jianfeng
- Crabtree, Angela
- Piening, Brian
- Bifulco, Carlo
- Lungren, Matthew P.
- Naumann, Tristan
- Wang, Sheng
- Poon, Hoifung
- Venue
- NEJM AI
- Publisher
- Massachusetts Medical Society
- Published
- 2025-01-01
- Volume
- 2
- Issue
- 1
- Pages
- AIoa2400640-AIoa2400640
- ISSN
- 2836-9386 (ISSN-L)
- OpenAlex
- W4405669265
- Cited by
- 283
- References
- 41
- Citations by year
- 2026: 156 · 2025: 121 · 2024: 6
- FWCI
- 39.62
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- NEJM AI (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Keywords
- multimodal foundation models, large language models, multimodal pre-training
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