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Patterns of care and follow-up care of patients with uveal melanoma in German-speaking countries: a multinational survey of the German Dermatologic Cooperative Oncology Group (DeCOG) (2020)
Steeb, Theresa ; Wessely, Anja ; Alter, Mareike ; Bayerl, Christiane ; Bender, Armin ; Bruning, Guido ; Dabrowski, Evelyn ; Debus, Dirk ; Devereux, Nina ; Dippel, Edgar ; Drexler, Konstantin ; Dücker, Pia ; Dummer, Reinhard ; Emmert, Steffen ; Elsner, Peter ; Enk, Alexander ; Gebhardt, Christoffer ; Gesierich, Anja ; Goebeler, Matthias ; Goerdt, Sergij ; Goetze, Steven ; Gutzmer, Ralf ; Haferkamp, Sebastian ; Hansel, Gesina ; Hassel, Jessica C. ; Heinzerling, Lucie ; Kähler, Katharina C. ; Kaume, Kjell M. ; Krapf, Wolfgang ; Kreuzberg, Nicole ; Lehmann, Percy ; Livingstone, Elisabeth ; Löffler, Harald ; Loquai, Carmen ; Mauch, Cornelia ; Mangana, Johanna ; Meier, Friedegund ; Meissner, Markus ; Moritz, Rose K. C. ; Maul, Lara Valeska ; Müller, Verena ; Mohr, Peter ; Navarini, Alexander ; Van Nguyen, Ahn ; Pfeiffer, Christiane ; Pföhler, Claudia ; Posch, Christian ; Richtig, Erika ; Rompel, Rainer ; Sachse, Michael M. ; Sauder, Stefanie ; Schadendorf, Dirk ; Schatton, Kerstin ; Schulze, Hans-Joachim ; Schultz, Erwin ; Schilling, Bastian ; Schmuth, Matthias ; Simon, Jan. C. ; Streit, Markus ; Terheyden, Patrick ; Thiem, Alexander ; Tüting, Thomas ; Welzel, Julia ; Weyandt, Gerhard ; Wesselmann, Ulrich ; Wollina, Uwe ; Ziemer, Mirjana ; Zimmer, Lisa ; Zutt, Markus ; Berking, Carola ; Schlaak, Max ; Heppt, Markus V.
Surveillance of patients with conjunctival melanoma in German-speaking countries: a multinational survey of the German dermatologic cooperative oncology group [Letter] (2021)
Wessely, Anja ; Steeb, Theresa ; Berking, Carola ; Schlaak, Max ; Heppt, Markus V. ; Alter, Mareike ; Bayerl, Christiane ; Bender, Armin ; Bruning, Guido ; Dabrowski, Evelyn ; Debus, Dirk ; Devereux, Nina ; Dippel, Edgar ; Drexler, Konstantin ; Dücker, Pia ; Dummer, Reinhard ; Emmert, Steffen ; Elsner, Peter ; Enk, Alexander ; Gebhardt, Christoffer ; Gesierich, Anja ; Goebeler, Matthias ; Goerdt, Sergij ; Goetze, Steven ; Gutzmer, Ralf ; Haferkamp, Sebastian ; Hansel, Gesina ; Hassel, Jessica C. ; Heinzerling, Lucie ; Kähler, Katharina C. ; Kaume, Kjell M. ; Krapf, Wolfgang ; Kreuzberg, Nicole ; Lehmann, Percy ; Livingstone, Elisabeth ; Löffler, Harald ; Loquai, Carmen ; Mauch, Cornelia ; Mangana, Johanna ; Meier, Friedegund ; Meissner, Markus ; Moritz, Rose K.C. ; Maul, Lara Valeska ; Müller, Verena ; Mohr, Peter ; Navarini, Alexander ; Van Nguyen, Ahn ; Pfeiffer, Christiane ; Pföhler, Claudia ; Posch, Christian ; Richtig, Erika ; Rompel, Rainer ; Sachse, Michael M. ; Sauder, Stefanie ; Schadendorf, Dirk ; Schatton, Kerstin ; Schulze, Hans-Joachim ; Schultz, Erwin ; Schilling, Bastian ; Schmuth, Matthias ; Simon, Jan C. ; Streit, Markus ; Terheyden, Patrick ; Thiem, Alexander ; Tüting, Thomas ; Welzel, Julia ; Weyandt, Gerhard ; Wesselmann, Ulrich ; Wollina, Uwe ; Ziemer, Mirjana ; Zimmer, Lisa ; Zutt, Markus
Patients' and dermatologists' preferences in artificial intelligence–driven skin cancer diagnostics: a prospective multicentric survey study (2024)
Haggenmüller, Sarah ; Maron, Roman C. ; Hekler, Achim ; Krieghoff-Henning, Eva ; Utikal, Jochen S. ; Gaiser, Maria ; Müller, Verena ; Fabian, Sascha ; Meier, Friedegund ; Hobelsberger, Sarah ; Gellrich, Frank F. ; Sergon, Mildred ; Hauschild, Axel ; Weichenthal, Michael ; French, Lars E. ; Heinzerling, Lucie ; Schlager, Justin G. ; Ghoreschi, Kamran ; Schlaak, Max ; Hilke, Franz J. ; Schuh, Sandra ; Wolff, Isabel
To the Editor: Artificial intelligence (AI) has shown promise for improving diagnostics of skin cancer by matching or surpassing experienced clinicians.1 However, the successful clinical application depends on acceptance by patients and dermatologists. In this prospective multicentric survey study with a response rate of 63%, we therefore investigate the criteria required for patients and dermatologists to accept AI-systems and assess their importance on patients’ and dermatologists’ decision-making when considering the use of such systems. To this end, we perform an adaptive choice-based conjoint analysis and analyze it using hierarchical Bayes estimation.2 By employing an adaptive choice-based conjoint analysis, we investigate multiple influencing AI-features simultaneously (see Table I) whilst accounting for possible trade-offs (see Fig 1). For details on questionnaire development, participant recruitment, and statistical analysis, see Supplementary Methods, available via Mendeley at https://data.mendeley.com/datasets/2chcwnhpwj/1.
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