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"Nothing works without the doctor:" Physicians' perception of clinical decision-making and artificial intelligence.
Samhammer, David; Roller, Roland; Hummel, Patrik; Osmanodja, Bilgin; Burchardt, Aljoscha; Mayrdorfer, Manuel; Duettmann, Wiebke; Dabrock, Peter.
Afiliação
  • Samhammer D; Institute for Systematic Theology II (Ethics), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.
  • Roller R; German Research Center for Artificial Intelligence (DFKI), Berlin, Germany.
  • Hummel P; Department of Nephrology and Medical Intensive Care, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany.
  • Osmanodja B; Department of Industrial Engineering and Innovation Sciences, Philosophy and Ethics Group, TU Eindhoven, Eindhoven, Netherlands.
  • Burchardt A; Department of Nephrology and Medical Intensive Care, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany.
  • Mayrdorfer M; German Research Center for Artificial Intelligence (DFKI), Berlin, Germany.
  • Duettmann W; Department of Nephrology and Medical Intensive Care, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany.
  • Dabrock P; Division of Nephrology and Dialysis, Department of Internal Medicine III, Medical University of Vienna, Vienna, Austria.
Front Med (Lausanne) ; 9: 1016366, 2022.
Article em En | MEDLINE | ID: mdl-36606050
Introduction: Artificial intelligence-driven decision support systems (AI-DSS) have the potential to help physicians analyze data and facilitate the search for a correct diagnosis or suitable intervention. The potential of such systems is often emphasized. However, implementation in clinical practice deserves continuous attention. This article aims to shed light on the needs and challenges arising from the use of AI-DSS from physicians' perspectives. Methods: The basis for this study is a qualitative content analysis of expert interviews with experienced nephrologists after testing an AI-DSS in a straightforward usage scenario. Results: The results provide insights on the basics of clinical decision-making, expected challenges when using AI-DSS as well as a reflection on the test run. Discussion: While we can confirm the somewhat expectable demand for better explainability and control, other insights highlight the need to uphold classical strengths of the medical profession when using AI-DSS as well as the importance of broadening the view of AI-related challenges to the clinical environment, especially during treatment. Our results stress the necessity for adjusting AI-DSS to shared decision-making. We conclude that explainability must be context-specific while fostering meaningful interaction with the systems available.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Qualitative_research Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Qualitative_research Idioma: En Ano de publicação: 2022 Tipo de documento: Article