Barriers and Enablers for Artificial Intelligence in Dental Diagnostics: A Qualitative Study
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/...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | Journal of Clinical Medicine |
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| Online Access: | https://www.mdpi.com/2077-0383/10/8/1612 |
| _version_ | 1797538162239602688 |
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| author | Anne Müller Sarah Marie Mertens Gerd Göstemeyer Joachim Krois Falk Schwendicke |
| author_facet | Anne Müller Sarah Marie Mertens Gerd Göstemeyer Joachim Krois Falk Schwendicke |
| author_sort | Anne Müller |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T12:26:36Z |
| format | Article |
| id | doaj.art-f29a8c7fa2dc45be830b9ba16721d522 |
| institution | Directory Open Access Journal |
| issn | 2077-0383 |
| language | English |
| last_indexed | 2024-03-10T12:26:36Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Clinical Medicine |
| spelling | doaj.art-f29a8c7fa2dc45be830b9ba16721d5222023-11-21T14:59:46ZengMDPI AGJournal of Clinical Medicine2077-03832021-04-01108161210.3390/jcm10081612Barriers and Enablers for Artificial Intelligence in Dental Diagnostics: A Qualitative StudyAnne Müller0Sarah Marie Mertens1Gerd Göstemeyer2Joachim Krois3Falk Schwendicke4Department of Oral Diagnostics, Digital Health and Health Services Research, Charité-Universitätsmedizin Berlin, Aßmannshauser Str. 4-6, 14197 Berlin, GermanyDepartment of Operative and Preventive Dentistry, Charité-Universitätsmedizin Berlin, Aßmannshauser Str. 4-6, 14197 Berlin, GermanyDepartment of Operative and Preventive Dentistry, Charité-Universitätsmedizin Berlin, Aßmannshauser Str. 4-6, 14197 Berlin, GermanyDepartment of Oral Diagnostics, Digital Health and Health Services Research, Charité-Universitätsmedizin Berlin, Aßmannshauser Str. 4-6, 14197 Berlin, GermanyDepartment of Oral Diagnostics, Digital Health and Health Services Research, Charité-Universitätsmedizin Berlin, Aßmannshauser Str. 4-6, 14197 Berlin, GermanyThe 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.https://www.mdpi.com/2077-0383/10/8/1612artificial intelligenceradiography dental digitalqualitative researchmodels psychologicalmodels theoretical |
| spellingShingle | Anne Müller Sarah Marie Mertens Gerd Göstemeyer Joachim Krois Falk Schwendicke Barriers and Enablers for Artificial Intelligence in Dental Diagnostics: A Qualitative Study Journal of Clinical Medicine artificial intelligence radiography dental digital qualitative research models psychological models theoretical |
| title | Barriers and Enablers for Artificial Intelligence in Dental Diagnostics: A Qualitative Study |
| title_full | Barriers and Enablers for Artificial Intelligence in Dental Diagnostics: A Qualitative Study |
| title_fullStr | Barriers and Enablers for Artificial Intelligence in Dental Diagnostics: A Qualitative Study |
| title_full_unstemmed | Barriers and Enablers for Artificial Intelligence in Dental Diagnostics: A Qualitative Study |
| title_short | Barriers and Enablers for Artificial Intelligence in Dental Diagnostics: A Qualitative Study |
| title_sort | barriers and enablers for artificial intelligence in dental diagnostics a qualitative study |
| topic | artificial intelligence radiography dental digital qualitative research models psychological models theoretical |
| url | https://www.mdpi.com/2077-0383/10/8/1612 |
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