Explainable artificial intelligence in skin cancer recognition: A systematic review. (May 2022)

• Record Type: Journal Article
• Authors: Hauser, Katja; Kurz, Alexander; Haggenmüller, Sarah; Maron, Roman C.; von Kalle, Christof; Utikal, Jochen S.; Meier, Friedegund; Hobelsberger, Sarah; Gellrich, Frank F.; Sergon, Mildred; Hauschild, Axel; French, Lars E.; Heinzerling, Lucie; Schlager, Justin G.; Ghoreschi, Kamran; Schlaak, Max; Hilke, Franz J.; Poch, Gabriela; Kutzner, Heinz; Berking, Carola; Heppt, Markus V.; Erdmann, Michael; Haferkamp, Sebastian; Schadendorf, Dirk; Sondermann, Wiebke; Goebeler, Matthias; Schilling, Bastian; Kather, Jakob N.; Fröhling, Stefan; Lipka, Daniel B.; Hekler, Achim; Krieghoff-Henning, Eva; Brinker, Titus J.;
• Is Part Of: European journal of cancer. Volume 167(2022)
• Page Start: 54
• Page End: 69
• Publication Date: 2022-05
• Subjects: Artificial intelligence -- Dermatology -- Man-machine systems -- Skin neoplasms -- Systematic review; Cancer -- Periodicals; Neoplasms -- Periodicals; Cancer -- Périodiques; Cancer; Tumors; Electronic journals; Periodicals; Electronic journals; 616.994
• DOI: 10.1016/j.ejca.2022.02.025
• Languages: English
• ISSNs: 0959-8049

Abstract

Abstract: Background: Due to their ability to solve complex problems, deep neural networks (DNNs) are becoming increasingly popular in medical applications. However, decision-making by such algorithms is essentially a black-box process that renders it difficult for physicians to judge whether the decisions are reliable. The use of explainable artificial intelligence (XAI) is often suggested as a solution to this problem. We investigate how XAI is used for skin cancer detection: how is it used during the development of new DNNs? What kinds of visualisations are commonly used? Are there systematic evaluations of XAI with dermatologists or dermatopathologists? Methods: Google Scholar, PubMed, IEEE Explore, Science Direct and Scopus were searched for peer-reviewed studies published between January 2017 and October 2021 applying XAI to dermatological images: the search terms histopathological image, whole-slide image, clinical image, dermoscopic image, skin, dermatology, explainable, interpretable and XAI were used in various combinations. Only studies concerned with skin cancer were included. Results: 37 publications fulfilled our inclusion criteria. Most studies (19/37) simply applied existing XAI methods to their classifier to interpret its decision-making. Some studies (4/37) proposed new XAI methods or improved upon existing techniques. 14/37 studies addressed specific questions such as bias detection and impact of XAI on man-machine-interactions. However, only three of them evaluated the performance and confidence of humans using CAD systems with XAI. Conclusion: XAI is commonly applied during the development of DNNs for skin cancer detection. However, a systematic and rigorous evaluation of its usefulness in this scenario is lacking. Highlights: No evaluation of explainable artificial intelligence (XAI) for skin cancer detection has been conducted to this date. Overview of 37 studies using XAI on dermatological and dermato histological data. Analysis of the usage of XAI to inform research on its role as part of computer-aided diagnosis (CAD) systems.