Ensuring responsible implementation of AI in dentistry

The gap

Evidence profile

Sourced from the future work and recommendations and stated challenges of the source papers, classified as general, spanning 3 journals.

Supporting evidence — 3 representative gaps

• A conceptual clinical reasoning framework for early syndromic recognition in dental practice (2026) · Frontiers in Oral Health · doi

  Prospective validation of the framework is essential before routine clinical implementation. Future research should follow a stepwise approach: • Content validation using structured expert consensus methods (e.g., Delphi methodology) to refine sentinel findings, thresholds, and referral criteria. • Retrospective case–control studies comparing confirmed estimate specificity, cases sensitivity, and non-syndromic syndromic diagnostic performance positive predictive value). (e.g., to • Inter-clinician reliability studies (e.g., kappa statistics) to framework application among assess consistency of general dental practitioners. • Prospective pilot studies in general dental settings to evaluate referral appropriateness, feasibility, and potential effects on time to syndromic recognition. real-world clinical utility, tools Integration with digital decision-support systems may further enhance usability. Emerging technologies such as AI-based facial phenotyping (e.g., Face2Gene, GestaltMatcher) may complement pattern recognition, although their role in routine dental practice remains exploratory (17). The framework may also be expanded to include additional sentinel findings as evidence evolves. Its educational value could be evaluated through structured assessments such as objective structured clinical examinations or script concordance testing (54).

  generalfuture workevidence 5/5
  Keywords: framework clinical structured syndromic dental prospective validation routine sentinel ndings referral value general recognition essential
• Artificial intelligence in endodontic decision-making: hallucinations and emerging challenges for clinical practice (2026) · Acta Odontologica Scandinavica · doi

  raises important ethical and medico-legal concerns. In situations where AI-generated outputs contribute to an incorrect regarding professional retreatment decision, questions accountability, standard-of-care compliance become increasingly relevant. This dimension further reinforces that such systems must remain under strict clinician supervision [8]. informed consent, and Beyond direct clinical implications, the uncritical use of LLMs may also shape diagnostic reasoning patterns among undergraduate students, residents, and early-career clinicians. In Endodontic education, excessive reliance on AI-generated recommendations without adequate critical appraisal may inadvertently weaken the development of independent diagnostic reasoning, radiographic interpretation skills, and biologically grounded clinical judgment [4, 5, 8]. Although AI and LLMs have the potential to enhance endodontic diagnosis, treatment planning, and educational support, their integration into clinical practice should remain strictly adjunctive and continuously guided by expert professional [1, 5, 6]. Until robust validation frameworks, factual verification protocols, and clinically tested multimodal models become widely available, the indiscriminate adoption of these technologies may compromise diagnostic accuracy, weaken clinical accountability, and ultimately expose patients to inappropriate therapeutic decisions [8]. Therefore, judgment the future of AI in Endodontics should be defined not solely by technological sophistication but by its ability to provide transparent, reliable, and biologically sound support that enhances diagnostic safety and strengthens responsible clinical decision-making [5, 7, 9].

  generalrecommendationsevidence 5/5
  Keywords: clinical diagnostic generated professional decision accountability become remain llms reasoning endodontic weaken biologically judgment support
• Responsible Clinical AI in Dentistry: Trust, Professional Autonomy, and Perceptions of Accountability Across Stakeholders in Romania—A Multidisciplinary Cross-Sectional Survey (2026) · Healthcare · doi

  Ensuring responsible implementation of AI in dentistry while preserving clinical reasoning, professional autonomy, patient safety, and accountability. Addressing the differences in AI awareness, literacy, and educational exposure among stakeholders. Balancing the need for governance safeguards with the need for institutional readiness, financing, professional guidance, and data-governance arrangements.

  generalstated challengesevidence 5/5
  Keywords: ensuring responsible implementation dentistry preserving clinical reasoning professional

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