Flags on follow-up X-rays
Possible use: Draw attention to a possible persistent lesion.
Evidence boundary: More sensitive detection can also create more false alarms. A flag does not establish treatment failure or a need for retreatment [3].
Technology • Evidence • Patient choices
AI in endodontics: a patient guide
AI may help a clinician interpret dental images or help explain information, but it cannot replace an examination, clinical judgment, or an individualized treatment plan. A promising research result is not proof that a tool improves healing or helps patients keep teeth.
Prepared by the practice editorial team with AI assistance. Clinically reviewed and approved by Dr. Jason Kung, DDS, MS, on September 30, 2026, in English and all seven translations. This approval covers this guide and the scoped Swanson and Turgeon JADA evidence additions, not separate research summaries or later additions. This educational guide does not state that our practice uses clinical AI software and is not personal medical advice.
What the research can—and cannot—tell you
Hidden canals
Possible use: Help specialists review small or additional canals on CBCT.
Evidence boundary: Extracted-tooth enhancement and a small internal test are not proof of finding every canal or improving healing [5,6].
Consumer image chatbots
Possible use: Recognize some visible abnormalities in selected images.
Evidence boundary: Important complications were often missed; treatment recommendations were not tested for patient benefit [4].
Answers and references
Possible use: Explain terminology or help prepare questions.
Evidence boundary: Trauma answers can be unsafe and references fabricated, even when simulated board answers score well [7,8].
Predicting treatment outcomes
Possible use: Explore which factors may relate to later healing.
Evidence boundary: Six reviewed models lacked external validation and formal calibration; none validated choosing retreatment versus microsurgery for the same tooth [9].
Imaging AI is not a chatbot
Imaging AI analyzes X-rays or CBCT scans for a defined task, such as marking a region that may need closer inspection. A generative chatbot produces text in response to a prompt. Fluent answers, or performance on a specialist board examination, do not establish competence to diagnose your tooth. Chatbots can invent facts and citations. Neither tool can perform your bite tests, assess symptoms in person, or take responsibility for treatment.
Potential benefits — and what they do not prove
An imaging tool might draw attention to a subtle finding or help organize a clinician's review. A chatbot might help you prepare questions or understand unfamiliar words. These are possible uses, not a guarantee of better care. More detections are not necessarily better: a useful tool must improve decisions without unnecessary scans, treatment, cost, or delay. Patient-important outcomes include pain, healing, quality of life, and keeping a tooth.
False positives and false negatives
A false positive flags disease that is not actually present; it can lead to worry, extra imaging, or unnecessary treatment. A false negative misses disease and may delay needed care. Accuracy depends on the task, image quality, the patients studied, and how the correct diagnosis was established. A high accuracy number from a selected dataset does not tell you how often the tool will be right for someone like you.
Why external validation matters
External validation tests a tool on patients and images that are separate from its development data, ideally from other clinics and scanners. Ask whether research included everyday clinical cases, different populations, and teeth with fillings or root canal materials that can create image artifacts. Validation of one task does not validate every use. Prospective studies in real care and comparisons with clinician care are needed to determine whether using a tool actually benefits patients.
What the JADA perspective supports
Swanson and colleagues discuss translating emerging technologies into clinical impact and evaluating patient-important outcomes [1]. Their September 2026 JADA paper is a translation perspective, not an AI diagnostic-accuracy study or a trial proving superior root canal outcomes. It supports asking how a promising technology becomes useful in practice; it does not justify an accuracy percentage or a promise of clinical benefit.
Does an AI alert mean my root canal failed?
No. Allihaibi and colleagues studied follow-up periapical X-rays of 376 treated teeth, using CBCT radiographic findings as the reference [3]. At tooth level, AI was more sensitive than the clinicians (67.3% versus 49.3%) but less specific (82.3% versus 92.5%): it detected more reference-positive findings while also raising more false alarms. These are study results, not this practice's performance. A persistent dark area can be shrinking during healing; an imaging finding alone is not proof of failure. The study did not show better healing or fewer unnecessary retreatments. Symptoms, examination, earlier images and follow-up all matter.
Can a consumer AI miss a serious complication?
Yes. Akyüz and colleagues tested three consumer image-and-text AI systems on 60 selected clinical cases against two endodontists' consensus [4]. Recognition of some lesions did not translate into reliable detection of complications: perforation sensitivity ranged from 0% to 22.7% across the tested systems. This was a small retrospective benchmark, not an independent-site clinical validation or a trial of patient outcomes. A confident chatbot response cannot rule out a perforation or a broken instrument, and its treatment advice should not replace assessment by an endodontist.
Can AI find hidden root canals?
Researchers are exploring this, but the study setting matters. Ji and colleagues tested image enhancement in 171 extracted teeth [5]. In a subgroup of 25 four-canal upper first molars, an additional canal was visible in 18 ordinary CBCT images, 23 enhanced images and all 25 micro-CT reference images. This laboratory comparison was not a clinical outcome study; even enhanced images missed canals. Turp and colleagues tested additional-canal detection in a held-out set of only 28 cases from one institution and scanner [6]. One case was missed. An internal test is not independent external validation. Neither study proves improved treatment success or justifies routine extra CBCT scans.
Why a better trauma prompt is not a safety guarantee
Ourang and colleagues compared chatbot answers to layperson and specialist-framed questions about four hypothetical dental-trauma scenarios [7]. Specialist questions supplied different clinical content as well as different wording, so the study cannot isolate the effect of phrasing alone. Some answers contained unsafe or fabricated advice. The 1,920 reported scores were rater-by-criterion assessments, not patient cases. Do not spend time refining a chatbot prompt instead of seeking prompt professional assessment for a dental injury.
Can convincing answers contain made-up citations?
Yes. In Jalali and colleagues' simulated endodontic oral-board study, each of the two tested models generated four fabricated citations [8]. The models answered questions about three supplied case histories; they did not examine patients or take the official board examination. A polished answer and an impressive score do not verify its references or establish safe independent clinical judgment. Open an important reference and check that it exists and actually supports the claim.
Can AI choose retreatment or microsurgery for my tooth?
The reviewed evidence does not establish that ability. Sabeti and Torabi's 2026 journal pre-proof is a scoping review and exploratory gap analysis, not a validated treatment-selection tool [9]. Across six prognostic studies, none reported external validation or formal calibration—checking whether predicted probabilities match observed outcomes. Accuracy at sorting cases is not the same as a reliable personal success percentage. No model provided validated predictions for both retreatment and microsurgery in the same scenario. Some inputs were only available after treatment or during follow-up, too late for an initial decision. Restorability, access to canals, surgical feasibility and your preferences still need clinician assessment. The pre-proof has a screening-flow discrepancy and unavailable supplementary tables; its proposed decision framework remains exploratory.
Privacy, consent, and accountability
Avoid uploading identifiable dental images, medical histories, or personal details to a public chatbot. Before an AI service receives your records, ask who receives the data, where they are stored, how long they are kept, whether they train future models, and what choices you have. WHO guidance emphasizes autonomy, safety, transparency, accountability, inclusion, and sustainability [2]. Your clinician remains responsible for interpreting findings and discussing uncertainty and alternatives.
How should I discuss an AI result with my clinician?
Narad, Sharda and Aasdhir's dental AI ethics narrative review discusses what AI can and cannot do, representative training data and fairness, privacy, and who is accountable when things go wrong [10, pp. 1000–1002]. Ask your clinician how an AI output fits your symptoms, examination and images rather than trusting an AI-generated explanation: even a plausible explanation made after the fact can mislead [10, p. 1002]. Ask how the tool performed on patients like you, and who will handle an error or concern. This review is not a diagnostic-accuracy or outcomes trial; it does not prove patient benefit or that any product is safe. This guide does not say our practice uses clinical AI.
Questions to ask your provider
What exact task is this tool designed for? Has it been tested independently on patients like me? What happens if it disagrees with your examination? Would its result change my treatment, and why? Could it lead to extra imaging or costs? Who checks its output? How are my records protected, and can I decline this use? A clear explanation of limitations is more useful than a best-case accuracy headline.
Common patient questions
Can AI tell me whether I need a root canal?
Not on its own. Your clinician combines symptoms, examination, pulp and bite tests, and appropriate imaging. An AI output is not an individualized diagnosis or treatment recommendation.
Should I get a CBCT scan just so AI can analyze it?
No. Imaging should be justified by a clinical question and the likely benefit to your care, not by the availability of AI. Discuss whether a scan is needed and whether a lower-dose alternative can answer the question.
Can I trust a dental chatbot's answer?
Use it cautiously for general explanations or preparing questions, not for diagnosis or urgent-care decisions. It can give confident but incorrect answers and fabricated references. Check important claims with your clinician.
Does this guide mean the practice uses AI to diagnose teeth?
No. This guide explains research and patient questions; it does not confirm that the practice uses any clinical AI system. Ask your provider what tools are involved in your own care.
Does an AI flag mean I need another root canal?
No. AI may flag more possible lesions but also more false alarms. A persistent radiographic shadow may be healing. Your clinician needs to interpret symptoms, examination and changes over time before recommending retreatment.
Does AI-enhanced CBCT guarantee every canal will be found?
No. Enhancement research in extracted teeth and a small internal additional-canal test both missed canals. These studies do not establish better healing, justify routine extra scans or replace specialist image review.
Should I ask a chatbot what to do after a dental injury?
Do not let a chatbot delay prompt professional assessment. Hypothetical trauma studies found unsafe or fabricated advice, and changing the prompt does not guarantee a safe answer for your injury.
Can an AI success percentage tell me whether to choose retreatment or surgery?
Not from the models in the reviewed evidence. Six prognostic studies lacked external validation and formal calibration, and none validated both options for the same tooth. Discuss the tooth's condition, treatment feasibility and your preferences with your clinician.
Explore the evidence and your care
Sources and evidence boundaries
- Swanson et al. Driving emerging technology to clinical impact. JADA. 2026;157(9):957–967.
Translation perspective; not an AI accuracy or clinical-benefit trial.
- World Health Organization. Ethics and governance of artificial intelligence for health. 2021.
Independent ethics guidance; not evidence of endodontic diagnostic accuracy.
- Allihaibi, Koller & Mannocci. Diagnostic Accuracy of a Commercial AI-based Platform in Evaluating Endodontic Treatment Outcomes on Periapical Radiographs Using CBCT as the Reference Standard. J Endod. 2025;51:898–908.
Retrospective radiographic comparison; sensitivity/specificity tradeoff, not proof of clinical failure or patient benefit.
- Akyüz, Kıvırcık & Aslan. Artificial Intelligence-assisted Periapical Radiographic Assessment: Lesion Detection, Endodontic Complication Analysis, and Review of Clinical Treatment Recommendations. J Endod. 2026;52:1194–1205.
Selected 60-case benchmark against expert consensus; missed complications and no patient-outcome trial.
- Ji et al. Construction and Evaluation of an AI-based CBCT Resolution Optimization Technique for Extracted Teeth. J Endod. 2024;50:1298–1306.
Extracted-tooth laboratory study with micro-CT reference; not external clinical validation or proven dose reduction.
- Turp et al. Automated Detection of Middle Mesial Canals in Mandibular Molars on CBCT using nnU-Net: A Retrospective Diagnostic Accuracy Study. J Endod. 2026;52:1352–1358.
Small 28-case internal held-out test from one institution/device; no independent external validation or demonstrated treatment benefit.
- Ourang et al. Artificial Intelligence Chatbots’ Performance on Dental Trauma Case-based Queries: Examining the Effect of Prompt and Content Engineering. J Endod. 2026;52:1444–1452.
Four hypothetical trauma scenarios; prompt and clinical content changed together. Not a study of injured patients.
- Jalali et al. Artificial Intelligence Chatbots Taking American Board of Endodontics Simulated Oral Board Examination. J Endod. 2026;52:1148–1156.
Three simulated cases; four fabricated citations per tested model. Not the official examination or validation of autonomous care.
- Sabeti MA, Torabi G. AI Prognostic Models in Endodontics: Scoping Review and Clinical Gap Analysis for Retreatment vs Microsurgery Decision Support. J Endod. 2026. Journal pre-proof.
Six prognostic studies, without external validation or formal calibration; exploratory treatment-choice framework. Supplied pre-proof has a screening-flow discrepancy and unavailable supplementary tables; final volume/pages not assigned here.
- Narad C, Sharda K, Aasdhir SS. Ethical dimensions of artificial intelligence integration into dental practice. JADA. 2026;157(9):999–1004.
Six-page narrative ethics review; not a diagnostic-accuracy study, patient-outcome trial, proof of benefit or product-safety evaluation.
The dental AI ethics article was read and verified as a narrative review. Its discussion informs patient questions; the article alone does not establish clinical benefit or product safety. The clinical approval of this guide and its limited scope are stated above, separately from source verification.