Tanno, Ryutaro

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

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

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

Accelerating scientific discovery with Co-Scientist

Gottweis, J., Weng, W.-H., Daryin, A., Tu, T., Sirkovic, P., Myaskovsky, A., Glowaty, G., Weissenberger, F., Orlandi, A., Popovici, D., et al. (2025) · arXiv

Computational Drug Discovery MethodsArtificial Intelligence Applications

Cited by 158

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

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

Uncertainty Quantification in Deep Learning for Safer Neuroimage Enhancement

Tanno, R., Worrall, D., Kaden, E., Ghosh, A., Grussu, F., Bizzi, A., Sotiropoulos, S. N., Criminisi, A., & Alexander, D. C. (2019) · arXiv

Advanced Image Processing TechniquesImage and Signal Denoising Methods

Cited by 30

A Principled Approach to Failure Analysis and Model Repairment: Demonstration in Medical Imaging

Henn, T., Sakamoto, Y., Jacquet, C., Yoshizawa, S., Andou, M., Tchen, S., Saga, R., Ishihara, H., Shimizu, K., Li, Y., et al. (2021) · Lecture Notes in Computer Science

Explainable Artificial Intelligence (XAI)Software Testing and Debugging Techniques

Cited by 3