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Who Is Really Deciding? AI and Clinical Judgement in Dentistry.
1Dental Service VA Greater Los Angeles Healthcare System and Network Lead Dentist, VA Desert Pacific Network, US Department of Veterans Affairs, Los Angeles, California; School of Dentistry, University of California, Los Angeles, California; American Academy of Artificial Intelligence in Dentistry, Los Angeles, California.
International Dental Journal
|March 13, 2026
Summary
Artificial intelligence (AI) in dentistry speeds up decisions but may introduce bias. Clinicians need AI literacy to critically evaluate AI insights for better patient care and trust.
Area of Science:
- Dental Informatics
- Artificial Intelligence in Healthcare
- Clinical Decision Support Systems
Background:
- AI-enabled software is increasingly integrated into dental practice, impacting clinical workflows.
- AI promotes rapid decision-making but carries the risk of automation bias.
- The real-world performance of AI, especially in non-standard cases, requires further validation.
Purpose of the Study:
- To explore the influence of AI integration on clinical workflows in dentistry.
- To highlight the need for enhanced AI literacy among dental professionals.
- To emphasize the importance of critical evaluation of AI in clinical settings.
Main Methods:
- Analysis of AI's role in dental clinical workflows.
- Review of AI accuracy in standardized versus real-world scenarios.
- Discussion on the implications for dental education and professional development.
Main Results:
- AI integration enhances decision-making speed but may introduce bias.
- High accuracy in standardized tests does not guarantee real-world efficacy, particularly with atypical cases.
- A gap exists in validating AI performance in diverse clinical situations.
Conclusions:
- Dental professionals require comprehensive AI literacy, focusing on ethical awareness and critical judgment.
- Continuous evaluation of AI's impact on clinical reasoning is crucial.
- Dental education must equip future professionals to judiciously integrate AI insights, balancing algorithmic output with clinical expertise to foster accountability and trust.
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