Indonesia's oral radiology landscape stands at a transformative crossroads, where technological innovation meets persistent challenges in accessibility, infrastructure, and standardization. This presentation chronicles the nation's evolving journey toward precision dentistry, examining both obstacles and opportunities in advancing diagnostic imaging across the archipelago's diverse healthcare ecosystem.

Despite growing adoption of Cone Beam Computed Tomography (CBCT) in urban centers, significant disparities persist in equipment availability, radiological expertise, and standardized imaging protocols across Indonesia's 34 provinces. Training gaps in advanced imaging interpretation and limited integration of digital workflows impede optimal patient care, particularly in underserved regions. These challenges mirror broader Southeast Asian patterns, where resource constraints and educational infrastructure limitations affect diagnostic quality and treatment planning precision.

Artificial intelligence presents unprecedented opportunities to democratize expertise and enhance diagnostic accuracy. Recent advances in deep learning algorithms demonstrate remarkable performance in automated detection of dental pathologies, implant planning, and anatomical landmarking from CBCT datasets, achieving diagnostic accuracy exceeding 90% in multiple clinical applications. Integration of AI-assisted CBCT analysis with digital treatment planning workflows enables true precision dentistry—personalized, predictable, and minimally invasive interventions guided by three-dimensional anatomical intelligence.

This presentation proposes a collaborative framework for advancing oral radiology in resource-variable settings, emphasizing telemedicine-enabled expert consultation networks, AI-augmented diagnostic support systems, and regional training partnerships.