The Economics of Accuracy: A Narrative Review of AI in Dental Radiology and its Impact on Laboratory Referral Patterns and Systemic Costs

Abstract

Background: Artificial intelligence (AI), particularly deep learning, is rapidly transforming dental radiology, demonstrating high accuracy in detecting pathologies like caries, periodontitis, and periapical lesions from panoramic and periapical radiographs. While diagnostic performance is well-studied, the economic implications and downstream effects on healthcare resource utilization remain poorly quantified. Aim: This narrative review aims to synthesize current evidence on the health economics of implementing AI diagnostic support in dental radiology, with a specific focus on modeling its impact on laboratory referral patterns (biopsies, microbiological cultures) and specialist consultations. Methods: A systematic search of literature (2010-2024) was conducted in PubMed, IEEE Xplore, Scopus, and health economics databases. Thematic analysis integrated findings from clinical validation studies, early economic models, and healthcare utilization research. Results: AI demonstrates significant potential to reduce false-positive referrals for benign conditions, decreasing unnecessary biopsies and specialist visits. Conversely, by improving sensitivity for early-stage disease, it may increase appropriate referrals for pre-malignant lesions and complex cases, shifting costs earlier in the care pathway. The economic viability hinges on implementation costs (software, integration), avoided misdiagnosis costs, and the value of earlier intervention. Current evidence is largely modeled, with real-world longitudinal data scarce. Conclusion: AI in dental radiology promises a shift towards more accurate, cost-effective triage. Realizing net economic benefit requires integrated systems that translate AI findings directly into referral decisions, coupled with standardized economic evaluations that capture long-term systemic savings from prevented disease progression.

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