%0 Journal Article %1 Hauser2022-de %A Hauser, Katja %A Kurz, Alexander %A Haggenmüller, Sarah %A Maron, Roman C %A von Kalle, Christof %A Utikal, Jochen S %A Meier, Friedegund %A Hobelsberger, Sarah %A Gellrich, Frank F %A Sergon, Mildred %A Hauschild, Axel %A French, Lars E %A Heinzerling, Lucie %A Schlager, Justin G %A Ghoreschi, Kamran %A Schlaak, Max %A Hilke, Franz J %A Poch, Gabriela %A Kutzner, Heinz %A Berking, Carola %A Heppt, Markus V %A Erdmann, Michael %A Haferkamp, Sebastian %A Schadendorf, Dirk %A Sondermann, Wiebke %A Goebeler, Matthias %A Schilling, Bastian %A Kather, Jakob N %A Fröhling, Stefan %A Lipka, Daniel B %A Hekler, Achim %A Krieghoff-Henning, Eva %A Brinker, Titus J %D 2022 %I Elsevier BV %J Eur. J. Cancer %K Artificial Dermatology; Man-machine Skin Systematic intelligence; neoplasms; review systems; topic_lifescience %P 54--69 %T Explainable artificial intelligence in skin cancer recognition: A systematic review %V 167 %X 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.

@article{Hauser2022-de, 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.}, added-at = {2024-09-10T11:54:51.000+0200}, author = {Hauser, Katja and Kurz, Alexander and Haggenm{\"u}ller, Sarah and Maron, Roman C and von Kalle, Christof and Utikal, Jochen S and Meier, Friedegund and Hobelsberger, Sarah and Gellrich, Frank F and Sergon, Mildred and Hauschild, Axel and French, Lars E and Heinzerling, Lucie and Schlager, Justin G and Ghoreschi, Kamran and Schlaak, Max and Hilke, Franz J and Poch, Gabriela and Kutzner, Heinz and Berking, Carola and Heppt, Markus V and Erdmann, Michael and Haferkamp, Sebastian and Schadendorf, Dirk and Sondermann, Wiebke and Goebeler, Matthias and Schilling, Bastian and Kather, Jakob N and Fr{\"o}hling, Stefan and Lipka, Daniel B and Hekler, Achim and Krieghoff-Henning, Eva and Brinker, Titus J}, biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/2b898f7460138d23964f8ade1ed2b516c/scadsfct}, copyright = {http://creativecommons.org/licenses/by-nc-nd/4.0/}, interhash = {645e78ea8fd1cc2451c26e5c4fac37f7}, intrahash = {b898f7460138d23964f8ade1ed2b516c}, journal = {Eur. J. Cancer}, keywords = {Artificial Dermatology; Man-machine Skin Systematic intelligence; neoplasms; review systems; topic_lifescience}, language = {en}, month = may, pages = {54--69}, publisher = {Elsevier BV}, timestamp = {2024-09-10T12:00:15.000+0200}, title = {Explainable artificial intelligence in skin cancer recognition: A systematic review}, volume = 167, year = 2022 }