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For: Lennox-Chhugani N, Chen Y, Pearson V, Trzcinski B, James J. Women's attitudes to the use of AI image readers: a case study from a national breast screening programme. BMJ Health Care Inform 2021;28:e100293. [PMID: 33795236 DOI: 10.1136/bmjhci-2020-100293] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
Number Citing Articles
1 Hogg HDJ, Al-Zubaidy M, Talks J, Denniston AK, Kelly CJ, Malawana J, Papoutsi C, Teare MD, Keane PA, Beyer FR, Maniatopoulos G; Technology Enhanced Macular Services Study Reference Group. Stakeholder Perspectives of Clinical Artificial Intelligence Implementation: Systematic Review of Qualitative Evidence. J Med Internet Res 2023;25:e39742. [PMID: 36626192 DOI: 10.2196/39742] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
2 de Vries CF, Colosimo SJ, Boyle M, Lip G, Anderson LA, Staff RT; iCAIRD Radiology Collaboration. AI in breast screening mammography: breast screening readers' perspectives. Insights Imaging 2022;13:186. [PMID: 36484919 DOI: 10.1186/s13244-022-01322-4] [Reference Citation Analysis]
3 Pesapane F, Rotili A, Valconi E, Agazzi GM, Montesano M, Penco S, Nicosia L, Bozzini A, Meneghetti L, Latronico A, Pizzamiglio M, Rossero E, Gaeta A, Raimondi S, Pizzoli SFM, Grasso R, Carrafiello G, Pravettoni G, Cassano E. Women’s perceptions and attitudes to the use of AI in breast cancer screening: a survey in a cancer referral centre. BJR 2022. [DOI: 10.1259/bjr.20220569] [Reference Citation Analysis]
4 Koh DM, Papanikolaou N, Bick U, Illing R, Kahn CE Jr, Kalpathi-Cramer J, Matos C, Martí-Bonmatí L, Miles A, Mun SK, Napel S, Rockall A, Sala E, Strickland N, Prior F. Artificial intelligence and machine learning in cancer imaging. Commun Med (Lond) 2022;2:133. [PMID: 36310650 DOI: 10.1038/s43856-022-00199-0] [Reference Citation Analysis]
5 Jenkinson G, Houghton N, van Zalk N, Waller J, Bello F, Tzemanaki A. Acceptability of Automated Robotic Clinical Breast Examination: A Survey (Preprint). Journal of Participatory Medicine 2022. [DOI: 10.2196/42704] [Reference Citation Analysis]
6 Ng AY, Glocker B, Oberije C, Fox G, Nash J, Karpati E, Kerruish S, Kecskemethy PD. A novel workflow for the safe and effective integration of AI as supporting reader in double reading breast cancer screening: A large-scale retrospective evaluation.. [DOI: 10.1101/2022.06.22.22276751] [Reference Citation Analysis]
7 Grimm LJ, Plichta JK, Hwang ES. More Than Incremental: Harnessing Machine Learning to Predict Breast Cancer Risk. J Clin Oncol 2022;:JCO2102733. [PMID: 35245093 DOI: 10.1200/JCO.21.02733] [Reference Citation Analysis]
8 Ng WY, Zhang S, Wang Z, Ong CJT, Gunasekeran DV, Lim GYS, Zheng F, Tan SCY, Tan GSW, Rim TH, Schmetterer L, Ting DSW. Updates in deep learning research in ophthalmology. Clin Sci (Lond) 2021;135:2357-76. [PMID: 34661658 DOI: 10.1042/CS20210207] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]