AI in Endodontics — What It Can Do Now, and What It Still Cannot

Will AI change endodontics? The answer to this question is gradually taking shape through the systematic reviews that have appeared one after another across 2024 and 2025. The number of papers has surged since 2022, with the majority of publications concentrated in 2024–2025. This article organizes, on the basis of evidence, the current standing of AI in endodontics from two angles: clinical application and scholarly activity. The aim is neither excessive expectation nor pessimism, but to discern “what it can be used for now, and what it still cannot.”

Clinical application — centered on diagnostic imaging

At present, the area where AI research has accumulated most is diagnostic imaging. Deep learning (particularly convolutional neural networks) has been reported to show high accuracy in tasks such as detecting apical lesions, judging root fractures and root resorption, and grasping root canal anatomy.

The reason AI is effective in this area lies in a structural challenge that endodontics faces. The interpretation of radiographic images has a limitation: intra- and inter-observer agreement is not necessarily high. AI-based image analysis is expected to serve as a support tool that compensates for this variability in interpretation and standardizes diagnosis. The main areas where application is advancing are as follows.

• Detection of apical lesions — support for detecting small, easily missed radiolucencies.

• Judgment of root fractures and root resorption — support for evaluating pathologies that are difficult to assess.

• Grasping root canal anatomy — morphological recognition of complex root canal systems, especially in integration with CBCT.

• Treatment planning and prognosis prediction — support for decision-making, such as predicting the success of nonsurgical retreatment.

Use in scholarly activity — supporting literature and presentations

The value of AI is not confined to the operatory. Indeed, for many clinicians, the setting where its benefit is most readily felt day to day is scholarly activity. Efficiently searching and summarizing a vast literature to keep up with the latest evidence, organizing research data, and preparing materials for conference presentations — in all of these, AI can be a powerful aid.

To practice evidence-based care, one must continually appraise the latest literature. Yet doing this alongside a busy clinical schedule is not easy. Using AI to support literature review and information gathering is a realistic means of sustaining the “capacity to keep learning.”

Understanding the limitations correctly — this is the most important point

In discussing AI’s possibilities, we must understand its limitations with equal weight. What systematic reviews uniformly point out is that the routine clinical application of AI in endodontics remains constrained by several factors.

• Lack of interpretability — it is opaque why the AI arrived at a given judgment, making it difficult for the clinician to verify the rationale.

• The problem of generalizability — a model trained on specific data will not necessarily deliver the same performance in a different environment or population.

• Data limitations — high-quality public databases are scarce, which affects the reliability of models.

• Ethics and privacy — issues concerning the handling of patient data.

Because of these limitations, it is reasonable to position current AI not as a “replacement” for clinical judgment, but strictly as “support” for the clinician’s judgment. The responsibility for the final diagnosis and decision rests with the clinician.

The attitude required of clinicians

The key to making good use of AI lies less in mastery of the technology itself than in the professional grounding to critically appraise its output. Comparing the lesion detection or prognosis prediction that AI presents against one’s own diagnostic reasoning and judging its validity — only when this is possible does AI become a truly useful tool.

In other words, even in the age of AI — or precisely because it is the age of AI — the importance of the ability to understand pathology, appraise evidence, and judge for oneself is, if anything, growing. AI does not take over thinking; it extends the capabilities of a clinician who possesses a solid foundation of thought.

In closing

AI in endodontics is a developing technology: while application is advancing steadily, centered on diagnostic imaging, challenges remain before routine clinical adoption. Avoiding excessive expectation, discerning what it can and cannot do, and using it wisely in both scholarly activity and clinical practice — that is the very attitude required of the dentist going forward. AI is not an end, but a means to better diagnosis and treatment.

This article was prepared on the basis of several systematic reviews and literature reviews on the application of AI in endodontics published in 2024–2025.