PURPOSE: This systematic review aimed to evaluate the role of artificial intelligence (AI)-based technologies, including machine learning algorithms, predictive analytics, and AI-assisted surgical guidance, in prosthodontic-driven planning for dental implant positioning, with particular attention to diagnostic accuracy, planning consistency, and workflow optimization. METHODS: This systematic review aimed to evaluate the role of artificial intelligence (AI)-based technologies, including machine learning algorithms, predictive analytics, and AI-assisted surgical guidance, in prosthodontic-driven planning for dental implant positioning, with particular attention to diagnostic accuracy, planning consistency, and workflow optimization. RESULTS: The included studies suggest that AI-based approaches are associated with improved consistency in diagnostic image interpretation, enhanced accuracy of virtual implant planning, and reduced operator-dependent variability. Across predominantly moderate- to high-quality studies, AI-assisted workflows were reported to support more individualized implant positioning by integrating anatomical, prosthetic, and occlusal parameters. However, the evidence remains heterogeneous, and reported benefits are primarily derived from observational and pilot studies rather than large-scale clinical trials. CONCLUSIONS: Current evidence indicates that AI has the potential to support prosthodontic-driven implant planning by enhancing diagnostic standardization and facilitating personalized treatment strategies. Nevertheless, the absence of quantitative synthesis, limited external validation of AI models, and variability in study quality warrant cautious interpretation. Further well-designed clinical studies and standardized validation protocols are required before definitive conclusions regarding clinical outcomes can be drawn.
Sokoli et al. (Mon,) studied this question.