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

MCML Authors

Tobias Brock

→ Group Thomas Nagler
Computational Statistics & Data Science

Abstract

Background: Artificial intelligence (AI) is transforming medical imaging, yet its economic impact in dentistry remains largely unexplored.<br>Aim: This study evaluated the cost-effectiveness of AI-assisted detection of apical periodontitis on panoramic radiographs, including downstream clinical decision-making.<br>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.<br>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.<br>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.

article LPB+26b