Academic Journal
Artificial Intelligence Applications in Forensic Sciences: A Systematic Review of Methods, Performance, Validation, and Medico-Legal Implications.
| Title: | Artificial Intelligence Applications in Forensic Sciences: A Systematic Review of Methods, Performance, Validation, and Medico-Legal Implications. |
|---|---|
| Alternate Title: | Adli Bilimlerde Yapay Zekâ Uygulamaları: Yöntemler, Performans, Doğrulama ve Medikolegal Etkiler Üzerine Sistematik Derleme. (Turkish) |
| Authors: | Şenışık, Melek |
| Source: | Bulletin of Legal Medicine / Adli Tıp Bülteni; 2026, Vol. 31 Issue 2, p233-242, 10p |
| Subject Terms: | Forensic sciences, Artificial intelligence, Forensic genetics, Biometric identification, Machine learning, Digital forensics |
| Abstract (English): | Objective: Artificial intelligence (AI) and machine learning (ML) are increasingly used in forensic sciences to analyze large-scale and heterogeneous evidence, providing improved speed, consistency, and decision support. This review aims to categorize current AI/ML application domains in forensic practice, summarize methodological trends, and discuss key validation requirements for operational and court-defensible use. The scope covers forensic image analysis and computer vision (traces, injuries, and crime-scene imagery), biometric identification, AI-assisted interpretation of complex forensic genetic data (including mixtures and low-template DNA), AI-enabled triage and authenticity assessment in digital/cyber forensics, and segmentation/classification tasks in forensic pathology and postmortem imaging. Despite promising performance in research settings, critical barriers remain, including explainability, data representativeness and bias, cross-laboratory reproducibility, robustness against manipulation, privacy-preserving governance, and legal admissibility. The review highlights the need for independent validation, standardized performance reporting, and clearly defined human oversight to ensure responsible and defensible integration of AI into forensic workflows. [ABSTRACT FROM AUTHOR] |
| Abstract (Turkish): | Yapay zekâ (YZ) ve makine öğrenmesi (MÖ), adli bilimlerde büyük hacimli ve heterojen delil verilerinin analizinde hız, tutarlılık ve karar desteği sağlayarak giderek daha fazla kullanılmaktadır. Bu derlemenin amacı, adli bilimlerde YZ/MÖ'nün güncel uygulama alanlarını sınıflandırmak, yöntemsel eğilimleri özetlemek ve sahaya/muhakemeye entegrasyon için kritik doğrulama gereksinimlerini tartışmaktır. Kapsam; adli görüntü analizi ve bilgisayarlı görme (iz, yara, olay yeri görüntüsü), biyometrik tanıma, adli genetikte karışım ve düşük miktarlı DNA yorumlama, dijital/siber adli tıpta delil triyajı ve içerik özgünlüğü analizi, adli patoloji ve postmortem görüntülemede segmentasyon ve sınıflandırma uygulamalarını içermektedir. Potansiyel kazanımlara rağmen açıklanabilirlik, veri temsiliyeti ve yanlılık, laboratuvarlar arası tekrarlanabilirlik, manipülasyona dayanıklılık, mahremiyet ve mahkemede kabul edilebilirlik gibi sınırlılıklar sürmektedir. Derleme, YZ çıktılarının insan uzman denetimi altında, standart performans raporlaması ve bağımsız validasyonla desteklenmesi gerektiğini vurgulayarak sorumlu ve savunulabilir kullanım için bir çerçeve sunar. [ABSTRACT FROM AUTHOR] |
| Copyright of Bulletin of Legal Medicine / Adli Tıp Bülteni is the property of Galenos Yayinevi Tic. LTD. STI 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.) | |
| Database: | Complementary Index |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:edb&genre=article&issn=1300865X&ISBN=&volume=31&issue=2&date=20260501&spage=233&pages=233-242&title=Bulletin of Legal Medicine / Adli Tıp Bülteni&atitle=Artificial%20Intelligence%20Applications%20in%20Forensic%20Sciences%3A%20A%20Systematic%20Review%20of%20Methods%2C%20Performance%2C%20Validation%2C%20and%20Medico-Legal%20Implications.&aulast=%C5%9Een%C4%B1%C5%9F%C4%B1k%2C%20Melek&id=DOI:10.17986/blm.1807 Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
|---|---|
| Header | DbId: edb DbLabel: Complementary Index An: 196165985 RelevancyScore: 1061 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1060.76245117188 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Artificial Intelligence Applications in Forensic Sciences: A Systematic Review of Methods, Performance, Validation, and Medico-Legal Implications. – Name: TitleAlt Label: Alternate Title Group: TiAlt Data: Adli Bilimlerde Yapay Zekâ Uygulamaları: Yöntemler, Performans, Doğrulama ve Medikolegal Etkiler Üzerine Sistematik Derleme. (Turkish) – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Şenışık%2C+Melek%22">Şenışık, Melek</searchLink> – Name: TitleSource Label: Source Group: Src Data: Bulletin of Legal Medicine / Adli Tıp Bülteni; 2026, Vol. 31 Issue 2, p233-242, 10p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Forensic+sciences%22">Forensic sciences</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Forensic+genetics%22">Forensic genetics</searchLink><br /><searchLink fieldCode="DE" term="%22Biometric+identification%22">Biometric identification</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+forensics%22">Digital forensics</searchLink> – Name: AbstractNonEng Label: Abstract (English) Group: Ab Data: Objective: Artificial intelligence (AI) and machine learning (ML) are increasingly used in forensic sciences to analyze large-scale and heterogeneous evidence, providing improved speed, consistency, and decision support. This review aims to categorize current AI/ML application domains in forensic practice, summarize methodological trends, and discuss key validation requirements for operational and court-defensible use. The scope covers forensic image analysis and computer vision (traces, injuries, and crime-scene imagery), biometric identification, AI-assisted interpretation of complex forensic genetic data (including mixtures and low-template DNA), AI-enabled triage and authenticity assessment in digital/cyber forensics, and segmentation/classification tasks in forensic pathology and postmortem imaging. Despite promising performance in research settings, critical barriers remain, including explainability, data representativeness and bias, cross-laboratory reproducibility, robustness against manipulation, privacy-preserving governance, and legal admissibility. The review highlights the need for independent validation, standardized performance reporting, and clearly defined human oversight to ensure responsible and defensible integration of AI into forensic workflows. [ABSTRACT FROM AUTHOR] – Name: AbstractNonEng Label: Abstract (Turkish) Group: Ab Data: Yapay zekâ (YZ) ve makine öğrenmesi (MÖ), adli bilimlerde büyük hacimli ve heterojen delil verilerinin analizinde hız, tutarlılık ve karar desteği sağlayarak giderek daha fazla kullanılmaktadır. Bu derlemenin amacı, adli bilimlerde YZ/MÖ'nün güncel uygulama alanlarını sınıflandırmak, yöntemsel eğilimleri özetlemek ve sahaya/muhakemeye entegrasyon için kritik doğrulama gereksinimlerini tartışmaktır. Kapsam; adli görüntü analizi ve bilgisayarlı görme (iz, yara, olay yeri görüntüsü), biyometrik tanıma, adli genetikte karışım ve düşük miktarlı DNA yorumlama, dijital/siber adli tıpta delil triyajı ve içerik özgünlüğü analizi, adli patoloji ve postmortem görüntülemede segmentasyon ve sınıflandırma uygulamalarını içermektedir. Potansiyel kazanımlara rağmen açıklanabilirlik, veri temsiliyeti ve yanlılık, laboratuvarlar arası tekrarlanabilirlik, manipülasyona dayanıklılık, mahremiyet ve mahkemede kabul edilebilirlik gibi sınırlılıklar sürmektedir. Derleme, YZ çıktılarının insan uzman denetimi altında, standart performans raporlaması ve bağımsız validasyonla desteklenmesi gerektiğini vurgulayarak sorumlu ve savunulabilir kullanım için bir çerçeve sunar. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Bulletin of Legal Medicine / Adli Tıp Bülteni is the property of Galenos Yayinevi Tic. LTD. STI 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.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edb&AN=196165985 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.17986/blm.1807 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 233 Subjects: – SubjectFull: Forensic sciences Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Forensic genetics Type: general – SubjectFull: Biometric identification Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Digital forensics Type: general Titles: – TitleFull: Artificial Intelligence Applications in Forensic Sciences: A Systematic Review of Methods, Performance, Validation, and Medico-Legal Implications. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Şenışık, Melek IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: 2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 1300865X Numbering: – Type: volume Value: 31 – Type: issue Value: 2 Titles: – TitleFull: Bulletin of Legal Medicine / Adli Tıp Bülteni Type: main |
| ResultId | 1 |