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Artificial intelligence in dental clinical decision support: Concept, challenges, and progress
Peisheng Zeng1, Gengbin Cai1, Shijie Chen1
1Hospital of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Stomatology, and Guangdong Provincial Clinical Research Center of Oral Diseases, Guangzhou, China.
Purpose:
In this review, we propose and elaborate on the conceptual framework of "decision support intelligence" (DSI) in dentistry. We aimed to define DSI as the intelligent execution of evidence-based clinical decision trees, outline a preliminary implementation pathway, identify key technical challenges hindering its development, and summarize the current research progress across major dental specialties to guide future artificial intelligence (AI) integration in clinical decision-making.
Study Selection:
We searched for studies using major databases including PubMed, Web of Science, and IEEE Xplore, to construct a coherent conceptual framework and illustrate the key arguments.
Results:
The transition toward intelligent dentistry remains incomplete, with "operational intelligence" maturing while DSI lags. This review is the first to report the core concept of DSI as an intelligent execution of decision trees that simulate clinical reasoning. It outlines the preliminary implementation strategies and key developmental challenges.
Conclusions:
This review may accelerate the translation of AI from research to practical clinical tools, ultimately supporting dentists in making more standardized, efficient, and evidence-based decisions, reducing workload, and minimizing errors, particularly in resource-variable settings.
