ArticleCureus2026
Assessing the Reliability of the ORADIII (Oral Radiology Artificial Intelligence Diagnostic - Version 3) Software Application in Rendering a Diagnosis: A Retrospective Study.
Article in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Introduction Accurate interpretation of radiographic images is crucial for general dentists in making diagnoses and treatment decisions for their patients, yet the reliability of their diagnoses is often uncertain. This study evaluates the diagnostic efficacy of the differential diagnosis software ORADIII (Oral Radiology Artificial Intelligence Diagnostic - Version 3) in interpreting jaw lesions from cone-beam computed tomography (CBCT) scans, comparing its performance to that of an oral and maxillofacial radiologist. Materials and methods A total of 100 CBCT cases were selected from a clinical archive between the years 2013 and 2023, focusing on patients with jaw lesions. Among these, 85 cases were confirmed by biopsy reports. The top three differential diagnoses provided by ORADIII were analyzed, and their diagnostic accuracy was compared with that of an oral and maxillofacial radiologist who utilized clinical information and 3D-rendering software (Invivo; Anatomage Inc., Santa Clara, CA, USA). Statistical analyses were performed to evaluate the significance of differences in diagnostic accuracy between the two methods. Results The accuracy of ORADIII was 21% when compared to the oral radiologist's diagnoses, which achieved an accuracy of 68% against a definitive biopsy. ORADIII accurately diagnosed dentigerous cysts in five out of six instances. However, statistical analysis revealed a significant difference at the 0.05 level, indicating that the oral radiologist's top differential diagnosis was more accurate than ORADIII's. Conclusion While ORADIII demonstrates potential as an adjunct tool for general dentists in diagnosing jaw lesions, it should not be relied upon as a standalone solution. The study underscores the importance of clinical expertise in achieving accurate diagnoses and highlights the need for further research to enhance ORADIII's algorithms by incorporating additional clinical data and exploring machine-learning techniques for improved diagnostic accuracy. This study reinforces the complementary roles of technology and clinical judgment in the field of dentistry.
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