Revolutionizing endodontics: the impact and innovations of artificial intelligence

Background Artificial intelligence (AI) has been increasingly applied in the diagnosis and treatment of endodontic conditions. This narrative review summarizes the current literature on AI applications in endodontics from a clinical workflow perspective, discussing challenges in translating these approaches to practice and highlighting areas where further research is needed to better understand their effectiveness and limitations.

Methods This narrative review summarizes applications and innovations of AI in endodontics up to July 2025. Relevant literature was identified through searches of PubMed and Web of Science using a combination of MeSH terms and free-text keywords covering areas such as diagnosis, image analysis, treatment planning, and prognosis prediction. Studies were selected for their relevance to clinical or experimental applications, and a few early, foundational studies were also included to provide context and show how AI has developed in this field.

Results AI, leveraging its efficient image processing and pattern recognition capabilities, has demonstrated considerable advantages in endodontic imaging analysis. Evidence indicates that AI can assist clinicians in disease diagnosis, treatment planning, and prognosis assessment, thereby enhancing the quality of endodontic care across multiple clinical steps. Moreover, applications of AI in root canal anatomy identification, lesion detection, and integration into digital workflows further optimize clinical decision-making and procedural efficiency.

Conclusions Despite its promising potential in endodontics, the routine clinical application of AI remains limited by several factors, including the scarcity of publicly available databases, insufficient interpretability of algorithms, and ethical and privacy concerns. To achieve comprehensive integration of AI into clinical practice, future efforts could focus on multicenter validation studies, data sharing initiatives, development of interpretable AI models, and establishment of ethical and regulatory frameworks through interdisciplinary collaboration.