Azizi, Shekoofeh

Large language models encode clinical knowledge

Singhal, K., Azizi, S., Tu, T., Mahdavi, S. S., Wei, J., Chung, H. W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al. (2023) · Nature

Artificial Intelligence in Healthcare and EducationTopic Modeling

Cited by 4,083

Large Language Models Encode Clinical Knowledge

Singhal, K., Azizi, S., Tu, T., Mahdavi, S. S., Wei, J., Chung, H. W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al. (2022) · arXiv

Artificial Intelligence in Healthcare and EducationTopic Modeling

Cited by 4,083

Toward expert-level medical question answering with large language models

Singhal, K., Tu, T., Gottweis, J., Sayres, R., Wulczyn, E., Amin, M., Hou, L., Clark, K., Pfohl, S. R., Cole-Lewis, H., et al. (2025) · Nature Medicine

Artificial Intelligence in Healthcare and EducationTopic Modeling

Cited by 1,734

Big Self-Supervised Models Advance Medical Image Classification

Azizi, S., Mustafa, B., Ryan, F., Beaver, Z., Freyberg, J., Deaton, J., Loh, A., Karthikesalingam, A., Kornblith, S., Chen, T., et al. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Domain Adaptation and Few-Shot LearningArtificial Intelligence in Healthcare and Education

Cited by 685

Towards Generalist Biomedical AI

Tu, T., Azizi, S., Driess, D., Schaekermann, M., Amin, M., Chang, P.-C., Carroll, A., Lau, C., Tanno, R., Ktena, I., et al. (2023) · arXiv

Artificial Intelligence in Healthcare and EducationMultimodal Machine Learning Applications

Cited by 483

Towards Generalist Biomedical AI

Tu, T., Azizi, S., Driess, D., Schaekermann, M., Amin, M., Chang, P.-C., Carroll, A., Lau, C., Tanno, R., Ktena, I., et al. (2024) · NEJM AI

Artificial Intelligence in Healthcare and EducationMultimodal Machine Learning Applications

Cited by 483

Towards conversational diagnostic artificial intelligence

Tu, T., Schaekermann, M., Palepu, A., Saab, K., Freyberg, J., Tanno, R., Wang, A., Li, B., Amin, M., Cheng, Y., et al. (2025) · Nature

Artificial Intelligence in Healthcare and EducationArtificial Intelligence in Healthcare

Cited by 366

Towards Expert-Level Medical Question Answering with Large Language Models

Singhal, K., Tu, T., Gottweis, J., Sayres, R., Wulczyn, E., Hou, L., Clark, K., Pfohl, S., Cole-Lewis, H., Neal, D., et al. (2023) · arXiv

Artificial Intelligence in Healthcare and EducationTopic Modeling

Cited by 338

Towards accurate differential diagnosis with large language models

McDuff, D., Schaekermann, M., Tu, T., Palepu, A., Wang, A., Garrison, J., Singhal, K., Sharma, Y., Azizi, S., Kulkarni, K., et al. (2025) · Nature

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 311

Towards Accurate Differential Diagnosis with Large Language Models

McDuff, D., Schaekermann, M., Tu, T., Palepu, A., Wang, A., Garrison, J., Singhal, K., Sharma, Y., Azizi, S., Kulkarni, K., et al. (2023) · arXiv

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 311

Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging

Azizi, S., Culp, L., Freyberg, J., Mustafa, B., Baur, S., Kornblith, S., Chen, T., Tomasev, N., Mitrović, J., Strachan, P., et al. (2023) · Nature Biomedical Engineering

Domain Adaptation and Few-Shot LearningArtificial Intelligence in Healthcare and Education

Cited by 250

Collaboration between clinicians and vision–language models in radiology report generation

Tanno, R., Barrett, D. G. T., Sellergren, A., Ghaisas, S., Dathathri, S., See, A., Welbl, J., Lau, C., Tu, T., Azizi, S., et al. (2025) · Nature Medicine

Artificial Intelligence in Healthcare and EducationMultimodal Machine Learning Applications

Cited by 186

Synthetic Data from Diffusion Models Improves ImageNet Classification

Azizi, S., Kornblith, S., Saharia, C., Norouzi, M., & Fleet, D. J. (2023) · arXiv

Generative Adversarial Networks and Image SynthesisAdvanced Neural Network Applications

Cited by 184

Generative models improve fairness of medical classifiers under distribution shifts

Wiles, O., Albuquerque, I., Rebuffi, S.-A., Tanno, R., Roy, A. G., Azizi, S., Belgrave, D., Kohli, P., Cemgil, T., Karthikesalingam, A., et al. (2023) · Nature Medicine

Artificial Intelligence in Healthcare and EducationDomain Adaptation and Few-Shot Learning

Cited by 165

Generative models improve fairness of medical classifiers under distribution shifts

Ktena, I., Wiles, O., Albuquerque, I., Rebuffi, S.-A., Tanno, R., Roy, A. G., Azizi, S., Belgrave, D., Kohli, P., Cemgil, T., et al. (2024) · Nature Medicine

Artificial Intelligence in Healthcare and EducationDomain Adaptation and Few-Shot Learning

Cited by 165

Capabilities of Gemini Models in Medicine

Saab, K., Tu, T., Weng, W.-H., Tanno, R., Stutz, D., Wulczyn, E., Zhang, F., Strother, T., Park, C., Vedadi, E., et al. (2024) · arXiv

Artificial Intelligence in Healthcare and EducationMultimodal Machine Learning Applications

Cited by 117

A toolbox for surfacing health equity harms and biases in large language models

Pfohl, S. R., Cole-Lewis, H., Sayres, R., Neal, D., Asiedu, M., Dieng, A., Tomasev, N., Rashid, Q. M., Azizi, S., Rostamzadeh, N., et al. (2024) · Nature Medicine

Ethics and Social Impacts of AIArtificial Intelligence in Healthcare and Education

Cited by 113

Publisher Correction: Large language models encode clinical knowledge

Singhal, K., Azizi, S., Tu, T., Mahdavi, S. S., Wei, J., Chung, H. W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al. (2023) · Nature

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 101

Does your dermatology classifier know what it doesn’t know? Detecting the long-tail of unseen conditions

Guha Roy, A., Ren, J., Azizi, S., Loh, A., Natarajan, V., Mustafa, B., Pawlowski, N., Freyberg, J., Liu, Y., Beaver, Z., et al. (2022) · Medical Image Analysis

Anomaly Detection Techniques and ApplicationsCutaneous Melanoma Detection and Management

Cited by 77

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Comanici, G., Bieber, E., Schaekermann, M., Pasupat, I., Sachdeva, N., Dhillon, I., Blistein, M., Ram, O., Zhang, D., Rosen, E., et al. (2025) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 69

MedGemma Technical Report

Sellergren, A., Kazemzadeh, S., Jaroensri, T., Kiraly, A., Traverse, M., Kohlberger, T., Xu, S., Jamil, F., Hughes, C., Lau, C., et al. (2025) · arXiv

Multimodal Machine Learning ApplicationsArtificial Intelligence in Healthcare and Education

Cited by 49

Detection and grading of prostate cancer using temporal enhanced ultrasound: combining deep neural networks and tissue mimicking simulations

Azizi, S., Bayat, S., Yan, P., Tahmasebi, A., Nir, G., Kwak, J. T., Xu, S., Wilson, S., Iczkowski, K. A., Lucia, M. S., et al. (2017) · International Journal of Computer Assisted Radiology and Surgery

Ultrasound Imaging and ElastographyRadiomics and Machine Learning in Medical Imaging

Cited by 47

Detection of prostate cancer using temporal sequences of ultrasound data: a large clinical feasibility study

Azizi, S., Imani, F., Ghavidel, S., Tahmasebi, A., Kwak, J. T., Xu, S., Turkbey, B., Choyke, P., Pinto, P., Wood, B., et al. (2016) · International Journal of Computer Assisted Radiology and Surgery

Prostate Cancer Diagnosis and TreatmentRadiomics and Machine Learning in Medical Imaging

Cited by 45

Advancing Multimodal Medical Capabilities of Gemini

Yang, L., Xu, S., Sellergren, A., Kohlberger, T., Zhou, Y., Ktena, I., Kiraly, A., Ahmed, F., Hormozdiari, F., Jaroensri, T., et al. (2024) · arXiv

Artificial Intelligence in Healthcare and EducationMultimodal Machine Learning Applications

Cited by 34