Karthikesalingam, Alan

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

A systematic review of percutaneous mechanical thrombectomy in the treatment of deep venous thrombosis

Karthikesalingam, A., Young, E. L., Hinchliffe, R. J., Loftus, I. M., Thompson, M. M., & Holt, P. J. E. (2011) · European Journal of Vascular and Endovascular Surgery

Venous Thromboembolism Diagnosis and ManagementCerebral Venous Sinus Thrombosis

Cited by 143

Common Limitations of Image Processing Metrics: A Picture Story

Reinke, A., Tizabi, M. D., Sudre, C. H., Eisenmann, M., Rädsch, T., Baumgartner, M., Acion, L., Antonelli, M., Arbel, T., Bakas, S., et al. (2021) · arXiv

Medical Image Segmentation TechniquesCell Image Analysis Techniques

Cited by 126

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

Tackling bias in AI health datasets through the STANDING TOGETHER initiative

Ganapathi, S., Palmer, J., Alderman, J. E., Calvert, M., Espinoza, C., Gath, J., Ghassemi, M., Heller, K., Mckay, F., Karthikesalingam, A., et al. (2022) · Nature Medicine

Ethics and Social Impacts of AIArtificial Intelligence in Healthcare and Education

Cited by 82

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

Metrics reloaded: Recommendations for image analysis validation

Maier-Hein, L., Reinke, A., Godau, P., Tizabi, M. D., Buettner, F., Christodoulou, E., Glocker, B., Isensee, F., Kleesiek, J., Kozubek, M., et al. (2022) · arXiv

Cell Image Analysis TechniquesMedical Image Segmentation Techniques

Cited by 59

AI mirrors experimental science to uncover a novel mechanism of gene transfer crucial to bacterial evolution

Penadés, J. R., Gottweis, J., He, L., Patkowski, J. B., Shurick, A., Weng, W.-H., Tu, T., Palepu, A., Myaskovsky, A., Pawlosky, A., et al. (2025) · Cell

Bacteriophages and microbial interactionsArtificial Intelligence Applications

Cited by 42

AI mirrors experimental science to uncover a mechanism of gene transfer crucial to bacterial evolution

Penadés, J. R., Gottweis, J., He, L., Patkowski, J. B., Daryin, A., Weng, W.-H., Tu, T., Palepu, A., Myaskovsky, A., Pawlosky, A., et al. (2025) · Cell

Bacteriophages and microbial interactionsArtificial Intelligence Applications

Cited by 42

Generative AI for medical education: Insights from a case study with medical students and an AI tutor for clinical reasoning

Wang, A., Ruparel, R., Iurchenko, A., Jhun, P., Séguin, J. A., Strachan, P., Wong, R., Karthikesalingam, A., Matias, Y., Hassidim, A., et al. (2025) · Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems

Artificial Intelligence in Healthcare and EducationArtificial Intelligence in Education

Cited by 6

Developing Deep Learning Continuous Risk Models for Early Adverse Event Prediction in Electronic Health Records: an AKI Case Study

Tomašev, N., Glorot, X., Rae, J. W., Zielinski, M., Askham, H., Saraiva, A., Mottram, A., Meyer, C., Ravuri, S., Protsyuk, I., et al. (2019) · Research Square

Machine Learning in HealthcareAcute Kidney Injury Research

Cited by 3