Randomized Trials of AI-Based Interventions for Oral Healthcare and Dental Education: A Systematic Review of Randomized Controlled Trials

Vollständiger Abstract

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Artificial intelligence (AI) is increasingly being incorporated into oral healthcare and dental education, yet the quality and effectiveness of randomized evidence supporting these interventions remain uncertain. We systematically reviewed randomized controlled trials (RCTs) evaluating AI-based interventions in oral healthcare and dental education. A comprehensive literature search was conducted in PubMed, Embase, ClinicalTrials.gov, and OpenGrey from database inception to 30 June 2026. Twenty-eight published RCTs involving 2306 participants and 26 registered unpublished RCTs were identified. Educational applications represented the largest category (53.6%), followed by diagnosis and clinical decision support, treatment planning, and disease monitoring. Across the included RCTs, AI-based interventions were more frequently associated with improvements in educational performance, diagnostic accuracy, treatment planning, clinical decision-making, and periodontal monitoring than conventional approaches, although findings were heterogeneous, several studies reported no significant differences, and a small number favored conventional methods. No serious adverse events were reported, but safety reporting was inconsistent. Most published RCTs presented some concerns regarding risk of bias (82.1%), primarily related to randomization, deviations from intended interventions, and selective reporting. APPRAISE-AI demonstrated predominantly moderate methodological quality (median score 44%), with recurrent weaknesses in robustness, reproducibility, and data quality. The identification of 26 registered RCTs without published results also suggests a potential risk of dissemination bias. These findings indicate that AI-based interventions show promise across multiple applications in oral healthcare and dental education, but improvements in trial methodology, reporting quality, and transparency are needed to strengthen the evidence base and support clinical implementation.