Fuzzy Logic and Fuzzy-Based Artificial Intelligence in Dentistry: A Systematic Review.

Background/

Objectives

Fuzzy logic has been increasingly investigated for managing uncertainty in dental diagnosis, risk assessment, image analysis, and clinical decision support. However, the available literature remains fragmented across computational methodologies and clinical applications. This systematic review synthesized the available evidence regarding the diagnostic, predictive, and clinical decision-support performance of fuzzy logic and fuzzy-based artificial intelligence systems in dentistry.

Methods

A systematic literature search was conducted in PubMed/MEDLINE, Scopus, Web of Science, Embase, the Cochrane Library, and IEEE Xplore in accordance with PRISMA 2020 guidelines. The review protocol was prospectively registered in the Open Science Framework (Registration No. zs3w6). Study selection was performed independently by two reviewers, while data extraction and methodological quality assessments were performed by one reviewer and subsequently verified by a second reviewer. Eligible studies evaluated fuzzy logic or fuzzy-based AI methodologies across dental applications.

Results

Fifteen studies published between 2005 and 2025 met the inclusion criteria. Applications primarily involved dental disease diagnosis, periodontal risk assessment, oral cancer risk prediction, and radiographic image segmentation. Reported diagnostic accuracy ranged from 82.1% to 100%. However, the evidence base consisted predominantly of proof-of-concept, retrospective, or simulation-based investigations using relatively small datasets, with external validation and prospective clinical evaluation rarely reported.

Conclusions

Fuzzy logic and fuzzy-based AI systems demonstrate potential for uncertainty-sensitive dental applications involving diagnostic decision support, risk assessment, and radiographic image analysis. However, the current evidence remains exploratory and methodologically heterogeneous, with limited prospective validation and minimal real-world clinical implementation. Larger, externally validated studies are needed before routine clinical adoption can be recommended.