--- title: "AI and dental x-rays: what the evidence shows so far" description: "What two peer-reviewed reviews report on AI reading dental radiographs: caries and periapical results, why lab accuracy may not carry over, and open limits." url: https://joncopeland.multisites.ai/blog/ai-dental-radiographs-evidence publisher: Copeland Consulting Services email: drcopeland@joncopelanddds.com contact: https://joncopeland.multisites.ai/contact --- # AI and dental x-rays: what the evidence shows so far Two peer-reviewed reviews report promising AI results for finding caries, periapical lesions and bone loss on dental radiographs. They also say most results come from experimental settings and note problems with data labeling, bias, and models that underperform on new data. The evidence supports AI as an aid to the clinician. Clinical and technologyPublished October 26, 20264 min read This post summarizes published research for general education by Copeland Consulting Services. It is not clinical advice. It names no products. ## What does AI do with a dental radiograph? It looks for patterns that match labeled examples, such as a cavity or a bone defect. A 2023 review in Frontiers in Dental Medicine explains the base idea: in a two-dimensional radiograph, each pixel has an intensity that represents density, and an algorithm learns patterns from those values to segment teeth, detect caries and make predictions. The 2023 review also describes how such a model is built. Clinicians label images by hand. Computer scientists prepare the data and the algorithm. Then both groups check the results together. Labeling takes time, and it shapes what the model can find. For the technology itself, see [artificial intelligence in dentistry](https://joncopeland.multisites.ai/glossary/artificial-intelligence-in-dentistry) and [digital radiography](https://joncopeland.multisites.ai/glossary/digital-radiography). ## What have studies reported? The reviews list results for caries, periapical lesions, and bone loss. The table gives examples as the reviews report them. Each figure comes from a single study within a review, under that study’s own conditions. Task Result reported in a review Review Caries prediction and depth classification Accuracy 95.3%, sensitivity 92.1%, specificity 96.3% in one study 2025 Periapical lesion detection Detection rate of 92.8%, with 142 of 153 lesions found, in one study 2025 Caries and apical periodontitis on periapical films F1-scores of 0.829 and 0.828 in one study 2025 Early lesion detection “Same accuracy or even better compared with dentists” across several studies 2023 The 2023 review adds a cost finding. It says one study compared AI with dentists for proximal caries detection and found the AI “more effective and less costly.” That is one reported study, and its setting may differ from yours. Do not read these numbers as a promise. They come from different datasets, designs and definitions, so they cannot be compared side by side. ## Why is reading radiographs hard for AI? Radiographs hide small and early changes. The 2025 review lists several problems: - Noise from patient movement, equipment quality and operator skill can create artifacts. - Carious lesions make up on average only 1.5% of a panoramic image, and shallow caries less than 0.5%. This small size is hard for neural networks. - About 40% demineralization of hard tissue is needed before a lesion shows on a radiograph. That last point cuts both ways. Early lesions are faint for a human reader and for software. The 2023 review notes that early lesions are hard to detect with deep fissures, tight interproximal contacts and secondary lesions, and that many lesions are found only at an advanced stage. ## Do study results carry over to a real office? Not automatically. The 2025 review says the field is “in its nascent stages,” with “high precision in experimental settings” but open questions about “clinical accuracy and reliability.” It also says models that depend on specific data “often underperform in new environments,” because image quality and noise differ between devices. The 2023 review makes a related point. Data from one country or region can make a model “precise but not accurate” for places with a different health system. Other limits named by the reviews: 1. Labeled data is scarce, because manual labeling is slow. 2. Privacy rules make it harder to build large, standard datasets. 3. Learning algorithms can carry bias. 4. The “black box” problem: some tools cannot explain a result. 5. Legal risk tied to malpractice and data privacy. ## What does this mean for a practice? It means AI reading fits as a second look, with the clinician in charge. The 2025 review says AI systems should “complement” clinical decision-making “without supplanting the nuanced judgment of experienced clinicians.” Before adopting any tool for radiographs, a practice can ask: 1. On what images was the tool trained and tested, and do they resemble the practice’s images? 2. Was it tested on images from other offices and devices? 3. What are its false positive and false negative rates? 4. How does it show its reasoning? 5. Who is responsible for the final read, and how is that documented? 6. Does the vendor sign a [business associate agreement](https://joncopeland.multisites.ai/glossary/business-associate-agreement)? ## What do the reviews say about other image types? The reviews cover more than bitewing and periapical films. The 2025 review reports AI work on panoramic images for caries segmentation and bone loss, on near-infrared transillumination images, and on photographs from standard phone cameras. It also reports studies on optical coherence tomography. These other image types matter for radiograph decisions. Some of the methods use no ionizing radiation, which is why researchers study them. The 2025 review notes that, despite the relatively low dose from X-rays, “there remains a risk to patients.” The 2023 review lists tooth-level tasks across panoramic, periapical and cephalometric images. It covers tooth segmentation, caries detection, periodontal bone loss and apical lesion detection. In each area, the same caveat applies: the data and the algorithm decide what the tool can find, and the clinician still decides what to do with the finding. Teams should also match any tool to their [informed consent](https://joncopeland.multisites.ai/glossary/informed-consent) and record-keeping routines. Ask your attorney how. For the wider evidence, see [AI in dentistry: what the research says](https://joncopeland.multisites.ai/blog/ai-in-dentistry-research). For the radiation side of imaging, see [cone beam CT and radiation safety](https://joncopeland.multisites.ai/blog/cbct-dentistry-radiation-safety). ## Next step The [AI in the dental practice guide](https://joncopeland.multisites.ai/guides/ai-in-the-dental-practice) covers uses, evidence and risks. To discuss a related project, [contact Dr. Copeland about consulting](https://joncopeland.multisites.ai/contact?topic=consulting). ## Frequently asked questions ### Can AI find cavities on dental X-rays? Studies in two peer-reviewed reviews report that AI models can detect caries on radiographs and other images, with some studies reporting accuracy comparable to or better than dentists. The reviews also say results come largely from experimental settings and need more clinical validation. ### Why might AI do worse in a real office than in a study? A 2025 review says models that depend on specific data often underperform in new environments, because imaging quality and noise differ between devices. A 2023 review adds that data from one region can be precise but not accurate elsewhere. ### Does AI replace the dentist's reading of a radiograph? The reviews describe AI as a tool that complements clinicians. The 2025 review says AI should enhance clinical decision-making without supplanting the nuanced judgment of experienced clinicians. ## Terms on this page - [Artificial intelligence in dentistry](https://joncopeland.multisites.ai/glossary/artificial-intelligence-in-dentistry) - [Business associate agreement](https://joncopeland.multisites.ai/glossary/business-associate-agreement) - [Digital radiography](https://joncopeland.multisites.ai/glossary/digital-radiography) - [Informed consent](https://joncopeland.multisites.ai/glossary/informed-consent) - [CBCT](https://joncopeland.multisites.ai/glossary/cbct) ## Sources 1. [Medical Science Monitor (PMC): Artificial Intelligence in Dentistry, A Narrative Review of Diagnostic and Therapeutic Applications (2025)](https://pmc.ncbi.nlm.nih.gov/articles/PMC11992950/) 2. [Frontiers in Dental Medicine (PMC): Artificial intelligence in dentistry, A review (2023)](https://pmc.ncbi.nlm.nih.gov/articles/PMC11811754/) Written by Copeland Consulting Services and checked against the sources above on October 26, 2026. Rules, data and guidance change, so check the linked page before you act on it. ## Keep reading - [AI in the dental practice: what it does, what studies show and what to checkAI in dentistry mostly means software that reads images or handles office tasks. Studies of radiograph tools show promising accuracy but often rest on weak designs. Some image tools are regulated by the FDA as prescription devices. Any tool that touches patient data raises HIPAA questions you should settle before use.](https://joncopeland.multisites.ai/guides/ai-in-the-dental-practice) - [AI in dentistry: what the research says so farTwo peer-reviewed reviews find that most dental AI work is image-based diagnosis, with promising results in experimental settings and open problems in data quality, bias, generalization, privacy and liability. The ADA describes AI as a tool that aids clinicians, and says human judgment stays essential. Standards are still being written.](https://joncopeland.multisites.ai/blog/ai-in-dentistry-research) - [Cone beam CT in dentistry: uses and radiation safetyDental cone beam CT builds a 3D image of teeth, jaws and nearby structures. The FDA says doses are generally lower than other CT exams but typically higher than conventional dental X-rays. FDA and ADA both say to use it only when other imaging cannot answer the clinical question, and to keep doses as low as reasonably achievable.](https://joncopeland.multisites.ai/blog/cbct-dentistry-radiation-safety) - [Dental case acceptance: what the research, the ADA Code and vendors sayCase acceptance is the share of presented treatment plans that patients schedule or complete. Research on patient decisions points to cost, fear and communication. The ADA Code requires dentists to inform patients of proposed treatment and reasonable alternatives. A higher rate is not an ethical goal by itself, and vendor benchmarks use different formulas.](https://joncopeland.multisites.ai/guides/dental-case-acceptance-guide) - [Dental leadership: team skills, training programs and where to learn themDental leadership is the work of guiding a dental team, a practice or a part of the profession. Core skills include clear communication, delegation, feedback and hiring. Learn them through team routines, mentors, organized dentistry such as the ADA and state societies, and formal programs like the ADEA Leadership Institute.](https://joncopeland.multisites.ai/guides/dental-leadership) - [Dental practice ownership: buying, starting, valuing, selling, associates and DSOsDentists reach ownership by starting a practice, buying one, buying in as a partner, or working as an associate or for a DSO first. ADA data show 73 percent of dentists owned a practice in 2023, down from 85 percent in 2005. Each path needs an attorney, a CPA and often a lender or broker.](https://joncopeland.multisites.ai/guides/dental-practice-ownership) ## Talk with Dr. Copeland [Contact Dr. Copeland](https://joncopeland.multisites.ai/contact)[Email](mailto:drcopeland@joncopelanddds.com)