SynthesisInternational dental journal2026
Robot-Assisted Oral Examination: Challenges and Innovations.
Synthesis in International dental journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo 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. MATERIALS AND
methodsThis 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.
resultsThe 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.
conclusionsCurrent 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. CLINICAL RELEVANCE: 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.