King, Dominic

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

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

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

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

‘Gamification’: Influencing health behaviours with games

King, D., Greaves, F., Exeter, C., & Darzi, A. (2013) · Journal of the Royal Society of Medicine

Educational Games and GamificationMobile Health and mHealth Applications

Cited by 319

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

Medication Adherence Apps: Review and Content Analysis

Ahmed, I., Ahmad, N. S., Ali, S., Ali, S., George, A., Saleem Danish, H., Uppal, E., Soo, J., Mobasheri, M. H., King, D., et al. (2018) · JMIR mHealth and uHealth

Mobile Health and mHealth ApplicationsMedication Adherence and Compliance

Cited by 221

Smartphone breast applications – What's the evidence?

Mobasheri, M. H., Johnston, M., King, D., Leff, D., Thiruchelvam, P., & Darzi, A. (2014) · The Breast

Mobile Health and mHealth ApplicationsMobile Learning in Education

Cited by 137

Communication aid requirements of intensive care unit patients with transient speech loss

Mobasheri, M. H., King, D., Judge, S., Arshad, F., Larsen, M., Safarfashandi, Z., Shah, H., Trepekli, A., Trikha, S., Xylas, D., et al. (2016) · Augmentative and Alternative Communication

Assistive Technology in Communication and MobilityVoice and Speech Disorders

Cited by 44

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