Beta testing and clinical evaluation of an artificial intelligence-powered clinical decision support system for endodontic case assessment

Background: Structured clinical digital decision support may help reduce variability in endodontic diagnosis, case difficulty assessment, and referral communication. Aim: To evaluate the agreement between a structured clinical decision support system for endodontic case assessment and independent expert endodontist judgement. Materials and Methods: Sixty anonymised simulated endodontic cases were entered into a previously developed clinical decision support system. Thirty cases were entered by an endodontist and thirty by a general dental practitioner. System-generated diagnostic and treatment assessments were independently reviewed by two blinded endodontists using standardised diagnostic terminology. Independent expert judgement served as the reference standard. Proportion agreement with 95% confidence intervals was calculated using the Wilson method. Results: Agreement with the expert reference standard ranged from 83% to 97%. Inter-operator agreement between reviewers was 95%. Discrepancies were primarily associated with incomplete clinical information. Conclusion: The system demonstrated close alignment with expert judgement in this exploratory evaluation. Structured digital prescriptions may support consistent diagnostic reasoning while preserving clinician oversight.