Cost‐Effectiveness of AI‐Assisted Detection of Apical Periodontitis on Panoramic Radiographs

Abstract

BACKGROUND: Artificial intelligence (AI) is transforming medical imaging, yet its economic impact in dentistry remains largely unexplored. AIM: This study evaluated the cost-effectiveness of AI-assisted detection of apical periodontitis on panoramic radiographs, including downstream clinical decision-making. MATERIAL AND METHODS: Using data from a randomised study on AI-assisted detection of apical lesions, a decision-analytic model was established to analyse costs and effectiveness from a German mixed-payer perspective. RESULTS: AI support reduced average costs per case and increased treatment effectiveness, outperforming unaided examiner performance. These gains were primarily driven by improved specificity, reducing false-positive detection. However, effects varied by examiner experience; junior clinicians achieved the greatest cost savings and effectiveness gains, whereas senior examiners showed reduced sensitivity and slightly lower effectiveness at similar costs. CONCLUSION: AI-assisted diagnostics offer significant potential to improve cost-effectiveness by reducing overtreatment, with benefits being most pronounced among less experienced practitioners. Adapting AI systems to individual examiners or experience levels might further enhance clinical and economic impact.

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