Sarah Haggenmüller, Roman C. Maron, Achim Hekler, Eva Krieghoff-Henning, Jochen S. Utikal, Maria Gaiser, Verena Müller, Sascha Fabian, Friedegund Meier, Sarah Hobelsberger, Frank F. Gellrich, Mildred Sergon, Axel Hauschild, Michael Weichenthal, Lars E. French, Lucie Heinzerling, Justin G. Schlager, Kamran Ghoreschi, Max Schlaak, Franz J. Hilke, Sandra Schuh, Isabel Wolff
- 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 viaTo 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.…
