Tham, Yih-Chung

Global Prevalence of Glaucoma and Projections of Glaucoma Burden through 2040

Tham, Y.-C., Li, X., Wong, T. Y., Quigley, H. A., Aung, T., & Cheng, C.-Y. (2014) · Ophthalmology

Glaucoma and retinal disordersOphthalmology and Visual Impairment Studies

Cited by 7,159

Worldwide trends in hypertension prevalence and progress in treatment and control from 1990 to 2019: a pooled analysis of 1201 population-representative studies with 104 million participants

Zhou, B., Carrillo-Larco, R. M., Danaei, G., Riley, L. M., Paciorek, C. J., Stevens, G. A., Gregg, E. W., Bennett, J. E., Solomon, B., Singleton, R. K., et al. (2021) · The Lancet

Blood Pressure and Hypertension StudiesSodium Intake and Health

Cited by 3,902

Worldwide trends in underweight and obesity from 1990 to 2022: a pooled analysis of 3663 population-representative studies with 222 million children, adolescents, and adults

Phelps, N. H., Singleton, R. K., Zhou, B., Heap, R. A., Mishra, A., Bennett, J. E., Paciorek, C. J., Lhoste, V. P., Carrillo-Larco, R. M., Stevens, G. A., et al. (2024) · The Lancet

Obesity, Physical Activity, DietDiabetes, Cardiovascular Risks, and Lipoproteins

Cited by 2,544

Global Prevalence of Diabetic Retinopathy and Projection of Burden through 2045

Teo, Z. L., Tham, Y.-C., Yu, M., Chee, M. L., Rim, T. H., Cheung, N., Bikbov, M. M., Wang, Y. X., Tang, Y., Lu, Y., et al. (2021) · Ophthalmology

Retinal Diseases and TreatmentsRetinal Imaging and Analysis

Cited by 2,420

Worldwide trends in diabetes prevalence and treatment from 1990 to 2022: a pooled analysis of 1108 population-representative studies with 141 million participants

Zhou, B., Rayner, A. W., Gregg, E. W., Sheffer, K. E., Carrillo-Larco, R. M., Bennett, J. E., Shaw, J. E., Paciorek, C. J., Singleton, R. K., Barradas Pires, A., et al. (2024) · The Lancet

Diabetes, Cardiovascular Risks, and LipoproteinsDiabetes Treatment and Management

Cited by 1,013

Impact of common genetic determinants of hemoglobin A1c on type 2 diabetes risk and diagnosis in ancestrally diverse populations: A transethnic genome-wide meta-analysis

Wheeler, E., Leong, A., Liu, C.-T., Hivert, M.-F., Strawbridge, R. J., Podmore, C., Li, M., Yao, J., Sim, X., Hong, J., et al. (2017) · PLOS Medicine

Genetic Associations and EpidemiologyGenetic Mapping and Diversity in Plants and Animals

Cited by 408

A deep learning system for predicting time to progression of diabetic retinopathy

Dai, L., Sheng, B., Chen, T., Wu, Q., Liu, R., Cai, C., Wu, L., Yang, D., Hamzah, H., Liu, Y., et al. (2024) · Nature Medicine

Retinal Imaging and AnalysisRetinal Diseases and Treatments

Cited by 339

A deep learning algorithm to detect chronic kidney disease from retinal photographs in community-based populations

Sabanayagam, C., Xu, D., Ting, D. S. W., Nusinovici, S., Banu, R., Hamzah, H., Lim, C., Tham, Y.-C., Cheung, C. Y., Tai, E. S., et al. (2020) · The Lancet Digital Health

Retinal Imaging and AnalysisChronic Kidney Disease and Diabetes

Cited by 279

Integrated image-based deep learning and language models for primary diabetes care

Li, J., Guan, Z., Wang, J., Cheung, C. Y., Zheng, Y., Lim, L.-L., Lim, C. C., Ruamviboonsuk, P., Raman, R., Corsino, L., et al. (2024) · Nature Medicine

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 250

Deep-learning-based cardiovascular risk stratification using coronary artery calcium scores predicted from retinal photographs

Rim, T. H., Lee, C. J., Tham, Y.-C., Cheung, N., Yu, M., Lee, G., Kim, Y., Ting, D. S. W., Chong, C. C. Y., Choi, Y. S., et al. (2021) · The Lancet Digital Health

Cardiac Imaging and DiagnosticsRetinal Imaging and Analysis

Cited by 234

Large language models and their impact in ophthalmology

Betzler, B. K., Chen, H., Cheng, C.-Y., Lee, C. S., Ning, G., Song, S. J., Lee, A. Y., Kawasaki, R., van Wijngaarden, P., Grzybowski, A., et al. (2023) · The Lancet Digital Health

Artificial Intelligence in Healthcare and EducationOphthalmology and Visual Health Research

Cited by 161

Telehealth demand trends during the COVID-19 pandemic in the top 50 most affected countries: infodemiological evaluation

Wong, M. Y. Z., Gunasekeran, D. V., Nusinovici, S., Sabanayagam, C., Yeo, K. K., Cheng, C.-Y., & Tham, Y.-C. (2021) · JMIR Public Health and Surveillance

Telemedicine and Telehealth ImplementationData-Driven Disease Surveillance

Cited by 115

Artificial intelligence for diabetes care: current and future prospects

Sheng, B., Pushpanathan, K., Guan, Z., Lim, Q. H., Lim, Z. W., Yew, S. M. E., Goh, J. H. L., Bee, Y. M., Sabanayagam, C., Sevdalis, N., et al. (2024) · The Lancet Diabetes & Endocrinology

Artificial Intelligence in Healthcare and EducationDiabetes Management and Research

Cited by 106

Harnessing the potential of large language models in medical education: promise and pitfalls

Benítez, T. M., Xu, Y., Boudreau, J. D., Kow, A. W. C., Bello, F., Van Phuoc, L., Wang, X., Sun, X., Leung, G. K.-K., Lan, Y., et al. (2024) · Journal of the American Medical Informatics Association

Artificial Intelligence in Healthcare and EducationArtificial Intelligence in Education

Cited by 100

Medical education and physician training in the era of artificial intelligence

Xu, Y., Jiang, Z., Ting, D. S. W., Kow, A. W. C., Bello, F., Car, J., Tham, Y.-C., & Wong, T. Y. (2024) · Singapore Medical Journal

Artificial Intelligence in Healthcare and EducationInnovations in Medical Education

Cited by 76

A Peer-to-Peer Live-Streaming Intervention for Children During COVID-19 Homeschooling to Promote Physical Activity and Reduce Anxiety and Eye Strain: Cluster Randomized Controlled Trial

Zheng, Y., Wang, W., Zhong, Y., Wu, F., Zhu, Z., Tham, Y.-C., Lamoureux, E., Xiao, L., Zhu, E., Liu, H., et al. (2021) · Journal of Medical Internet Research

COVID-19 and Mental HealthPhysical Activity and Health

Cited by 73

AI-induced never-skilling in medical education

Ke, Y., Jin, L., Ong, J. C. L., Thirunavukarasu, A. J., Car, J., Cheung, C. Y., Tham, Y. C., Ting, D. S. W., Ong, M. E. H., Compton, S., et al. (2026) · Nature Medicine

Artificial Intelligence in Healthcare and EducationInnovations in Medical Education

Cited by 71

Diminishing benefits of urban living for children and adolescents' growth and development

NCD Risk Factor Collaboration (NCD-RisC), Mishra, A., Zhou, B., Rodriguez-Martinez, A., Bixby, H., Singleton, R. K., Carrillo-Larco, R. M., Sheffer, K. E., Paciorek, C. J., Bennett, J. E., et al. (2023) · Nature

Obesity, Physical Activity, DietHuman Health and Disease

Cited by 70

Do we have enough ophthalmologists to manage vision-threatening diabetic retinopathy? A global perspective

Teo, Z. L., Tham, Y.-C., Yu, M., Cheng, C.-Y., Wong, T. Y., & Sabanayagam, C. (2020) · Eye

Global Health Workforce IssuesRetinal Diseases and Treatments

Cited by 62

A multi-ancestry genome-wide study incorporating gene–smoking interactions identifies multiple new loci for pulse pressure and mean arterial pressure

Sung, Y. J., de las Fuentes, L., Winkler, T. W., Chasman, D. I., Bentley, A. R., Kraja, A. T., Ntalla, I., Warren, H. R., Guo, X., Schwander, K., et al. (2019) · Human Molecular Genetics

Genetic Associations and EpidemiologyBlood Pressure and Hypertension Studies

Cited by 43

Uncertainty-aware Medical Diagnostic Phrase Identification and Grounding

Zou, K., Bai, Y., Liu, B., Chen, Y., Chen, Z., Zhou, Y., Yuan, X., Wang, M., Shen, X., Cao, X., et al. (2024) · arXiv

Multimodal Machine Learning ApplicationsArtificial Intelligence in Healthcare and Education

Cited by 20

Generative artificial intelligence for fundus fluorescein angiography interpretation and human expert evaluation

Shao, A., Liu, X., Shen, W., Li, Y., Wu, H., Pan, X., Yang, Z., Xu, Y., Zhu, T., Wang, Y., et al. (2025) · npj Digital Medicine

Retinal Imaging and AnalysisArtificial Intelligence in Healthcare and Education

Cited by 16

Have AI-Generated Texts from LLM Infiltrated the Realm of Scientific Writing? A Large-Scale Analysis of Preprint Platforms

Cheng, H., Sheng, B., Lee, A., Chaudary, V., Atanasov, A. G., Liu, N., Qiu, Y., Wong, T. Y., Tham, Y.-C., & Zheng, Y. (2024) · bioRxiv (Cold Spring Harbor Laboratory)

Academic integrity and plagiarismAcademic Publishing and Open Access

Cited by 15

Prospective real-world implementation of deep learning systems in healthcare: a systematic review guided by implementation science

Tseng, R. M. W. W., Ong, L. C., Goh, J. H. L., Chen, Y., Chen, T., Lum, E., & Tham, Y.-C. (2026) · npj Digital Medicine

Artificial Intelligence in Healthcare and EducationHealth Policy Implementation Science

Cited by 9