ArticleQuantitative imaging in medicine and surgery2023
Comparison of artificial intelligence
Article in Quantitative imaging in medicine and surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed, 2 syntheses or guidelines pooled it, 29 citations in OpenAlex.
- Artificial intelligence for binary dental caries diagnosis using intraoral images and dental radiographs: a systematic review and meta-analysis.BMC oral health · 2026Pooled it
- Application and performance of artificial intelligence-based models in the detection, segmentation and classification of periapical lesions: a systematic review.Frontiers in dental medicine · 2025Pooled it
- Multicentre Evaluation of AI-Assisted Caries Detection on Panoramic Radiographs Among Early-Career Dentists.International dental journal · 2026Article
- Automated tooth numbering on panoramic radiographs versus cone-beam computed tomographs: a diagnostic accuracy study of a commercial artificial intelligence system.Dento maxillo facial radiology · 2026Article
- Image Resolution's Impact on Artificial Intelligence & Human Accuracy in Full-Mouth Radiographic Analysis.International dental journal · 2026Article
- Assessment of artificial intelligence in detecting errors on panoramic radiographs.Imaging science in dentistry · 2026Article
- Deep learning model development and clinical validation for radiographic surrogate markers of implant esthetic risk.NPJ digital medicine · 2026Article
- Evaluating AI diagnostic accuracy in approximal dental caries detection on bitewing radiographs.Clinical oral investigations · 2026Article
- Detection of Pediatric Dental Caries in Panoramic Radiograph Using Deep Learning: A Benchmark Study on MD-OPG.Sensors (Basel, Switzerland) · 2026Article
- Deep Learning-Based Dental Caries Diagnosis on Panoramic Radiographies: Performance of YOLOv8 Versus Human Observers.Diagnostics (Basel, Switzerland) · 2026Article
- Deep Learning-Based Classification of Zirconia and Metal-Supported Porcelain Fixed Restorations on Panoramic Radiographs.Diagnostics (Basel, Switzerland) · 2026Article
- AI-Based Detection of Periapical Lesions in Endodontic Radiographs: a Diagnostic Accuracy Study.Maedica · 2026Article
- Detection of Periapical Lesions Using Artificial Intelligence: A Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Comparative diagnostic accuracy of ChatGPT large language models and expert clinicians in complex oral and maxillofacial diseases.Scientific reports · 2026Article
- Evaluating the diagnostic performance of artificial intelligence-assisted decision-making software for pulmonary nodules in a resource-limited setting.Journal of clinical imaging science · 2026Article
- The Leeuwenhoek paradox in dental artificial intelligence: A narrative review of artificial intelligence-detected subclinical periapical changes and human-labeled false positives.Journal of dental sciences · 2026Review
- Article
- Artificial intelligence for dental caries diagnosis: translating algorithms to clinical practice.Frontiers in medicine · 2026Review
- Examination of new clinical dental caries in school children using real intra oral photos with artificial intelligence model YOLO-V8x.BMC oral health · 2025Article
- Diagnostic Accuracy and Efficiency of AI-Assisted Radiographic Interpretation Compared to Conventional Methods in Early Detection of Dental Caries.Journal of pharmacy & bioallied sciences · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: There is information missing in the literature about the comparison of dentists Methods: In the initial stage of the study, 2 specialist dentists evaluated the presence of caries and periapical lesions on 500 digital panoramic radiographs, and the detection time was recorded in seconds. In the second stage, 3 junior dentists and an AI software performed diagnoses on the same panoramic radiographs, and the diagnostic results and durations were recorded in seconds. Results: The AI and the three junior dentists, respectively, detected dental caries at a sensitivity (SEN) of 0.907, 0.889, 0.491, 0.907; a specificity (SPEC) of 0.760, 0.740, 0.454, 0.696; a positive predictive value (PPV) of 0.693, 0.470, 0.155, 0.666; a negative predictive value (NPV) of 0.505, 0.415, 0.275, 0.367 and a F1-score of 0.786, 0.615, 0.236, 0.768. The AI and the three junior dentists respectively detected periapical lesions at an SEN of 0.973, 0.962, 0.758, 0.958; a SPEC of 0.629, 0.421, 0.404, 0.621; a PPV of 0.861, 0.651, 0.312, 0.648; a NPV of 0.689, 0.673, 0.278, 0.546 and an F1-score of 0.914, 0.777, 0.442, 0.773. The AI software gave more accurate results, especially in detecting periapical lesions. On the other hand, in caries detection, the underdiagnosis rate was high for both AI and junior dentists. Conclusions: Regarding the evaluation time needed, AI performed faster, on average.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.