| 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 | 1723 | 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 | 973 | 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 | 783 | 2020 |
| 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, ... bmj 377, 2022 | 424 | 2022 |
| 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 | 298 | 2021 |
| 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 | 295 | 2019 |
| 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 | 211 | 2020 |
| 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 | 160 | 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 | 155 | 2020 |
| DECIDE-AI: new reporting guidelines to bridge the development-to-implementation gap in clinical artificial intelligence Nature Medicine 27 (2), 186-187, 2021 | 144 | 2021 |
| 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 | 130 | 2021 |
| Efficacy and adverse events of aflibercept, ranibizumab and bevacizumab in age-related macular degeneration: a trade-off analysis MK Schmid, LM Bachmann, L Fäs, AG Kessels, OM Job, MA Thiel British Journal of Ophthalmology 99 (2), 141-146, 2015 | 117 | 2015 |
| Quantitative analysis of OCT for neovascular age-related macular degeneration using deep learning G Moraes, DJ Fu, M Wilson, H Khalid, SK Wagner, E Korot, D Ferraz, ... Ophthalmology 128 (5), 693-705, 2021 | 111 | 2021 |
| Diagnostic accuracy of the Amsler grid and the preferential hyperacuity perimetry in the screening of patients with age-related macular degeneration: systematic review and meta … L Fäs, NS Bodmer, LM Bachmann, MA Thiel, MK Schmid Eye 28 (7), 788-796, 2014 | 108 | 2014 |
| Reporting guidelines for clinical trials evaluating artificial intelligence interventions are needed Nature Medicine 25 (10), 1467-1468, 2019 | 95 | 2019 |
| Extension of the CONSORT and SPIRIT statements X Liu, L Faes, MJ Calvert, AK Denniston The Lancet 394 (10205), 1225, 2019 | 70 | 2019 |
| Clinically relevant deep learning for detection and quantification of geographic atrophy from optical coherence tomography: a model development and external validation study G Zhang, DJ Fu, B Liefers, L Faes, S Glinton, S Wagner, R Struyven, ... The Lancet Digital Health 3 (10), e665-e675, 2021 | 67 | 2021 |
| Evidence assessing the diagnostic performance of medical smartphone apps: a systematic review and exploratory meta-analysis KRL Rahel Buechi, Livia Faes, Lucas M Bachmann, Michael A Thiel, Nicolas S ... BMJ Open, 2017 | 63 | 2017 |
| Reliability and diagnostic performance of a novel mobile app for hyperacuity self-monitoring in patients with age-related macular degeneration MK Schmid, MA Thiel, K Lienhard, RO Schlingemann, L Faes, ... Eye 33 (10), 1584-1589, 2019 | 54 | 2019 |
| Causes of low neonatal T-cell receptor excision circles: a systematic review AA Mauracher, F Pagliarulo, L Faes, S Vavassori, T Güngör, ... The Journal of Allergy and Clinical Immunology: In Practice 5 (5), 1457-1460 …, 2017 | 50 | 2017 |
Pearse KeaneUKRI Future Leaders Fellow / University College London / Moorfields Eye HospitalZweryfikowany adres z ucl.ac.uk
