Beyond the Radiographic Image: Advancing AI CDS Through Clinical Insight

Abstract

Background Artificial intelligence (AI)-based clinical Decision Support (CDS) tools are increasingly being integrated into dental practice, particularly for dental radiology. While there is interest in exploring expansion of its capabilities, many factors have contributed to its limited advancement in dentistry as compared to medicine.Objective Evidence available to train AI machine learning (ML) models is compared between medicine and dentistry. Medical databases utilize a large centralized and searchable repository to train AI machine learning (ML) models, and findings here suggest that similar infrastructure in dentistry can improve ML model performance. In combination with the multimodal approach in dentistry to validate the potential increase in quantitative and qualitative data, AI CDS tool reliability and utility can be significantly enhanced.Conclusion As the adoption of AI CDS tools in dentistry accelerates, algorithmic transparency is a prerequisite for building clinician trust and ensuring patient safety. This requires training and validating models on standardized, large-scale public databases. Such an endeavor demands unprecedented collaboration across the oral health sector.

Ähnliche Arbeiten