KUMJ | VOL. 23 | NO. 5 | ISSUE 93 | DECEMBER, 2026

From Images to Insight: Clinical Decision Support Systems (CDSSs) in Oral and Maxillofacial Radiology
Bali H, Neupane G, Adhikari S, Kafle D

Abstract:
The clinician’s preliminary diagnosis and the pathologist’s conclusive histopathological diagnosis, which is considered the gold standard, are not always the same. Due to time constraints and the high volume of patients, clinicians often rely on initial impressions or memorable experiences, which can potentially lead to diagnostic inaccuracies. Artificial Intelligence (AI), with its data-driven approach, offers a more objective analysis free from personal biases. Clinical Decision Support Systems (CDSSs), combine demographic, clinical, and radiological data, to generate differential diagnoses, providing clinicians with additional decision support. This, in turn, increases the efficiency and accuracy of clinical diagnoses. CDSSs have demonstrated high accuracy rates in internal medicine, especially when diagnosing common chief complaints. Bayesian Belief Networks (BBNs) are applied in both medical and dental fields to enhance diagnostic accuracy. An example is the Oral Radiographic Differential Diagnosis (ORADIII) system developed by the University of California, Los Angeles (UCLA) in the 1990s, which employs the Bayesian approach to diagnose intra-bony lesions of the jaw. Using AI in decision support for diagnosing bony jaw lesions (including cysts and tumors) lies in its potential to aid in improving clinician’s diagnostic accuracy, efficiency, and consistency. AI can further help with complex diagnoses, reduce diagnostic errors, provide support in high-volume or remote settings, as well as work educational tool for students.
Keyword : Artificial intelligence, Clinical decision support systems, Diagnostic imaging, Jaw diseases