Academic Journal
Artificial intelligence in forensic science: a systematic review. Part I: personal identification.
| Τίτλος: | Artificial intelligence in forensic science: a systematic review. Part I: personal identification. |
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| Συγγραφείς: | 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. |
| Πηγή: | International journal of legal medicine [Int J Legal Med] 2026 Sep; Vol. 140 (5), pp. 3317-3329. Date of Electronic Publication: 2026 Jun 04. |
| Τύπος έκδοσης: | Journal Article; Systematic Review; Review |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | 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- |
| Ιατρικοί όροι (MeSH): | Forensic Sciences*/methods , Artificial Intelligence*, Humans ; Sex Determination Analysis ; Deep Learning ; Forensic Imaging |
| Περίληψη: | 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 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1437-1596 |
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| DOI: | 10.1007/s00414-026-03855-5 |