ReviewJournal of pharmacy & bioallied sciences2025
AI-Driven Innovations in Forensic Odontology: Challenges and Opportunities.
Review in Journal of pharmacy & bioallied sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Forensic odontology serves as a pivotal domain in the forensic sciences, contributing significantly to personal identification in criminal investigations, disaster victim identification, and age estimation scenarios. With the advent of artificial intelligence (AI), the landscape of forensic odontology is rapidly evolving, offering more precise, automated, and efficient analysis tools. This paper critically evaluates the recent AI-driven developments in forensic odontology, including machine learning algorithms for dental identification, pattern recognition in bite mark analysis, and age estimation using radiographic imaging. Furthermore, it addresses the current challenges, including data scarcity, ethical considerations, legal acceptability, and technical limitations. Finally, the article proposes future directions for integrating AI more robustly and ethically within forensic odontology.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.