Artificial Intelligence in Forensic Dentistry: Evidence-Based Insights

Abstract

Background: Artificial intelligence (AI) is increasingly being investigated as a decision-support technology in forensic dentistry (forensic odontology). Potential applications include antemortem-postmortem dental comparison, candidate retrieval from large radiographic databases, dental age estimation, biological-sex estimation, and structured documentation in disaster victim identification (DVI).
Methods: This narrative, evidence-informed review prioritized peer-reviewed systematic and scoping reviews, representative validation studies, the 2023 National Institute of Standards and Technology (NIST) scientific-foundation review of bite-mark analysis, the 2023 INTERPOL DVI Guide, and applicable Indian legal and data-protection sources available through July 2026.
Results: Machine-learning and deep-learning models can detect complex image patterns and may improve speed and consistency in defined tasks. However, high performance reported in retrospective research datasets should not be equated with proven casework reliability: much of the literature remains limited by small or single-population datasets, heterogeneous methods, limited external validation, incomplete reporting of uncertainty, and the risk of spectrum or selection bias. Bite-mark comparison requires particular caution, because contemporary scientific-foundation reviews have found insufficient support for its core premises, and AI cannot correct limitations arising from biological distortion, unreliable ground truth, or an unvalidated forensic method. In India, implementation additionally requires attention to data protection, cybersecurity, chain of custody, electronic-record requirements, expert interpretation, and the preservation of human oversight.
Conclusions: The present evidence supports AI as an assistive and triage tool in forensic odontology, not as an autonomous source of identification or a substitute for a qualified forensic odontologist. Responsible adoption depends on representative data, external validation, transparent reporting, and enforceable governance safeguards.