[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-326881-105":59,"doc-detail-326881-en":134},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":127,"head_meta":129,"extra_data":131,"updated_unix":133},105,"en","artificial-intelligence-applied-to-non-invasive-imaging-modalities-in-identification-of-nonmelanoma-skin-cancer-a-systematic-review","Artificial Intelligence Applied to Non-Invasive Imaging Modalities in Identification of Nonmelanoma Skin Cancer - A Systematic Review","","Artificial intelligence diagnostic algorithms are evaluated for early detection of nonmelanoma skin cancers using noninvasive imaging modalities. The review systematically analyzes literature on AI machine learning models, measuring clinical relevance through reported accuracy, sensitivity, and specificity while assessing risk of bias. Searches covered MEDLINE, Cochrane, PubMed, and Embase (2018–2023), focusing on AI-based classification of nonmelanoma skin cancers and extracting efficacy measures with QUADAS-2. Forty-four studies were included, with overall favorable performance.",{"@graph":69,"@context":126},[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/artificial-intelligence-applied-to-non-invasive-imaging-modalities-in-identification-of-nonmelanoma-skin-cancer-a-systematic-review/326881/",{"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/artificial-intelligence-applied-to-non-invasive-imaging-modalities-in-identification-of-nonmelanoma-skin-cancer-a-systematic-review/326881.png","ImageObject",300,407,{"name":92,"@type":93},"Patrick","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-23","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":52},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118,122],{"name":109,"@type":110,"acceptedAnswer":111},"What is the purpose of this systematic review?","Question",{"text":112,"@type":113},"To systematically analyze the current literature on AI machine learning models using noninvasive imaging for early detection of nonmelanoma skin cancers, and to evaluate their accuracy and clinical relevance while assessing bias risk.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which databases and time range were searched?",{"text":117,"@type":113},"MEDLINE, Cochrane, PubMed, and Embase were searched for peer-reviewed studies published between 2018 and 2023.",{"name":119,"@type":110,"acceptedAnswer":120},"How many studies were included and what imaging modalities were used?",{"text":121,"@type":113},"A total of 44 studies were included: 40 using dermoscopy, 3 using reflectance confocal microscopy (RCM), and 1 using hyperspectral epidermal imaging (HEI).",{"name":123,"@type":110,"acceptedAnswer":124},"What overall performance did the AI algorithms show?",{"text":125,"@type":113},"Across all imaging modalities combined, the average accuracy was 86.80% (and the same average for dermoscopy). The review reports limited measurement of accuracy for RCM and none for HEI, indicating areas needing further research.","https://schema.org",{"og:url":83,"og:type":128,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":130,"canonical":83},"index,follow",{"doc_id":132,"site_id":62},326881,1790127590,{"code":4,"msg":5,"data":135},{"doc_id":132,"user_id":136,"nickname":92,"user_avatar":137,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":138,"file_id":139,"file_url":140,"file_type":141,"file_size":142,"view_count":52,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":143,"language":144,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":145,"faqs":146,"seo_title":147,"seo_description":67,"update_tm":148,"read_time":149},549758146520,"https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470","cancers   \nSystematic Review  \nArtificial Intelligence Applied to Non-Invasive Imaging Modalities in Identification of Nonmelanoma Skin Cancer:  \nA Systematic Review  \nEmilie A. Foltz 1,2, *, Alexander Witkowski 1, Alyssa L. Becker 1,3, Emile Latour 4, Jeong Youn Lim 4, Andrew Hamilton 1 and Joanna Ludzik 1  \nCitation: Foltz, E.A.; Witkowski, A.; Becker, A.L.; Latour, E.; Lim, J.Y.; Hamilton, A.; Ludzik, J. Artificial Intelligence Applied to Non-Invasive Imaging Modalities in Identification of Nonmelanoma Skin Cancer: A Systematic Review. Cancers 2024, 16, 629. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)cancers16030629  \nAcademic Editor: David Wong  \nReceived: 6 December 2023  \nRevised: 28 January 2024  \nAccepted: 29 January 2024  \nPublished: 1 February 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Dermatology, Oregon Health & Science University, Portland, OR 97201, USA  \n2 Elson S. Floyd College of Medicine, Washington State University, Spokane, WA 99202, USA  \n3 John A. Burns School of Medicine, University of Hawai’i at Manoa, Honolulu, HI 96813, USA  \n4 Biostatistics Shared Resource, Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97201, USA  \n* Correspondence: [emilie.foltz@wsu.edu](emilie.foltz@wsu.edu)  \nSimple Summary: Artificial intelligence (AI) has shown promise in detecting and diagnosing nonmelanoma skin cancer through image analysis. The incidence of skin cancer continues to rise each year, and it is estimated that one in five Americans will have nonmelanoma skin cancer at some point in their lifetime. Non-invasive diagnostic tools are becoming more widely adopted as the standard of care. When integrated with AI, there is the potential to identify skin cancer earlier and more rapidly compared to traditional methods. This review aims to assess the current status of AI diagnostic algorithms in tandem with noninvasive imaging for the detection of nonmelanoma skin cancer.  \nAbstract: Background: The objective of this study is to systematically analyze the current state of the literature regarding novel artificial intelligence (AI) machine learning models utilized in noninvasive imaging for the early detection of nonmelanoma skin cancers. Furthermore, we aimed to assess their potential clinical relevance by evaluating the accuracy, sensitivity, and specificity of each algorithm and assessing for the risk of bias. Methods: Two reviewers screened the MEDLINE, Cochrane, PubMed, and Embase databases for peer-reviewed studies that focused on AI-based skin cancer classification involving nonmelanoma skin cancers and were published between 2018 and 2023. The search terms included skin neoplasms, nonmelanoma, basal-cell carcinoma, squamouscell carcinoma, diagnostic techniques and procedures, artificial intelligence, algorithms, computer systems, dermoscopy, reflectance confocal microscopy, and optical coherence tomography. Based on the search results, only studies that directly answered the review objectives were included and the efficacy measures for each were recorded. A QUADAS-2 risk assessment for bias in included studies was then conducted. Results: A total of 44 studies were included in our review; 40 utilizing dermoscopy, 3 using reflectance confocal microscopy (RCM), and 1 for hyperspectral epidermal imaging (HEI) . The average accuracy of AI algorithms applied to all imaging modalities combined was 86.80%, with the same average for dermoscopy. Only one of the three studies applying AI to RCM measured accuracy, with a result of 87% . Accuracy was not measured in regard to AI based HEI interpretation. Conclusion: AI algorithms","cbCaieXDZsvxOUfl","https://ap.wps.com/l/cbCaieXDZsvxOUfl","pdf",1234486,13,"English","# Systematic Review Overview\n## Objective\n## Methods\n## Results\n## Conclusions\n# Background\n## Nonmelanoma Skin Cancer and Early Detection","[{\"question\":\"What is the purpose of this systematic review?\",\"answer\":\"To systematically analyze the current literature on AI machine learning models using noninvasive imaging for early detection of nonmelanoma skin cancers, and to evaluate their accuracy and clinical relevance while assessing bias risk.\"},{\"question\":\"Which databases and time range were searched?\",\"answer\":\"MEDLINE, Cochrane, PubMed, and Embase were searched for peer-reviewed studies published between 2018 and 2023.\"},{\"question\":\"How many studies were included and what imaging modalities were used?\",\"answer\":\"A total of 44 studies were included: 40 using dermoscopy, 3 using reflectance confocal microscopy (RCM), and 1 using hyperspectral epidermal imaging (HEI).\"},{\"question\":\"What overall performance did the AI algorithms show?\",\"answer\":\"Across all imaging modalities combined, the average accuracy was 86.80% (and the same average for dermoscopy). The review reports limited measurement of accuracy for RCM and none for HEI, indicating areas needing further research.\"}]","Artificial Intelligence Applied to Non-Invasive Imaging Modalities in Identification of Nonmelanoma Skin Cancer - A Systematic Review | PDF",1789975644,33]