| A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis X Liu, L Faes, AU Kale, SK Wagner, DJ Fu, A Bruynseels, T Mahendiran, ... The lancet digital health 1 (6), e271-e297, 2019 | 1201 | 2019 |
| Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension X Liu, SC Rivera, D Moher, MJ Calvert, AK Denniston, H Ashrafian, ... The Lancet Digital Health 2 (10), e537-e548, 2020 | 629 | 2020 |
| Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension SC Rivera, X Liu, AW Chan, AK Denniston, MJ Calvert, H Ashrafian, ... The Lancet Digital Health 2 (10), e549-e560, 2020 | 530 | 2020 |
| Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility study L Faes, SK Wagner, DJ Fu, X Liu, E Korot, JR Ledsam, T Back, R Chopra, ... The Lancet Digital Health 1 (5), e232-e242, 2019 | 222 | 2019 |
| Developing specific reporting guidelines for diagnostic accuracy studies assessing AI interventions: The STARD-AI Steering Group V Sounderajah, H Ashrafian, R Aggarwal, J De Fauw, AK Denniston, ... Nature medicine 26 (6), 807-808, 2020 | 175 | 2020 |
| A global review of publicly available datasets for ophthalmological imaging: barriers to access, usability, and generalisability SM Khan, X Liu, S Nath, E Korot, L Faes, SK Wagner, PA Keane, ... The Lancet Digital Health 3 (1), e51-e66, 2021 | 173 | 2021 |
| Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI B Vasey, M Nagendran, B Campbell, DA Clifton, GS Collins, S Denaxas, ... Nature medicine 28 (5), 924-933, 2022 | 164 | 2022 |
| SPIRIT-AI and CONSORT-AI Working Group. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension X Liu, S Cruz Rivera, D Moher, MJ Calvert, AK Denniston Nat Med 26 (9), 1364-1374, 2020 | 154 | 2020 |
| Health data poverty: an assailable barrier to equitable digital health care H Ibrahim, X Liu, N Zariffa, AD Morris, AK Denniston The Lancet Digital Health 3 (4), e260-e265, 2021 | 137 | 2021 |
| A clinician's guide to artificial intelligence: how to critically appraise machine learning studies L Faes, X Liu, SK Wagner, DJ Fu, K Balaskas, DA Sim, LM Bachmann, ... Translational vision science & technology 9 (2), 7-7, 2020 | 125 | 2020 |
| Insights into systemic disease through retinal imaging-based oculomics SK Wagner, DJ Fu, L Faes, X Liu, J Huemer, H Khalid, D Ferraz, E Korot, ... Translational vision science & technology 9 (2), 6-6, 2020 | 121 | 2020 |
| DECIDE-AI: new reporting guidelines to bridge the development-to-implementation gap in clinical artificial intelligence Nature Medicine 27 (2), 186-187, 2021 | 105 | 2021 |
| Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol V Sounderajah, H Ashrafian, RM Golub, S Shetty, J De Fauw, L Hooft, ... BMJ open 11 (6), e047709, 2021 | 104 | 2021 |
| Code-free deep learning for multi-modality medical image classification E Korot, Z Guan, D Ferraz, SK Wagner, G Zhang, X Liu, L Faes, ... Nature Machine Intelligence 3 (4), 288-298, 2021 | 101 | 2021 |
| The medical algorithmic audit X Liu, B Glocker, MM McCradden, M Ghassemi, AK Denniston, ... The Lancet Digital Health 4 (5), e384-e397, 2022 | 99 | 2022 |
| Characteristics of publicly available skin cancer image datasets: a systematic review D Wen, SM Khan, AJ Xu, H Ibrahim, L Smith, J Caballero, L Zepeda, ... The Lancet Digital Health 4 (1), e64-e74, 2022 | 99 | 2022 |
| SPIRIT-AI and CONSORT-AI Working Group. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension S Cruz Rivera, X Liu, AW Chan, AK Denniston, MJ Calvert Lancet Digit Health 2 (10), e549-e560, 2020 | 91 | 2020 |
| Predicting sex from retinal fundus photographs using automated deep learning E Korot, N Pontikos, X Liu, SK Wagner, L Faes, J Huemer, K Balaskas, ... Scientific reports 11 (1), 10286, 2021 | 76 | 2021 |
| Reporting guidelines for clinical trials evaluating artificial intelligence interventions are needed Nature Medicine 25 (10), 1467-1468, 2019 | 75 | 2019 |
| A quality assessment tool for artificial intelligence-centered diagnostic test accuracy studies: QUADAS-AI V Sounderajah, H Ashrafian, S Rose, NH Shah, M Ghassemi, R Golub, ... Nature medicine 27 (10), 1663-1665, 2021 | 59 | 2021 |
Alastair DennistonConsultant Ophthalmologist/Hon Professor, University Hospitals Birmingham/University of BirminghamVerified email at bham.ac.uk
