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Artificial intelligence and machine learning algorithms for early detection of skin cancer in community and primary care settings : a systematic review

dc.contributor.authorJones, O T
dc.contributor.authorMatin, R N
dc.contributor.authorvan der Schaar, M
dc.contributor.authorPrathivadi Bhayankaram, K
dc.contributor.authorRanmuthu, C K I
dc.contributor.authorIslam, M S
dc.contributor.authorBehiyat, D
dc.contributor.authorBoscott, R
dc.contributor.authorCalanzani, N
dc.contributor.authorEmery, Jon
dc.contributor.authorWilliams, H C
dc.contributor.authorWalter, F M
dc.date.accessioned2023-12-21T00:11:21Z
dc.date.available2023-12-21T00:11:21Z
dc.date.issued2022-06
dc.identifier225747173
dc.identifier59cadf81-2921-4ab9-8adb-be9f89b1a0a0
dc.identifier.citationJones , O T , Matin , R N , van der Schaar , M , Prathivadi Bhayankaram , K , Ranmuthu , C K I , Islam , M S , Behiyat , D , Boscott , R , Calanzani , N , Emery , J , Williams , H C & Walter , F M 2022 , ' Artificial intelligence and machine learning algorithms for early detection of skin cancer in community and primary care settings : a systematic review ' , The Lancet Digital Health , vol. 4 , no. 6 , pp. e466-e476 . https://doi.org/10.1016/S2589-7500(22)00023-1en
dc.identifier.issn2589-7500
dc.identifier.otherRIS: urn:C09C073D7851E2BBE214B677E94944D3
dc.identifier.otherORCID: /0000-0002-5068-2543/work/128823995
dc.identifier.urihttp://aura-test.abdn.ac.uk/handle/2164/20027
dc.descriptionAcknowledgments This systematic review was funded by the National Institute for Health Research Policy Research Programme, conducted through the Policy Research Unit in Cancer Awareness, Screening, and Early Diagnosis (PR-PRU-1217–21601). The views expressed in this publication are those of the authors and not necessarily those of the National Health Service, the NIHR or the Department of Health and Social Care. The first author (OTJ) was also supported by the CanTest Collaborative funded by Cancer Research UK (C8640/A23385), of which FMW is Director, JE is an Associate Director, and NC is Research Fellow. During protocol development, this Review benefited from the advice of an international expert panel from the CanTest collaborative, including Willie Hamilton (University of Exeter, Exeter, UK), Greg Rubin (University of Newcastle, Newcastle, UK), Hardeep Singh (Baylor College of Medicine, Houston, TX, USA), and Niek de Wit (University Medical Center Utrecht, Utrecht, Netherlands). The research was also supported by a Cancer Research UK Cambridge Centre Clinical Research Fellowship for OTJ, and a National Health and Medical Research Council Investigator Fellowship (APP1195302) for JE. The funding sources had no role in the study design, data collection, data analysis, data interpretation, writing of the report, or in the decision to submit for publication. The authors would like to thank Isla Kuhn (Reader Services Librarian, University of Cambridge Medical Library, Cambridge, UK) for her help in developing the search strategy. We also thank Smiji Saji, who assisted with the early stages of the Review, Haruyuki Yanaoka, who assisted with the translation and assessment of papers that were written in Korean, and Steve Morris who assisted with the analysis of the data.en
dc.format.extent1728071
dc.language.isoeng
dc.relation.ispartofThe Lancet Digital Healthen
dc.subjectSDG 3 - Good Health and Well-beingen
dc.subjectCancer Research UKen
dc.subjectC8640/A23385en
dc.subjectMedical Research Council (MRC)en
dc.subjectAPP1195302en
dc.subjectSupplementary Informationen
dc.titleArtificial intelligence and machine learning algorithms for early detection of skin cancer in community and primary care settings : a systematic reviewen
dc.typeJournal articleen
dc.contributor.institutionUniversity of Aberdeen.Other Applied Health Sciencesen
dc.description.statusPeer revieweden
dc.identifier.doihttps://doi.org/10.1016/S2589-7500(22)00023-1
dc.identifier.urlhttps://doi.org/10.1016/S2589-7500(22)00023-1en
dc.identifier.vol4en
dc.identifier.iss6en


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