Impact of artificial intelligence assistance on diagnosing periapical radiolucencies: A randomized controlled trial

Abstract

OBJECTIVES: This randomized controlled trial aimed to evaluate the impact of artificial intelligence (AI) assistance on dentists' diagnostic accuracy, confidence, and treatment decisions when detecting periapical radiolucencies (PRs) on panoramic radiographs. We specifically investigated whether AI support influenced diagnostic performance across different levels of clinical experience. METHODS: Thirty dentists with varying levels of experience evaluated 50 panoramic radiographs for the presence or absence of PRs, with and without the aid of AI, using a cross-over design. Diagnostic performance metrics, confidence scores, and clinical decision choices were analyzed. CBCT scans served as the reference standard. Outcomes included sensitivity, specificity, positive and negative predictive values, overall diagnostic accuracy, and area under the ROC and AFROC curves. Statistical analyses were conducted using mixed-effects regression models. RESULTS: AI assistance significantly improved overall diagnostic accuracy (91.6 % unaided vs. 93.3 % AI-aided; p < 0.001), mainly by reducing false positive diagnoses (false positive rate: 4.3 % unaided vs. 2.0 % AI-aided). Sensitivity remained stable (46.0 % unaided vs. 45.8 % AI-aided). Junior dentists showed the greatest improvements in performance and confidence. AI support shifted treatment decisions toward more conservative approaches. CONCLUSIONS: AI assistance modestly enhanced dentists' diagnostic accuracy for detecting periapical radiolucencies, primarily by decreasing false positive diagnoses. Junior dentists benefited most from AI support. Integration of AI in diagnostic workflows may reduce overtreatment and enhance diagnostic consistency, especially among less experienced clinicians. CLINICAL SIGNIFICANCE: The integration of AI support in dental diagnostics reduced false positive diagnoses and supported more conservative treatment decisions, particularly benefiting less experienced clinicians. These findings suggest that AI assistance can enhance diagnostic consistency and reduce overtreatment in clinical dental practice.

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