Academic Journal

Artificial intelligence in forensic science: a systematic review. Part I: personal identification.

Bibliographic Details
Title: Artificial intelligence in forensic science: a systematic review. Part I: personal identification.
Authors: Bugelli V; University of Parma, Parma, Italy., Calabrò F; University of Parma, Parma, Italy., Donato L; University of Rome Tor Vergata, Rome, Italy., Cecchi R; University of Modena and Reggio Emilia, Modena, Italy., Camatti J; University of Parma, Parma, Italy. jessika.camatti@unipr.it., Di Paolo M; University of Pisa, Pisa, Italy., Franceschetti L; University of Milano, Milan, Italy.
Source: International journal of legal medicine [Int J Legal Med] 2026 Sep; Vol. 140 (5), pp. 3317-3329. Date of Electronic Publication: 2026 Jun 04.
Publication Type: Journal Article; Systematic Review; Review
Language: English
Journal Info: Publisher: Springer International Country of Publication: Germany NLM ID: 9101456 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1437-1596 (Electronic) Linking ISSN: 09379827 NLM ISO Abbreviation: Int J Legal Med Subsets: MEDLINE
Imprint Name(s): Original Publication: Heidelberg, FRG : Springer International, c1990-
MeSH Terms: Forensic Sciences*/methods , Artificial Intelligence*, Humans ; Sex Determination Analysis ; Deep Learning ; Forensic Imaging
Abstract: Artificial intelligence (AI) has emerged as a promising tool in forensic sciences, offering new opportunities for personal identification through automated analysis of biological and imaging data. AI-based approaches have been increasingly applied to tasks such as sex estimation, human identification, ancestry estimation, and kinship analysis. This systematic review aims to synthesize the available evidence regarding the applications, methodological characteristics, and performance of AI models in forensic personal identification. A systematic literature search was conducted in PubMed/MEDLINE and Scopus following PRISMA guidelines. Studies investigating AI applications for forensic identification were included. Data extraction focused on study characteristics, dataset type, AI model architecture, forensic task, validation strategy, and reported performance metrics. A total of 89 studies published between 2012 and 2026 met the inclusion criteria. The majority of studies focused on sex estimation (63%), followed by human identification, ancestry estimation, multi-task prediction, and kinship verification. Most studies relied on imaging datasets, particularly computed tomography and radiographic images. Deep learning models represented the most frequently used analytical approaches. Reported accuracy values were generally high, with a median accuracy of 91.4% and an interquartile range of 88.9-95.0% in studies reporting single-value accuracy metrics. Deep learning approaches tended to achieve slightly higher performance than traditional machine learning models. AI shows considerable potential to support forensic personal identification, particularly in imaging-based applications. However, methodological heterogeneity, population-specific datasets, and limited external validation remain important challenges. Future research should prioritize standardized validation protocols, multi-population datasets, and transparent reporting to ensure the forensic applicability of AI-based identification systems.
(© 2026. The Author(s).)
Competing Interests: Declarations. Ethics approval and ethical standards: This study is a systematic review of published literature and did not involve new human participants or biological samples. Therefore, approval from an institutional review board or ethics committee was not required. Human ethics and consent to participate declaration: Not applicable. Clinical trial number: Not applicable. Competing interests: The authors declare no competing interests.
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Contributed Indexing: Keywords: Artificial intelligence; Deep learning; Forensic anthropology; Forensic identification; Machine learning; Sex estimation
Entry Date(s): Date Created: 20260603 Date Completed: 20260822 Latest Revision: 20260903
Update Code: 20260903
PubMed Central ID: PMC13499856
DOI: 10.1007/s00414-026-03855-5
PMID: 42236556
Database: MEDLINE
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: Artificial intelligence in forensic science: a systematic review. Part I: personal identification.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AU" term="%22Bugelli+V%22">Bugelli V</searchLink>; University of Parma, Parma, Italy.<br /><searchLink fieldCode="AU" term="%22Calabrò+F%22">Calabrò F</searchLink>; University of Parma, Parma, Italy.<br /><searchLink fieldCode="AU" term="%22Donato+L%22">Donato L</searchLink>; University of Rome Tor Vergata, Rome, Italy.<br /><searchLink fieldCode="AU" term="%22Cecchi+R%22">Cecchi R</searchLink>; University of Modena and Reggio Emilia, Modena, Italy.<br /><searchLink fieldCode="AU" term="%22Camatti+J%22">Camatti J</searchLink>; University of Parma, Parma, Italy. jessika.camatti@unipr.it.<br /><searchLink fieldCode="AU" term="%22Di+Paolo+M%22">Di Paolo M</searchLink>; University of Pisa, Pisa, Italy.<br /><searchLink fieldCode="AU" term="%22Franceschetti+L%22">Franceschetti L</searchLink>; University of Milano, Milan, Italy.
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  Data: <searchLink fieldCode="JN" term="%229101456%22">International journal of legal medicine</searchLink> [Int J Legal Med] 2026 Sep; Vol. 140 (5), pp. 3317-3329. <i>Date of Electronic Publication: </i>2026 Jun 04.
– Name: TypePub
  Label: Publication Type
  Group: TypPub
  Data: Journal Article; Systematic Review; Review
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: TitleSource
  Label: Journal Info
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Springer+International%22">Springer International </searchLink><i>Country of Publication: </i>Germany <i>NLM ID: </i>9101456 <i>Publication Model: </i>Print-Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>1437-1596 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2209379827%22">09379827 </searchLink><i>NLM ISO Abbreviation: </i>Int J Legal Med <i>Subsets: </i>MEDLINE
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  Data: <i>Original Publication</i>: Heidelberg, FRG : Springer International, c1990-
– Name: SubjectMESH
  Label: MeSH Terms
  Group: Su
  Data: <searchLink fieldCode="MM" term="%22Forensic+Sciences%22">Forensic Sciences*</searchLink>/<searchLink fieldCode="MM" term="%22Forensic+Sciences+methods%22">methods</searchLink> <br /><searchLink fieldCode="MM" term="%22Artificial+Intelligence%22">Artificial Intelligence*</searchLink><br /><searchLink fieldCode="MH" term="%22Humans%22">Humans</searchLink> ; <searchLink fieldCode="MH" term="%22Sex+Determination+Analysis%22">Sex Determination Analysis</searchLink> ; <searchLink fieldCode="MH" term="%22Deep+Learning%22">Deep Learning</searchLink> ; <searchLink fieldCode="MH" term="%22Forensic+Imaging%22">Forensic Imaging</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Artificial intelligence (AI) has emerged as a promising tool in forensic sciences, offering new opportunities for personal identification through automated analysis of biological and imaging data. AI-based approaches have been increasingly applied to tasks such as sex estimation, human identification, ancestry estimation, and kinship analysis. This systematic review aims to synthesize the available evidence regarding the applications, methodological characteristics, and performance of AI models in forensic personal identification. A systematic literature search was conducted in PubMed/MEDLINE and Scopus following PRISMA guidelines. Studies investigating AI applications for forensic identification were included. Data extraction focused on study characteristics, dataset type, AI model architecture, forensic task, validation strategy, and reported performance metrics. A total of 89 studies published between 2012 and 2026 met the inclusion criteria. The majority of studies focused on sex estimation (63%), followed by human identification, ancestry estimation, multi-task prediction, and kinship verification. Most studies relied on imaging datasets, particularly computed tomography and radiographic images. Deep learning models represented the most frequently used analytical approaches. Reported accuracy values were generally high, with a median accuracy of 91.4% and an interquartile range of 88.9-95.0% in studies reporting single-value accuracy metrics. Deep learning approaches tended to achieve slightly higher performance than traditional machine learning models. AI shows considerable potential to support forensic personal identification, particularly in imaging-based applications. However, methodological heterogeneity, population-specific datasets, and limited external validation remain important challenges. Future research should prioritize standardized validation protocols, multi-population datasets, and transparent reporting to ensure the forensic applicability of AI-based identification systems.<br /> (© 2026. The Author(s).)
– Name: Abstract
  Label: Competing Interests
  Group: Ab
  Data: Declarations. Ethics approval and ethical standards: This study is a systematic review of published literature and did not involve new human participants or biological samples. Therefore, approval from an institutional review board or ethics committee was not required. Human ethics and consent to participate declaration: Not applicable. Clinical trial number: Not applicable. Competing interests: The authors declare no competing interests.
– Name: Ref
  Label: References
  Group: RefInfo
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  Data: <i>Keywords: </i>Artificial intelligence; Deep learning; Forensic anthropology; Forensic identification; Machine learning; Sex estimation
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  Data: <i>Date Created: </i>20260603 <i>Date Completed: </i>20260822 <i>Latest Revision: </i>20260903
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  Data: 20260903
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  Data: 10.1007/s00414-026-03855-5
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        Value: 10.1007/s00414-026-03855-5
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      – Code: eng
        Text: English
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        StartPage: 3317
    Subjects:
      – SubjectFull: Humans
        Type: general
      – SubjectFull: Sex Determination Analysis
        Type: general
      – SubjectFull: Deep Learning
        Type: general
      – SubjectFull: Forensic Imaging
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      – SubjectFull: Forensic Sciences methods
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      – SubjectFull: Artificial Intelligence
        Type: general
    Titles:
      – TitleFull: Artificial intelligence in forensic science: a systematic review. Part I: personal identification.
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            – D: 01
              M: 09
              Text: 2026 Sep
              Type: published
              Y: 2026
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              Value: 140
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            – TitleFull: International journal of legal medicine
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