Müller Anne, Mertens Sarah Marie, Göstemeyer Gerd, Krois Joachim, Schwendicke Falk
Department of Oral Diagnostics, Digital Health and Health Services Research, Charité-Universitätsmedizin Berlin, Aßmannshauser Str. 4-6, 14197 Berlin, Germany.
Department of Operative and Preventive Dentistry, Charité-Universitätsmedizin Berlin, Aßmannshauser Str. 4-6, 14197 Berlin, Germany.
J Clin Med. 2021 Apr 10;10(8):1612. doi: 10.3390/jcm10081612.
The present study aimed to identify barriers and enablers for the implementation of artificial intelligence (AI) in dental, specifically radiographic, diagnostics. Semi-structured phone interviews with dentists and patients were conducted between the end of May and the end of June 2020 (convenience/snowball sampling). A questionnaire developed along the Theoretical Domains Framework (TDF) and the Capabilities, Opportunities and Motivations influencing Behaviors model (COM-B) was used to guide interviews. Mayring's content analysis was employed to point out barriers and enablers. We identified 36 barriers, conflicting themes or enablers, covering nine of the fourteen domains of the TDF and all three determinants of behavior (COM). Both stakeholders emphasized chances and hopes for AI. A range of enablers for implementing AI in dental diagnostics were identified (e.g., the chance for higher diagnostic accuracy, a reduced workload, more comprehensive reporting and better patient-provider communication). Barriers related to reliance on AI and responsibility for medical decisions, as well as the explainability of AI and the related option to de-bug AI applications, emerged. Decision-makers and industry may want to consider these aspects to foster implementation of AI in dentistry.
本研究旨在确定牙科领域，特别是放射诊断中人工智能（AI）实施的障碍和促进因素。2020年5月底至6月底，对牙医和患者进行了半结构化电话访谈（便利抽样/滚雪球抽样）。一份依据理论领域框架（TDF）和影响行为的能力、机会及动机模型（COM-B）编制的问卷被用于指导访谈。采用迈林的内容分析法指出障碍和促进因素。我们确定了36个障碍、相互冲突的主题或促进因素，涵盖了TDF十四个领域中的九个以及行为的所有三个决定因素（COM）。双方利益相关者都强调了人工智能的机遇和希望。确定了一系列在牙科诊断中实施人工智能的促进因素（例如，提高诊断准确性的机会、减少工作量、更全面的报告以及更好的医患沟通）。出现了与依赖人工智能和医疗决策责任相关的障碍，以及人工智能的可解释性和调试人工智能应用程序的相关选项。决策者和行业可能希望考虑这些方面，以促进人工智能在牙科领域的实施。
