Artificial Intelligence-Powered Tools and Clinical Decision Support Systems for Prosthodontic Treatments

Abstract

Objective: To provide a narrative overview of the applications, performance, and limitations of artificial intelligence (AI) diagnostic tools and clinical decision support systems (CDSS) in prosthodontics, spanning diagnosis, treatment planning, prosthesis design, and prognosis.

Place and Duration of Study: This study was conducted at the Department of Prosthodontics and Implantology, College of Dentistry, King Faisal University, Al-Ahsa, Saudi Arabia, from September 2025 to March 2026.

Methods: A narrative review of the literature was conducted, synthesising evidence from published systematic reviews, scoping reviews, diagnostic-accuracy studies, and clinical reports. PubMed, Scopus, Web of Science,
Google Scholar, and Semantic Scholar were searched for English-language articles published between 2021 and 2026 using combinations of the terms “artificial intelligence,” “machine learning,” “deep learning,” “convolutional neural network,” “clinical decision support system,” “prosthodontics,” “fixed/removable prosthodontics,” “dental implant,” “maxillofacial prosthesis,” “tooth shade,” “occlusion,” and “digital smile design.” The review followed the Introduction-Methods-Results-Discussion structure commonly used for narrative reviews.

Results: AI diagnostic tools demonstrated high accuracy in image-based recognition tasks relevant to prosthodontics, including implant-system identification on radiographs and classification of partially edentulous
arches, while performance in objective shade matching, margin detection, and multivariable prognosis was more modest. AI-assisted methodologies in treatment planning and prosthesis design have demonstrated clinically
promising accuracy and significant workflow efficiencies. However, it is noteworthy that the majority of evidence supporting these findings originates from retrospective or simulation-based studies. CDSS, including case-based and rule-based systems, improved the consistency and evidence-basis of clinical decision-making, but real-world adoption, maturity, and clinician uptake remained limited. Persistent barriers included a lack of prospective validation, limited data standardisation, limited model interpretability, and ethical and regulatory concerns.

Conclusion: AI diagnostic tools and CDSS show considerable potential to enhance diagnostic precision, treatment planning, and prosthesis design in prosthodontics. However, current evidence is largely early-stage, and prospective clinical validation, transparent models, and clear regulatory frameworks are required before routine clinical adoption. AI is best positioned as an adjunct that supports, rather than replaces, the prosthodontist.