Artificial intelligence and machine learning algorithms for early detection of skin cancer in community and primary care settings: a systematic review. Issue 6 (June 2022)

• Record Type: Journal Article
• Authors: Jones, 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
• Is Part Of: Lancet. Volume 4:Issue 6(2022)
• Page Start: e466
• Page End: e476
• Publication Date: 2022-06
• Subjects: Medical care -- Data processing -- Periodicals; Medical care -- Information technology -- Periodicals; Medical informatics -- Periodicals; 610.285
• DOI: 10.1016/S2589-7500(22)00023-1
• Languages: English
• ISSNs: 2589-7500

Abstract

Summary: Skin cancers occur commonly worldwide. The prognosis and disease burden are highly dependent on the cancer type and disease stage at diagnosis. We systematically reviewed studies on artificial intelligence and machine learning (AI/ML) algorithms that aim to facilitate the early diagnosis of skin cancers, focusing on their application in primary and community care settings. We searched MEDLINE, Embase, Scopus, and Web of Science (from Jan 1, 2000, to Aug 9, 2021) for all studies providing evidence on applying AI/ML algorithms to the early diagnosis of skin cancer, including all study designs and languages. The primary outcome was diagnostic accuracy of the algorithms for skin cancers. The secondary outcomes included an overview of AI/ML methods, evaluation approaches, cost-effectiveness, and acceptability to patients and clinicians. We identified 14 224 studies. Only two studies used data from clinical settings with a low prevalence of skin cancers. We reported data from all 272 studies that could be relevant in primary care. The primary outcomes showed reasonable mean diagnostic accuracy for melanoma (89·5% [range 59·7–100%]), squamous cell carcinoma (85·3% [71·0–97·8%]), and basal cell carcinoma (87·6% [70·0–99·7%]). The secondary outcomes showed a heterogeneity of AI/ML methods and study designs, with high amounts of incomplete reporting (eg, patient demographics and methods of data collection). Few studies used data on populations with a low prevalence of skin cancers to train and test their algorithms; therefore, the widespread adoption into community and primary care practice cannot currently be recommended until efficacy in these populations is shown. We did not identify any health economic, patient, or clinician acceptability data for any of the included studies. We propose a methodological checklist for use in the development of new AI/ML algorithms to detect skin cancer, to facilitate their design, evaluation, and implementation.