Karthikesalingam, Alan

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

International evaluation of an AI system for breast cancer screening

McKinney, S. M., Sieniek, M., Godbole, V., Godwin, J., Antropova, N., Ashrafian, H., Back, T., Chesus, M., Corrado, G. S., Darzi, A., et al. (2020) · Nature

AI in cancer detectionDigital Radiography and Breast Imaging

Cited by 3,287

Key challenges for delivering clinical impact with artificial intelligence

Kelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G., & King, D. (2019) · BMC Medicine

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 3,136

Clinically applicable deep learning for diagnosis and referral in retinal disease

De Fauw, J., Ledsam, J. R., Romera-Paredes, B., Nikolov, S., Tomasev, N., Blackwell, S., Askham, H., Glorot, X., O’Donoghue, B., Visentin, D., et al. (2018) · Nature Medicine

Retinal Imaging and AnalysisRetinal Diseases and Treatments

Cited by 2,639

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

A clinically applicable approach to continuous prediction of future acute kidney injury

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

Machine Learning in HealthcareAcute Kidney Injury Research

Cited by 1,086

Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis

Aggarwal, R., Sounderajah, V., Martin, G., Ting, D. S. W., Karthikesalingam, A., King, D., Ashrafian, H., & Darzi, A. (2021) · npj Digital Medicine

Artificial Intelligence in Healthcare and EducationAI in cancer detection

Cited by 946

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

Underspecification Presents Challenges for Credibility in Modern Machine Learning

D'Amour, A., Heller, K., Moldovan, D., Adlam, B., Alipanahi, B., Beutel, A., Chen, C., Deaton, J., Eisenstein, J., Hoffman, M. D., et al. (2020) · arXiv

Domain Adaptation and Few-Shot LearningAdversarial Robustness in Machine Learning

Cited by 398

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

The STARD-AI reporting guideline for diagnostic accuracy studies using artificial intelligence

Sounderajah, V., Guni, A., Liu, X., Collins, G. S., Karthikesalingam, A., Markar, S. R., Golub, R. M., Denniston, A. K., Shetty, S., Moher, D., et al. (2025) · Nature Medicine

Meta-analysis and systematic reviewsArtificial Intelligence in Healthcare and Education

Cited by 352

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

Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol

Sounderajah, V., Ashrafian, H., Golub, R. M., Shetty, S., De Fauw, J., Hooft, L., Moons, K., Collins, G., Moher, D., Bossuyt, P. M., et al. (2021) · BMJ Open

Meta-analysis and systematic reviewsArtificial Intelligence in Healthcare and Education

Cited by 333

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

Predicting conversion to wet age-related macular degeneration using deep learning

Yim, J., Chopra, R., Spitz, T., Winkens, J., Obika, A., Kelly, C., Askham, H., Lukic, M., Huemer, J., Fasler, K., et al. (2020) · Nature Medicine

Retinal Imaging and AnalysisRetinal Diseases and Treatments

Cited by 297

Developing specific reporting guidelines for diagnostic accuracy studies assessing AI interventions: The STARD-AI Steering Group

Sounderajah, V., Ashrafian, H., Aggarwal, R., De Fauw, J., Denniston, A. K., Greaves, F., Karthikesalingam, A., King, D., Liu, X., Markar, S. R., et al. (2020) · Nature Medicine

Meta-analysis and systematic reviewsArtificial Intelligence in Healthcare and Education

Cited by 270

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

Microvascular disease and risk of cardiovascular events among individuals with type 2 diabetes: a population-level cohort study

Brownrigg, J. R. W., Hughes, C. O., Burleigh, D., Karthikesalingam, A., Patterson, B. O., Holt, P. J., Thompson, M. M., de Lusignan, S., Ray, K. K., & Hinchliffe, R. J. (2016) · The Lancet Diabetes & Endocrinology

Diabetes, Cardiovascular Risks, and LipoproteinsChronic Kidney Disease and Diabetes

Cited by 242

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

Meta-analysis of randomized controlled trials comparing laparoscopic with open mesh repair of recurrent inguinal hernia

Karthikesalingam, A., Markar, S. R., Holt, P. J. E., & Praseedom, R. K. (2010) · Journal of British Surgery

Hernia repair and managementMinimally Invasive Surgical Techniques

Cited by 173