Daniel Shu Wei Ting

Research interests

Publications

• PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods

  BMJ · 2025 · https://doi.org/10.1136/bmj-2024-082505

• Generative artificial intelligence and ethical considerations in health care: a scoping review and ethics checklist

  The Lancet Digital Health · 2024 · https://doi.org/10.1016/s2589-7500(24)00143-2

• Federated machine learning in healthcare: A systematic review on clinical applications and technical architecture

  Cell Reports Medicine · 2024 · https://doi.org/10.1016/j.xcrm.2024.101419

• Ethical and regulatory challenges of large language models in medicine

  The Lancet Digital Health · 2024 · https://doi.org/10.1016/s2589-7500(24)00061-x

• Large language models in medicine

  Nature Medicine · 2023 · https://doi.org/10.1038/s41591-023-02448-8

• The promise of digital healthcare technologies

  Frontiers in Public Health · 2023 · https://doi.org/10.3389/fpubh.2023.1196596

• Large language models in health care: Development, applications, and challenges

  Health care science · 2023 · https://doi.org/10.1002/hcs2.61

• Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI

  BMJ · 2022 · https://doi.org/10.1136/bmj-2022-070904

• Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI

  Nature Medicine · 2022 · https://doi.org/10.1038/s41591-022-01772-9

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

  npj Digital Medicine · 2021 · https://doi.org/10.1038/s41746-021-00438-z

• The promise of artificial intelligence: a review of the opportunities and challenges of artificial intelligence in healthcare

  British Medical Bulletin · 2021 · 10.1093/bmb/ldab016

• The promise of artificial intelligence: a review of the opportunities and challenges of artificial intelligence in healthcare

  British Medical Bulletin · 2021 · 10.1093/bmb/ldab016

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

  BMJ Open · 2021 · 10.1136/bmjopen-2020-047709

• Global Prevalence of Diabetic Retinopathy and Projection of Burden through 2045

  Ophthalmology · 2021 · https://doi.org/10.1016/j.ophtha.2021.04.027

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

  BMJ Open · 2021 · 10.1136/bmjopen-2020-047709

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

  The Lancet Digital Health · 2021 · https://doi.org/10.1016/s2589-7500(21)00043-1

• Artificial intelligence for teleophthalmology-based diabetic retinopathy screening in a national programme: an economic analysis modelling study

  The Lancet Digital Health · 2020 · 10.1016/s2589-7500(20)30060-1

• Digital technology and COVID-19

  Nature Medicine · 2020 · https://doi.org/10.1038/s41591-020-0824-5

• Logistic regression was as good as machine learning for predicting major chronic diseases

  Journal of Clinical Epidemiology · 2020 · 10.1016/j.jclinepi.2020.03.002

• Prediction of systemic biomarkers from retinal photographs: development and validation of deep-learning algorithms

  The Lancet Digital Health · 2020 · https://doi.org/10.1016/s2589-7500(20)30216-8

• Digital technology, tele-medicine and artificial intelligence in ophthalmology: A global perspective

  Progress in Retinal and Eye Research · 2020 · https://doi.org/10.1016/j.preteyeres.2020.100900

• Digital Screen Time During the COVID-19 Pandemic: Risk for a Further Myopia Boom?

  American Journal of Ophthalmology · 2020 · 10.1016/j.ajo.2020.07.034

• Novel Coronavirus disease 2019 (COVID-19): The importance of recognising possible early ocular manifestation and using protective eyewear

  British Journal of Ophthalmology · 2020 · 10.1136/bjophthalmol-2020-315994

• A deep-learning system for the assessment of cardiovascular disease risk via the measurement of retinal-vessel calibre

  Nature Biomedical Engineering · 2020 · 10.1038/s41551-020-00626-4

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

  The Lancet Digital Health · 2020 · 10.1016/s2589-7500(20)30063-7

• Artificial intelligence for teleophthalmology-based diabetic retinopathy screening in a national programme: an economic analysis modelling study

  The Lancet Digital Health · 2020 · 10.1016/s2589-7500(20)30060-1

• Digital Screen Time During the COVID-19 Pandemic: Risk for a Further Myopia Boom?

  American Journal of Ophthalmology · 2020 · 10.1016/j.ajo.2020.07.034

• Artificial Intelligence to Detect Papilledema from Ocular Fundus Photographs

  New England Journal of Medicine · 2020 · 10.1056/nejmoa1917130

• Novel Coronavirus disease 2019 (COVID-19): The importance of recognising possible early ocular manifestation and using protective eyewear

  British Journal of Ophthalmology · 2020 · 10.1136/bjophthalmol-2020-315994

• A deep-learning system for the assessment of cardiovascular disease risk via the measurement of retinal-vessel calibre

  Nature Biomedical Engineering · 2020 · 10.1038/s41551-020-00626-4

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

  The Lancet Digital Health · 2020 · 10.1016/s2589-7500(20)30063-7

• Artificial Intelligence to Detect Papilledema from Ocular Fundus Photographs

  New England Journal of Medicine · 2020 · 10.1056/nejmoa1917130

• Development and Validation of a Deep Learning System to Detect Glaucomatous Optic Neuropathy Using Fundus Photographs

  JAMA Ophthalmology · 2019 · 10.1001/jamaophthalmol.2019.3501

• Deep learning in ophthalmology: The technical and clinical considerations

  Progress in Retinal and Eye Research · 2019 · https://doi.org/10.1016/j.preteyeres.2019.04.003

• Artificial intelligence using deep learning to screen for referable and vision-threatening diabetic retinopathy in Africa: a clinical validation study

  The Lancet Digital Health · 2019 · 10.1016/s2589-7500(19)30004-4

• Development and Validation of a Deep Learning System to Detect Glaucomatous Optic Neuropathy Using Fundus Photographs

  JAMA Ophthalmology · 2019 · 10.1001/jamaophthalmol.2019.3501

• Artificial intelligence using deep learning to screen for referable and vision-threatening diabetic retinopathy in Africa: a clinical validation study

  The Lancet Digital Health · 2019 · 10.1016/s2589-7500(19)30004-4

• Artificial intelligence for diabetic retinopathy screening: a review

  Eye · 2019 · 10.1038/s41433-019-0566-0

• Artificial intelligence for diabetic retinopathy screening: a review

  Eye · 2019 · 10.1038/s41433-019-0566-0

• Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes

  JAMA · 2017 · https://doi.org/10.1001/jama.2017.18152

• Diabetic retinopathy: global prevalence, major risk factors, screening practices and public health challenges: a review

  Clinical and Experimental Ophthalmology · 2015 · https://doi.org/10.1111/ceo.12696

Current projects

No projects listed.