Barriers and Enablers for Artificial Intelligence in Dental Diagnostics: A Qualitative Study

Abstract

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.

Artificial intelligence and multidisciplinary team meetings; a communication challenge for radiologists' sense of agency and position as spider in a web?

“…This scepticism may, among other things, be related to the lack of multicentered trials to validate the AI software [9][10][11][12][13]. Secondly, there are a number of potential ethical issues regarding responsibility, reliability, and transparency attached to the AI implementation [14][15][16][17]. A third cause is derived from the professional experiencing ambiguity or mistrust in relation to the managerial justifications for buying and implementing AI-products, often associated with a lack of communication or potential conflicts of interest [6,18,19].…”

Human Remains Identification Using Micro-CT, Chemometric and AI Methods in Forensic Experimental Reconstruction of Dental Patterns after Concentrated Sulphuric Acid Significant Impact

“…A wide range of "enablers" for AI clinical implementation in dental diagnostics are already known. The opinion leaders in the field will consider these aspects to foster a further implementation of AI in dentistry [62].…”

Visual interpretation of panoramic radiographs in dental students using eye‐tracking technology

“…Nevertheless, decision‐makers must consider barriers associated with dependence on AI, accountability for clinical decisions, and associated ethical issues before widely implementing AI in dentistry (Muller et al.) 16 …”