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This study evaluated the feasibility of using artificial intelligence to flag possible melanomas in self-referred patients. Participants were recruited at two hospitals, images were analyzed by the AI and blinded dermatology experts, and suspicious lesions were excised for diagnosis. Results included 17 confirmed malignancies, demonstrating diagnostic performance comparable to specialist assessment. AI may support practical screening for earlier access despite technical limits.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/using-artificial-intelligence-as-a-melanoma-screening-tool-in-self-referred-patients/377482/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/using-artificial-intelligence-as-a-melanoma-screening-tool-in-self-referred-patients/377482.png","ImageObject",300,407,{"name":92,"@type":93},"awa","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-31","2026-09-24",true,{"@type":102,"interactionType":103,"userInteractionCount":52},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What was the study trying to achieve with artificial intelligence for melanoma detection?","Question",{"text":112,"@type":113},"To assess whether AI can flag possible melanomas in self-referred patients who are concerned that a lesion might be cancerous, supporting more timely access to diagnosis.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How were lesions selected and evaluated in the study?",{"text":117,"@type":113},"Lesions of concern were examined by a trained medical student, then scanned with the FotoFinder System. Macroscopic and dermoscopic images were assessed by three blinded dermatologists, and suspicious lesions identified by AI or clinicians were excised.",{"name":119,"@type":110,"acceptedAnswer":120},"What were the key findings about AI performance and missed melanomas?",{"text":121,"@type":113},"Seventeen confirmed malignancies were found, including 10 melanomas. Six melanomas were not flagged by the AI, and the study reports that AI’s ability to diagnose malignancy was not inferior to dermatologists assessing dermoscopic images.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},377482,1790297695,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":52,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":34,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":144},3985747858093,"https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2","Original Article  \nUsing Artificial Intelligence as a Melanoma Screening Tool in Self-Referred Patients  \nMadeleine E. Crawford, Bsc 1, Kiyana Kamali, Bsc 1, Rachel A. Dorey, Bsc 1, Olivia C. MacIntyre, Bsc 1, Kristyna Cleminson, MD 1, Michael L. MacGillivary, MD 1,  \nPeter J. Green, MS 1, Richard G. Langley, MD 1, Kerri S. Purdy, MD 1,  \nJournal of Cutaneous Medicine and Surgery 2024, Vol. 28(1) 37–43 © The Author(s) 2023  \nArticle reuse guidelines:  \n[sagepub.com/journals-permissions](sagepub.com/journals-permissions)[ ](sagepub.com/journals-permissions)[DOI: 10.1177/12034754231216967](DOI: 10.1177/12034754231216967)[ ](DOI: 10.1177/12034754231216967)[jcms.sagepub.com](jcms.sagepub.com)  \nRyan C. DeCoste, MD2, Jennette R. Gruchy, MD2, Sylvia Pasternak, MD2, Amanda Oakley, MB, ChB3, and Peter R. Hull, PhD 1  \nAbstract  \nIntroduction: Early detection of melanoma requires timely access to medical care. In this study, we examined the feasibility of using artificial intelligence (AI) to flag possible melanomas in self-referred patients concerned that a skin lesion might be cancerous.  \nMethods: Patients were recruited for the study through advertisements in 2 hospitals in Halifax, Nova Scotia, Canada. Lesions of concern were initially examined by a trained medical student and if the study criteria were met, the lesions were then scanned using the FotoFinder System® . The images were analyzed using their proprietary computer software. Macroscopic and dermoscopic images were evaluated by 3 experienced dermatologists and a senior dermatology resident, all blinded to the AI results. Suspicious lesions identified by the AI or any of the 3 dermatologists were then excised. Results: Seventeen confirmed malignancies were found, including 10 melanomas. Six melanomas were not flagged by the AI. These lesions showed ambiguous atypical melanocytic proliferations, and all were diagnostically challenging to the dermatologists and to the dermatopathologists. Eight malignancies were seen in patients with a family history of melanoma. The AI’s ability to diagnose malignancy is not inferior to the dermatologists examining dermoscopic images.  \nConclusion: AI, used in this study, may serve as a practical skin cancer screening aid. While it does have technical and diagnostic limitations, its inclusion in a melanoma screening program, directed at those with a concern about a particular lesion would be valuable in providing timely access to the diagnosis of skin cancer.  \nKeywords  \nartificial intelligence, convoluted neural networks, deep learning, dermoscopy, melanoma, melanoma detection, melanoma screening, machine learning  \nIntroduction  \nMelanoma is the leading cause of skin cancer mortality but has an excellent prognosis when diagnosed early. While thick primary cutaneous melanoma is often clinically obvious, early-stage melanoma may be difficult to diagnose. The use of computer-based learning (deep learning) has introduced a melanoma detection strategy that is equal to experienced dermatologists assessing the same dermoscopic image.1 The artificial intelligence (AI) system used in this study, FotoFinder Moleanalyzer Pro® Version 6.0 (FotoFinder Systems GmbH, Bad Birnbach, Germany), hereafter referred to as FotoFinder®, has been shown to identify melanoma, basal cell carcinoma, and most squamous cell carcinomas  \nincluding actinic keratoses. Our group and others have shown that this AI system has high sensitivity and specificity  \n1 Division of Clinical Dermatology and Cutaneous Science, Department of Medicine, Dalhousie University, Halifax, NS, Canada  \n2Department of Pathology and Laboratory Medicine, Dalhousie University, Halifax, NS, Canada  \n3Department of Medicine, Waikato Clinical Campus, University of Auckland, Hamilton, New Zealand  \nCorresponding Author:  \nPeter R. Hull, Division of Clinical Dermatology and Cutaneous Science, Department of Medicine, Dalhousie University and Nova Scotia Health, QEII 4-193 Dickson Building, 5820 Univers","cbCaitw02LaF9Dtr","https://ap.wps.com/l/cbCaitw02LaF9Dtr","pdf",624914,"English","# Abstract\n## Introduction\n## Methods\n## Results\n## Conclusion\n# Keywords\n# Introduction\n## Background and rationale\n## Study objective","[{\"question\":\"What was the study trying to achieve with artificial intelligence for melanoma detection?\",\"answer\":\"To assess whether AI can flag possible melanomas in self-referred patients who are concerned that a lesion might be cancerous, supporting more timely access to diagnosis.\"},{\"question\":\"How were lesions selected and evaluated in the study?\",\"answer\":\"Lesions of concern were examined by a trained medical student, then scanned with the FotoFinder System. Macroscopic and dermoscopic images were assessed by three blinded dermatologists, and suspicious lesions identified by AI or clinicians were excised.\"},{\"question\":\"What were the key findings about AI performance and missed melanomas?\",\"answer\":\"Seventeen confirmed malignancies were found, including 10 melanomas. Six melanomas were not flagged by the AI, and the study reports that AI’s ability to diagnose malignancy was not inferior to dermatologists assessing dermoscopic images.\"}]","Using Artificial Intelligence as a Melanoma Screening Tool in Self-Referred Patients | PDF",1790225523,18]