Robot-Assisted Oral Examination: Challenges and Innovations

To summarize and evaluate the core components, functional modules, and developmental challenges of intelligent oral examination robots, and to clarify their potential clinical relevance as well as the mechanisms by which robotics and artificial intelligence can enhance diagnostic efficiency and standardization in dentistry.

This study employed a systematic literature review methodology, retrieving relevant publications from the PubMed and ScienceDirect databases up to September 2025. The search strategy utilized Boolean operators to combine keywords such as 'robot,' 'AI,' 'oral diagnosis,' and 'intraoral scanner,' aiming to comprehensively cover the fields of robotic technology, oral diagnostics, artificial intelligence, and related sensing technologies. The initial search yielded approximately 1,027 articles, of which 134 were ultimately included after screening. Inclusion criteria comprised studies related to oral robotic technologies, applications of artificial intelligence in diagnosis, and robotic navigation, while exclusion criteria included non-peer-reviewed publications and studies with insufficient methodological descriptions. A combined approach of narrative review and critical analysis was adopted, with a focus on key technical domains, including hardware architecture, software integration, multimodal perception systems, and force feedback mechanisms.

The analysis identified several key technological components of intelligent oral examination robots, including high-resolution intraoral imaging systems, multimodal data fusion frameworks, force-sensing and adaptive control technologies, automated navigation systems, and AI-driven data-processing algorithms. These systems contribute to improved image acquisition accuracy, standardized diagnostic procedures, enhanced data management, and optimized clinical workflow. However, challenges remain in system integration, safety assurance, real-time responsiveness, and clinical validation.

Current technological advances suggest that intelligent oral examination robots have significant potential to reduce clinician workload, improve diagnostic accuracy, and enhance consistency in oral examinations. Continued refinement in hardware-software integration, safety mechanisms, and clinical adaptability will be essential for broader implementation.

Intelligent oral examination robots may significantly improve diagnostic quality through automation, standardized data acquisition, and enhanced imaging capabilities. Their application could facilitate early disease detection, improve patient management, and streamline clinical workflows in modern dental practice.