Clinical Decision Support Systems Using AI for Endodontic Retreatment vs. Extraction

Abstract

Clinical decision-making in endodontics often hinges on whether to pursue nonsurgical retreatment or proceed with extraction and prosthetic replacement. Traditional approaches rely heavily on clinician experience, radiographic interpretation, and patient-specific factors such as periodontal status, coronal integrity, and systemic health. However, these processes can be subjective and prone to variability. The integration of artificial intelligence (AI) into Clinical Decision Support Systems (CDSS) offers a novel framework for improving the objectivity, consistency, and accuracy of treatment planning in complex endodontic cases. AI technologies including machine learning, deep learning, and radiomics are increasingly capable of analyzing cone-beam computed tomography (CBCT) scans, periapical radiographs, and electronic health records to predict treatment outcomes with high precision. Early studies demonstrate that AI-driven models can identify prognostic indicators for both retreatment success and implant survival, enabling clinicians to weigh therapeutic options more systematically. Despite these advances, challenges remain regarding data standardization, algorithm transparency, and medico-legal accountability. Furthermore, patient-centered considerations, including cost-effectiveness and individual preferences, must be integrated into AI-supported recommendations. Looking ahead, the convergence of multimodal data, validated predictive models, and user-friendly interfaces may foster collaborative human–AI decision-making, supporting clinicians while preserving the primacy of professional judgment. Ultimately, AI-enabled CDSS has the potential to enhance clinical outcomes, optimize resource allocation, and promote shared decision-making in the management of teeth requiring complex restorative or surgical interventions.