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Artificial Intelligence and Identification of the Deceased: a Narrative Review With Implications in Forensic Science.

  • Published In: Behavioral Sciences & the Law, 2025, v. 43, n. 3. P. 341 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Siwan, Damini; Rana, Akansha; Krishan, Peehul; Sharma, Vishal; Krishan, Kewal 3 of 3

Abstract

Identification of the dead is of utmost importance in mass disasters, war crimes, and forensic examinations. The biological profile, established by a forensic anthropologist is one the necessary steps involved in the identification of the dead. Several parameters can be estimated such as sex, age, stature, biogeographical affinity, and DNA profile of the unknown person. It is crucial to estimate these parameters of identification which may narrow down the investigation process. On the other hand, Artificial Intelligence (AI) in the modern world is showing magical uses in different fields. This communication aims to highlight the uses of AI tools for predicting parameters such as sex, age, stature, biogeographical affinity, and DNA profile of unknown persons with more accuracy and in less time. A literature search was conducted using databases PubMed, Scopus, Web of Science, and ScienceDirect for analyzing the use of artificial intelligence, machine learning, and deep learning algorithms for establishing the biological profile in disaster victim identification (DVI) and forensic casework. Moreover, this research foresees a paradigm shift in investigative techniques as technology advances, highlighting the convergence of AI and anthropological ideas for an improved understanding of the biological profiles of unknown deceased individuals. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Behavioral Sciences & the Law. 2025/06, Vol. 43, Issue 3, p341
  • Document Type:Article
  • Subject Area:Science
  • Publication Date:2025
  • ISSN:0735-3936
  • DOI:10.1002/bsl.2718
  • Accession Number:185988959
  • Copyright Statement:Copyright of Behavioral Sciences & the Law is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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