Cost-Effectiveness of AI-Assisted Detection of Apical Periodontitis on Panoramic Radiographs.
Authors
Affiliations (4)
Affiliations (4)
- Department of Conservative Dentistry, Periodontology and Digital Dentistry, LMU Hospital, LMU, Munich, Germany.
- DTMD University for Digital Technologies in Medicine and Dentistry, Wiltz, Luxembourg.
- Department of Statistics, LMU Munich, Munich, Germany.
- Munich Center for Machine Learning (MCML), Munich, Germany.
Abstract
Artificial intelligence (AI) is transforming medical imaging, yet its economic impact in dentistry remains largely unexplored. This study evaluated the cost-effectiveness of AI-assisted detection of apical periodontitis on panoramic radiographs, including downstream clinical decision-making. 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. 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. 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.