<p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" class="first" dir="auto"
id="d3720947e198">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.
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