Transparent medical image AI via an image–text foundation model grounded in medical literature
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Abstract
Abstract Building trustworthy and transparent image-based medical AI systems requires the ability to interrogate data and models at all stages of the development pipeline: from training models to post-deployment monitoring. Ideally, the data and associated AI systems could be described using terms already familiar to physicians, but this requires medical datasets densely annotated with semantically meaningful concepts. Here, we present a foundation model approach, named MONET ( M edical c ON cept r ET riever), which learns how to connect medical images with text and generates dense concept annotations to enable tasks in AI transparency from model auditing to model interpretation. Dermatology provides a demanding use case for the versatility of MONET, due to the heterogeneity in diseases, skin tones, and imaging modalities. We trained MONET on the basis of 105,550 dermatological images paired with natural language descriptions from a large collection of medical literature. MONET can accurately annotate concepts across dermatology images as verified by board-certified dermatologists, outperforming supervised models built on previously concept-annotated dermatology datasets. We demonstrate how MONET enables AI transparency across the entire AI development pipeline from dataset auditing to model auditing to building inherently interpretable models.
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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 [39]
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 [39]
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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
CLIP in medical imaging: A survey
Zhao, Z., Liu, Y., Wu, H., Wang, M., Li, Y., Wang, S., Teng, L., Liu, D., Cui, Z., Wang, Q., et al. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103551 · Cited by 175 · see citing works
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
A Multimodal Vision Foundation Model for Clinical Dermatology
Yan, S., Yu, Z., Primiero, C., Vico-Alonso, C., Wang, Z., Yang, L., Tschandl, P., Hu, M., Ju, L., Tan, G., et al. (2025) · Nature Medicine · doi:10.1038/s41591-025-03747-y · Cited by 117 · see citing works
“Furthermore, PanDerm also surpassed vision-language models such as CLIP 36 , MONET 39 and biomedical-specific CLIP (BiomedCLIP) 40 in benchmark evaluations (Supplementary Table 1), while showing emergent capabilities in dermatology similar to those of DINOv2 in natural images, with linear probing performance comparabl…”
Principles, applications, and future of artificial intelligence in dermatology
Omiye, J. A., Gui, H., Daneshjou, R., Cai, Z. R., & Muralidharan, V. (2023) · Frontiers in Medicine · doi:10.3389/fmed.2023.1278232 · Cited by 78 · see citing works
Self-citationKurtansky, N. R., D’Alessandro, B. M., Gillis, M. C., Betz‐Stablein, B., Cerminara, S. E., García, R. P., Girundi, M. A., Goessinger, E. V., Gottfrois, P., Guitera, P., et al. (2024) · Scientific Data · doi:10.1038/s41597-024-03743-w · Cited by 53 · 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
Self-citationAdvancing medical imaging with language models: featuring a spotlight on ChatGPT
Hu, M., Qian, J. Y., Pan, S., Li, Y., Qiu, R. L. J., & Yang, X. (2024) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ad387d · Cited by 74 · see citing works
Ghosh, S., Poynton, C. B., Visweswaran, S., & Batmanghelich, K. N. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72390-2_59 · Cited by 46 · see citing works
Bian, Y., Li, J., Ye, C., Jia, X., & Yang, Q. (2025) · Chinese Medical Journal · doi:10.1097/cm9.0000000000003489 · Cited by 48 · see citing works
Developing a Transparent Anaemia Prediction Model Empowered With Explainable Artificial Intelligence
Farooq, M. S., Muhammad, M. H. G., Ali, O., Zeeshan, Z., Saleem, M. A., Ahmad, M., Abbas, S., Khan, M. A., & Ghazal, T. M. (2024) · IEEE Access · doi:10.1109/access.2024.3522080 · Cited by 37 · see citing works
Explainable Artificial Intelligence: Advancements and Limitations
Aysel, H. I., Cai, X., & Prügel‐Bennett, A. (2025) · Applied Sciences · doi:10.3390/app15137261 · Cited by 36 · see citing works
Wang, S., Zhou, X., Li, C., Wang, S., Li, Y., Tan, T., & Zheng, H. (2025) · Research · doi:10.34133/research.1029 · Cited by 29 · see citing works
Investigating the Quality of DermaMNIST and Fitzpatrick17k Dermatological Image Datasets
Abhishek, K., Jain, A., & Hamarneh, G. (2025) · Scientific Data · doi:10.1038/s41597-025-04382-5 · Cited by 32 · see citing works
Generative Artificial Intelligence in Medicine
Shanmugam, D., Agrawal, M., Movva, R., Chen, I. Y., Ghassemi, M., Jacobs, M. L., & Pierson, E. (2025) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-103123-095332 · Cited by 34 · see citing works
Shen, Y., Li, H., Sun, C., Ji, H., Zhang, D., Hu, K., Tang, Y., Chen, Y., Wei, Z., & Lv, J. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01014-x · Cited by 21 · 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
Visual–language foundation models in medicine
Liu, C.-Y., Jin, Y.-X., Guan, Z., Li, T.-Y., Qin, Y.-M., Qian, B., Jiang, Z.-H., Wu, Y.-L., Wang, X.-N., Zheng, Y., et al. (2024) · The Visual Computer · doi:10.1007/s00371-024-03579-w · Cited by 36 · see citing works
Guo, R., Zhang, R., Zhou, H., Xie, T., Peng, Y., Chen, X., Guo, Y. F., Wan, F., Li, L., Zhang, Y., et al. (2024) · Plants · doi:10.3390/plants13162274 · Cited by 18 · see citing works
Challenges and applications in generative AI for clinical tabular data in physiology
Umesh, C., Mahendra, M., Bej, S., Wolkenhauer, O., & Wolfien, M. (2024) · Pflügers Archiv - European Journal of Physiology · doi:10.1007/s00424-024-03024-w · Cited by 19 · see citing works
Artificial intelligence in clinical trials of lung cancer: Current and future prospects
Zhang, Y., You, P., Liu, R., Lu, Y., Li, J., Lei, Y., Wu, S., & Zhou, H. (2024) · Intelligent Oncology · doi:10.1016/j.intonc.2024.11.003 · Cited by 18 · see citing works
Leveraging large language models in dermatology
Matin, R., Linos, E., & Rajan, N. (2023) · British Journal of Dermatology · doi:10.1093/bjd/ljad230 · Cited by 18 · see citing works
Mechanistic understanding and validation of large AI models with SemanticLens
Dreyer, M., Berend, J., Labarta, T., Vielhaben, J., Wiegand, T., Lapuschkin, S., & Samek, W. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-01084-w · Cited by 16 · see citing works
Towards Concept-Based Interpretability of Skin Lesion Diagnosis Using Vision-Language Models
Patrício, C., Teixeira, L. F., & Neves, J. C. (2024) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi56570.2024.10635623 · Cited by 16 · see citing works
Medical multimodal large language models: A systematic review
Hu, Y., Xu, C., Lin, B., Yang, W., & Tang, Y. Y. (2025) · Intelligent Oncology · doi:10.1016/j.intonc.2025.09.005 · Cited by 14 · see citing works
The clinical potential of counterfactual AI models
Lee, S., & Topol, E. J. (2024) · The Lancet · doi:10.1016/s0140-6736(24)00313-1 · Cited by 14 · see citing works
Self-citationTraffic Scene-Informed Attribution of Autonomous Driving Decisions
Shi, R., Li, T., Yamaguchi, Y., & Zhang, L. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3547879 · Cited by 14 · see citing works
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 13 · see citing works
Artificial Intelligence in the Non-Invasive Detection of Melanoma
Mendi, B. İ., Köse, K., Fleshner, L., Adam, R., Safai, B., Farabi, B., & Atak, M. F. (2024) · Life · doi:10.3390/life14121602 · Cited by 14 · see citing works
Gao, Y., Gao, Z., Gao, X., Liu, Y., Wang, B., & Zhuang, X. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72117-5_29 · Cited by 11 · see citing works
Wan, Z., Guo, Y., Bao, S., Wang, Q., & Malin, B. (2025) · Health Data Science · doi:10.34133/hds.0256 · Cited by 12 · see citing works
Tschandl, P., Akay, B. N., Rosendahl, C. O., Rotemberg, V. M., Todorovska, V., Jochen, W., Wolber, A. K., Müller, C., Kurtansky, N. R., Halpern, A., et al. (2025) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2025.06.1594 · Cited by 13 · see citing works
AI dermatology: Reviewing the frontiers of skin cancer detection technologies
Yu, Z., Xin, C., Yu, Y., Xia, J., & Han, L. (2025) · Intelligent Oncology · doi:10.1016/j.intonc.2025.03.002 · Cited by 11 · see citing works
UniMRISegNet: Universal 3D Network for Various Organs and Cancers Segmentation on Multi-Sequence MRI
Zhang, Z., Han, L., Zhang, T., Lin, Z., Gao, Q., Tong, T., Sun, Y., & Tan, T. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3504603 · Cited by 8 · see citing works
Augmented reality microscopy to bridge trust between AI and pathologists
Badve, S., Kumar, G. L., Lang, T., Peigin, E., Pratt, J., Anders, R. A., Chatterjee, D., González, R. S., Graham, R. P., Krasinskas, A. M., et al. (2025) · npj Precision Oncology · doi:10.1038/s41698-025-00899-5 · Cited by 8 · see citing works
Medical Knowledge Intervention Prompt Tuning for Medical Image Classification
Du, Y., Yu, N., & Wang, S. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3584841 · Cited by 9 · see citing works
Yan, S., Li, X., Hu, M., Jiang, Y., Yu, Z., & Ge, Z. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04971-1_35 · Cited by 8 · see citing works
A two-step concept-based approach for enhanced interpretability and trust in skin lesion diagnosis
Patrício, C., Teixeira, L. F., & Neves, J. C. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.02.013 · Cited by 10 · see citing works
Borra, S., Dey, N., Fong, S., Sherratt, R. S., & Shi, F. (2026) · Cancers · doi:10.3390/cancers18091361 · Cited by 8 · see citing works
Artificial intelligence–enabled precision medicine for inflammatory skin diseases
Tang, A. S., Wei, M. L., Haemel, A., La, C., Sirota, M., & Lee, E. Y. (2026) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2025.10.596 · Cited by 6 · see citing works
Jiang, C., Jiang, Z., Zhang, Z., Huang, H., Zhou, H., Jiang, Q., Teng, Y., Li, H., Xu, B., Li, X., et al. (2025) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-025-07090-9 · Cited by 6 · see citing works
Interpretable framework for predicting preoperative cardiorespiratory fitness using wearable data
Hussain, I., Zeepvat, J., Reid, M. C., Czaja, S. J., Pryor, K. O., & Boyer, R. B. (2025) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2025.108980 · Cited by 6 · see citing works
Jiang, R., Chen, B., Dong, Z., Zeng, X., You, H., Li, Y., Deng, Y., Mu, G., Wang, J., Huang, L., et al. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02569-7 · Cited by 6 · see citing works
Fang, J., Zhang, D., Xie, K., Xu, L., & Bi, X.-A. (2024) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-024-00651-w · Cited by 6 · see citing works
An explainable machine learning framework for predicting driving states using electroencephalogram
Hussain, I., Park, S.-J., Azad, A., & Alyami, S. A. (2025) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2025.104355 · Cited by 6 · see citing works
A genotype-phenotype transformer to assess and explain polygenic riskPreprint
Lee, I., Wallace, Z. S., Wang, Y., Park, S., Nam, H., Majithia, A. R., & Ideker, T. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.23.619940 · Cited by 8 · see citing works
Exploring Decision Shifts in Autonomous Driving With Attribution-Guided Visualization
Shi, R., Li, T., Yamaguchi, Y., & Zhang, L. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3513400 · Cited by 5 · see citing works
Tu, P., Zheng, C., Xie, X., Lv, J., Xie, M., Guo, J., Yin, S., Qiu, K., Wei, Y., Wang, C., et al. (2026) · Nature Biomedical Engineering · doi:10.1038/s41551-026-01622-w · Cited by 4 · see citing works
Research progress of dosiomics in precision radiotherapy
Lei, Y., Bai, H., Yu, J., Zhang, Z., Wang, L., Li, B., Li, L., & Li, L. (2025) · Journal of Cancer Research and Therapeutics · doi:10.4103/jcrt.jcrt_132_25 · Cited by 4 · see citing works
Liang, Y., Wei, Z., Yi, D., Chen, X., Siyao, D. U., Wong, C., Xu, Z., Gao, W., Han, C., Chen, K., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-69212-7 · Cited by 4 · see citing works
Understanding Decision-Making of Autonomous Driving via Semantic Attribution
Shi, R., Li, T., Yamaguchi, Y., & Zhang, L. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3483810 · Cited by 4 · see citing works
Foundation Models in Medical Image Analysis: Overview and Prospects
Rashed, E. A., & Bekhit, M. (2024) · IEEE International Conference on Future Machine Learning and Data Science (FMLDS) · doi:10.1109/fmlds63805.2024.00011 · Cited by 4 · see citing works
From task-specific to foundation models: A paradigm shift in medical vision-language analysis
Ali, M. U., Zafar, A., Kim, S., Kim, K. S., & Lee, S. W. (2025) · Computer Science Review · doi:10.1016/j.cosrev.2025.100831 · Cited by 5 · see citing works
Self-citationSpatial biomarker discovery via interpretable semantic learning in histopathology
Liang, J., Jiang, X., Reitsam, N. G., Lenz, T., Zhang, L., Gustav, M., Carrero, Z. I., Muti, H. S., Neidlinger, P., Clusmann, J., et al. (2026) · Cancer Cell · doi:10.1016/j.ccell.2026.05.014 · Cited by 3 · see citing works
Mask of Truth: Model Sensitivity to Unexpected Regions of Medical Images
Sourget, T., Hestbek-Møller, M., Jiménez-Sánchez, A., Xu, J. J., & Cheplygina, V. (2025) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-025-01531-5 · Cited by 3 · see citing works
DREAM: A framework for discovering mechanisms underlying AI prediction of protected attributesPreprint
Gadgil, S. U., DeGrave, A. J., Janizek, J. D., Xu, S. H., Nwandu, L., Fonjungo, F., Lee, S., & Daneshjou, R. (2024) · medRxiv · doi:10.1101/2024.04.09.24305289 · Cited by 3 · see citing works
Self-citationEditorial for Special Issue on Foundation Models for Medical Image Analysis
Wang, X., Wang, D., Li, X., Rittscher, J., Metaxas, D., & Zhang, S. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103389 · Cited by 3 · see citing works
Zhu, Y. Y., Wang, X., Huang, X., Xia, M., Mu, P., Hu, H., & Zhang, X. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3606881 · Cited by 3 · see citing works
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 universal foundation model for grounded biomedical image interpretation
Wu, L., Nie, Y., He, S., Zhuang, J., Luo, L., Li, T., Xie, Z., Chen, D., Zhao, Y., Mahboobani, N. R., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-73986-1 · Cited by 6 · see citing works
Jia, H., Qian, B., Qu, Y., Wang, J., Chen, J. H., Li, T., Zheng, C., Han, J., Zhang, G., Jin, Y., et al. (2026) · Nature Medicine · doi:10.1038/s41591-026-04545-w · Cited by 2 · see citing works
Self-citationDivergent impacts of explainable AI for dermatological diagnosis on clinicians versus lay people
Xu, X., Hu, H.-Y., Zhang, H., Wang, W., Wang, R., Soenksen, L., Badri, O., Jafry, S., Burger, E., Nwandu, L., et al. (2026) · Nature Medicine · doi:10.1038/s41591-026-04553-w · Cited by 2 · see citing works
Self-citationTowards Robust Zero-shot Chest X-ray Classification
Bhat, S., Panambur, A. B., Mansoor, A., Georgescu, B., Grbić, S., & Maier, A. (2025) · Informatik aktuell · doi:10.1007/978-3-658-47422-5_42 · Cited by 2 · see citing works
Hyperbolic vision language representation learning on chest radiology images
Zhang, Z., Qiao, Z., Han, L., Yang, H. B., Qian, Z., & Wu, J. (2025) · Health Information Science and Systems · doi:10.1007/s13755-025-00341-x · Cited by 2 · see citing works
Limitations of ChatGPT in Dermatologic Image Analysis: Toward Broader and Deeper Evaluation
Keplinger, L. E., & Hu, G. (2025) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2025.05.015 · Cited by 2 · see citing works
Attribution explanations for decision-making in deep lane-change models
Shi, R., Li, T., Yamaguchi, Y., & Zhang, L. (2025) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2025.105361 · Cited by 2 · see citing works
Foundation 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
Widening the scope of artificial intelligence applications in dermatology
Matin, R. (2024) · Clinical and Experimental Dermatology · doi:10.1093/ced/llae174 · Cited by 2 · see citing works
Artificial intelligence-enabled precision medicine for inflammatory skin diseasesPreprint
Tang, A., Wei, M. L., Haemel, A. K., La, C., Sirota, M., & Lee, E. Y. (2025) · PubMed · doi:10.48550/arxiv.2505.09527 · Cited by 5 · see citing works
Haile, G., Zhu, Y., Maqsood, M. H., Yin, X., & Liew, A. W. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110734 · Cited by 1 · see citing works
Interpretable Gastric Cancer Survival Prediction via Concept Bottleneck Model
Zhao, F., Li, A., Tian, X., & Wang, M. (2025) · International Conference on Artificial Intelligence Technologies and Applications (ICAITA) · doi:10.1109/icaita67588.2025.11137851 · Cited by 1 · see citing works
Concept-Induced Graph Perception Model for Interpretable Diagnosis
Zhao, L., Chen, C., Pu, B., Qi, X., Peng, F., Wang, C., Li, K., & Tan, G. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-05162-2_21 · Cited by 1 · see citing works
Reducing bulky medical images via shape-texture decoupled deep neural networks
Yang, R., Xiao, T., Cheng, Y., & Suo, J. (2026) · Nature Communications · doi:10.1038/s41467-026-68292-9 · Cited by 1 · see citing works
Clinical Implementation of Artificial Intelligence Systems in Medical Imaging and Radiotherapy
Vienna International Centre, 100, P. B., Vienna, A.-1., Austria, 2600-0, T. +. (., & 2600-7, F. +. (. (2026) · IAEA human health series · doi:10.61092/iaea.9olq-aqa9 · Cited by 2 · see citing works
Marini, N., Liang, Z., Rajaraman, S., Xue, Z., & Antani, S. (2026) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2026.105055 · Cited by 1 · see citing works
Beyond Pattern Recognition: Call for Functionally Aware AI for Anatomical Illustration
Kawalec, A., & Simka, M. (2026) · JMIR Medical Education · doi:10.2196/92700 · Cited by 1 · see citing works
Generalizable CT vision-language modeling for population health and disease risk
Beeche, C., Kim, J., Tavolinejad, H., Zhao, B., Dong, J., Sharma, R., Duda, J. T., Gee, J., Dako, F., Verma, A., et al. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-03257-2 · Cited by 1 · see citing works
A Framework for Evaluating the Efficacy of Foundation Embedding Models in HealthcarePreprint
Xu, S. H., Gui, H., Rotemberg, V. M., Wang, T., Chen, Y. T., & Daneshjou, R. (2024) · medRxiv · doi:10.1101/2024.04.17.24305983 · Cited by 1 · see citing works
Self-citationOpportunities in AI/ML to endotype asthma and other eosinophilic diseases
Ray, A., Das, J., & Khoury, P. (2025) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2025.01.044 · Cited by 1 · see citing works
Nguyen, T., Panwar, V., Jarmale, V. N., Perny, A., Dusek, C., Cai, Q., Kapur, P., Danuser, G. M., & Rajaram, S. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.03.18.643999 · Cited by 1 · see citing works
Yılmaz, A., Yuceyalcin, F., Varol, R., Gökyayla, E., Erdem, O., Choi, D., Demirçalı, A. A., Gençoğlan, G., Posma, J. M., & Temelkuran, B. (2025) · medRxiv · doi:10.1101/2025.05.17.25327785 · Cited by 2 · see citing works
Translating clinical gene sequencing into a foundational representation of tumor subtypePreprint
Kong, J., Lee, I., Boecher, D., Singhal, A., Kelly, M. R., Moon, J., Ahn, C. H., Ock, C., Kumar, T., Sears, T. J., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.08.674723 · Cited by 1 · see citing works
Explainable AI as a Double-Edged Sword in Dermatology: The Impact on Clinicians versus The PublicPreprint
Xu, X., Hu, H., Zhang, H., Wang, W. K., Wang, R., Soenksen, L. R., Badri, O., Jafry, S., Burger, E., Nwandu, L., et al. (2025) · medRxiv · doi:10.64898/2025.12.19.25342205 · Cited by 1 · see citing works
Self-citationProspector Heads: Generalized Feature Attribution for Large Models & DataPreprint
Machiraju, G., Derry, A., Desai, A., Guha, N., Karimi, A.-H., Zou, J., Altman, R. B., Ré, C., & Mallick, P. (2024) · PubMed · doi:10.48550/arxiv.2402.11729
Artificial intelligence: empowering new insights into gamete and embryo development
Du, Z., & Gao, S. (2024) · Scientia Sinica Vitae · doi:10.1360/ssv-2024-0072
Injecting Visual Prototypes into CLIP-driven Long-Tailed Skin Lesion Classification
Pan, Y. (2025) · Applied and Computational Engineering · doi:10.54254/2755-2721/2025.tj22275
ToPoMesh: accurate 3D surface reconstruction from CT volumetric data via topology modification
Chen, J., Zhu, Q. X., Xie, B., & Li, T. (2025) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-025-03381-3
Artificial Intelligence and Skin Disease Scoring: An Opportunity to Reset the Standards
Kittler, H. (2025) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2025.07.002
Identifying Gender-Specific Visual Bias Signals in Skin Lesion Classification
Lee, H. J., Yang, S., Chu, Y., & Oh, B. H. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-05870-6_6
Automated Laryngoscope Using AI Image Detection
John, R. R., Eisha, M., Safla, P., Sreekutty, K. S., & Vidya, G. S. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-032-06674-9_45
Yu, J., Wang, Y., Li, Z., & Shen, Y. (2025) · Intelligent Medicine · doi:10.1016/j.imed.2025.04.005
Jin, C., Rezk, E., Moursi, W. M., & Wang, Z. (2025) · IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...) · doi:10.1109/bhi67747.2025.11269526
Skin-R1: Clinical Knowledge-Guided Dermatological Diagnosis Using Vision-Language Models
Liu, Z., Ren, W., Zhang, J., Zhao, T., Zhu, J., Li, X., & Honavar, V. G. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37314-4_1 · Cited by 1 · see citing works
Equitable AI in Healthcare: Navigating Sex, Gender, and Intersectional Biases in Diagnostics
Lee, H., Yang, S., & Chu, Y. (2026) · Springer eBooks · doi:10.1007/978-981-95-2070-1_10
Evaluating the Trustworthiness of Foundation Models for Skin Lesion Segmentation
Chu, Y., Oh, B., & Yang, S. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00065
Extending Aumann-Shapley for Reliable Neuron Attribution in Autonomous Driving Decisions
Shi, R., Li, T., Yamaguchi, Y., & Zhang, L. (2026) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2026.3678500
Finetuning medical vision‑language models for interpretable and fine‑grained disease classification
Engelson, S., Ehrhardt, J., & Heinz Handels, A. (2026) · SPIE Medical Imaging: Computer-Aided Diagnosis · doi:10.1117/12.3086032
Scheuerman, M. K., Hutiri, W. T., Rahmattalabi, A., Matthews, V., Xiang, A., & Andrews, J. T. A. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790364
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Bhat, S., Mansoor, A., Georgescu, B., Zinnen, M., Sahu, P., Panambur, A. B., Ghesu, F. C., Grbić, S., & Maier, A. (2026) · Scientific Reports · doi:10.1038/s41598-026-52235-x · Cited by 1 · see citing works
- Authors
- Kim, C., Gadgil, S. U., DeGrave, A. J., Omiye, J. A., Cai, Z. R., Daneshjou, R., & Lee, S.-I.
- Affiliations
- University of Washington (US)
- Stanford Medicine (US)
- Stanford University (US)
- Corresponding author
- Daneshjou, Roxana
- Lee, Su-In
- ORCID
- Venue
- Nature Medicine
- Published
- 2024-04
- Volume
- 30
- Issue
- 4
- Pages
- 1154-1165
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- PMC
- PMC10312868
- OpenAlex
- W4394832225
- Cited by
- 125
- References
- 72
- Citations by year
- 2026: 48 · 2025: 50 · 2024: 25 · 2023: 2
- FWCI
- 11.52
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Nature Medicine (publishedVersion, cc-by)
- medRxiv (acceptedVersion, cc-by-nc)
- PubMed (publishedVersion)
- PubMed Central (submittedVersion, cc-by-nc)
- Funders
- National Science Foundation (DBI-1552309, DBI-1759487)
- U.S. Department of Health & Human Services | National Institutes of Health (R01 AG061132, R35 GM 128638, 5T32 AR007422-38, AR007422-38)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- vision-language models, medical literature, dermatological images, model auditing, data auditing, model interpretability, AI transparency, self-supervised learning
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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