Artificial Intelligence in Human Identification in Forensic Dentistry: A Narrative Literature Review

Abstract

This study analyzes the application of Artificial Intelligence (AI) to human identification within forensic dentistry, a field in which computational tools have been progressively incorporated into the analysis of dental data and radiographic images. This study aimed to analyze the applications of artificial intelligence related to human identification in forensic dentistry, considering the reported performance of AI-based methods and their main benefits and limitations. A literature review was conducted based on the analysis of scientific articles published between 2015 and 2025. The search strategy used the terms “Artificial Intelligence”, “Machine Learning”, “Deep Learning”, “Forensic Dentistry”, “Forensic Odontology”, “Dental Forensics”, “Human Identification”, “Personal Identification”, “Age Estimation”, “Bite Mark Analysis”, “Diagnostic Imaging”, “Dental Radiography” in PubMed, SciELO, LILACS, and Google Scholar, the latter being used as a source of gray literature. Eleven studies were included, comprising literature reviews and retrospective studies. The main applications identified were related to age and sex estimation through the analysis of dental images, particularly panoramic radiographs, using deep learning models and convolutional neural networks. The studies reported promising performance of these methods; however, the findings were heterogeneous due to differences in study designs, populations, methodological approaches, and validation procedures. The findings indicate that artificial intelligence has potential to support methods related to human identification in forensic dentistry, particularly age and sex estimation. However, methodological heterogeneity among studies, population differences, and the need for further validation limit the generalizability of the findings and highlight the need for additional research before widespread application in forensic practice.