Explainable artificial intelligence in endodontics: Concepts, clinical applications, and future directions.

OBJECTIVES: An inherent `black box` nature of artificial intelligence (AI) remains a significant barrier to clinical adoption. Explainable artificial intelligence (XAI) has emerged as an approach to enhance transparency and interpretability. This narrative review evaluates the evolving role of XAI, with particular emphasis on its current applications, prospective utility in endodontics, and inherent challenges. DATA AND SOURCES: A search was conducted across databases, including PubMed and Scopus, covering literature up to April 2026. The retrieved studies were subsequently synthesised narratively. STUDY SELECTION: Studies on the discourse surrounding XAI were reviewed, including the technical foundations of XAI, applications of XAI in dentistry and endodontics, the benefits and challenges of XAI in endodontics, and research gaps and future directions in XAI for endodontics. CONCLUSION: The investigation of XAI in endodontics remains relatively limited. The literature review highlights the necessity of XAI systems that are methodologically rigorous, clinically relevant, and ethically sound, with their development supported by well-designed prospective studies. XAI development should be advanced as a transformative framework that strengthens the speciality's evidence base and enables interpretable, equitable, and precisely tailored treatment strategies. XAI has the potential to support more transparent, personalised, and reliable endodontic care. CLINICAL SIGNIFICANCE: By enhancing the transparency of AI systems, XAI have the potential to improve clinical decision-making in endodontics and contribute to the delivery of high-quality, patient-centred care.