Application of AI capabilities in forensics and criminal proceedings
Innovative technologies in forensic examinations
DOI:
https://doi.org/10.35295/osls.iisl.2555Parole chiave:
artificial intelligence, deep learning, expert opinion, forensic examinationAbstract
This study aims to identify and systematically present the modern domains of forensic examination where AI capabilities can be effectively utilized. Additionally, the study intends to provide a general assessment of the applicability of AI tools to forensic science in the future, particularly regarding the potential expansion of their role. The findings suggest that AI has the potential to substantially enhance the toolkit of forensic examinations within the techno-biological spectrum, especially in areas requiring the processing of large datasets, sample comparison, and the identification of complex patterns. However, AI is less applicable in fields that require an understanding of human psychology, behavior, or event context. This study interests professionals in forensic science and forensic examination from a practical standpoint. Furthermore, it highlights which branches of forensic science may require human involvement in the future, offering insights into the implications for the labor market and education in this field.
Downloads
Metrics
Statistiche globali ℹ️
|
98
Visualizzazioni
|
76
Download
|
|
174
Totale
|
|
Riferimenti bibliografici
Ahmed Alaa El-Din, E., 2022. Artificial intelligence in forensic science: Invasion or revolution? Egyptian Society of Clinical Toxicology Journal [online], 10(2), 20–32. Available at: https://doi.org/10.21608/esctj.2022.158178.1012 DOI: https://doi.org/10.21608/esctj.2022.158178.1012
Aljanaahi, A., et al., 2025. A review of analytical and chemometric strategies for forensic classification of homemade explosives. Analytical Science Advances [online], 6(1), e70010. Available at: https://doi.org/10.1002/ansa.70010 DOI: https://doi.org/10.1002/ansa.70010
Alketbi, S. K., 2024. Emerging technologies in forensic DNA analysis. Perspectives in Legal and Forensic Sciences [online], 1(1), 1–24. Available at: https://doi.org/10.70322/plfs.2024.10007 DOI: https://doi.org/10.70322/plfs.2024.10007
Allied Market Research, 2022. Automated Fingerprint Identification Systems (AFIS) Market Outlook – 2030 [online] Available at: https://www.alliedmarketresearch.com/automated-fingerprint-identification-system-market-A12196
Andrić, S. D., and Ivanović, A. B., 2023. Forensic sciences and ethics in the era of application of artificial intelligence. Kriminalističke Teme [online], 23(3-4), 95–102. Available at: https://doi.org/10.51235/kt.2023.23.3-4.95 DOI: https://doi.org/10.51235/kt.2023.23.3-4.95
Bennett, M. R., and Budka, M., 2024. AI trained to recognise footprints, but it won’t replace forensic experts yet [online]. Available at: https://www.europeandissemination.eu/ai-trained-to-recognise-footprints-but-it-wont-replace-forensic-experts-yet/14799
Bock, T., Conolly, P., and Barroso, F., 2024. Why high-quality data is crucial to fighting financial crime [online]. 26 June. Available at: https://www.kroll.com/en/insights/publications/ai-financial-crime-prevention
Brodsky, S., 2023. Deepfake audio powered by AI can fool you—Here’s why you should care [online]. 7 August. Available at: https://www.lifewire.com/ai-powered-deepfake-audio-7570357
Chen, Y., and Tseng, P., 2023. The boundary of artificial intelligence in forensic science. Dialogo [online], 10(1), 83–90. Available at: https://doi.org/10.51917/dialogo.2023.10.1.5 DOI: https://doi.org/10.51917/dialogo.2023.10.1.5
Denning, P. J., and Arquilla, J., 2022. The context problem in artificial intelligence [online]. 1 December. Available at: https://cacm.acm.org/opinion/the-context-problem-in-artificial-intelligence/ DOI: https://doi.org/10.1145/3567605
Dunsin, D., et al., 2024. A comprehensive analysis of the role of artificial intelligence and machine learning in modern digital forensics and incident response. Forensic Science International: Digital Investigation [online], 48, 301675. Available at: https://doi.org/10.1016/j.fsidi.2023.301675 DOI: https://doi.org/10.1016/j.fsidi.2023.301675
Dunsin, D., Ghanem, M. C., and Ouazzane, K., 2022. The use of artificial intelligence in digital forensics and incident response (DFIR) in a constrained environment [online]. International Conference on Digital Forensics and Security of Cloud Computing (ICDFSCC). London Met Repository, 1–5. Available at: https://repository.londonmet.ac.uk/7708/
Dutta, S. K., et al., 2021. Study on enhanced deep learning approaches for value-added identification and segmentation of striation marks in bullets for precise firearm classification. Applied Soft Computing [online], 112, 107789. Available at: https://doi.org/10.1016/j.asoc.2021.107789 DOI: https://doi.org/10.1016/j.asoc.2021.107789
Europolygraph, 2024. The polygraph: A reliable tool for truth detection in the 21st century [online]. 27 September. Available at: https://europolygraph.org/en/the-polygraph-a-reliable-tool-for-truth-detection-in-the-21st-century/
Farber, S., 2025. AI as a decision support tool in forensic image analysis: A pilot study on integrating large language models into crime scene investigation workflows. Journal of Forensic Sciences [online], 70(3), 932–943. Available at: https://doi.org/10.1111/1556-4029.70035 DOI: https://doi.org/10.1111/1556-4029.70035
FBI, 2012. Privacy Impact Assessment Integrated Automated Fingerprint Identification System (IAFIS)/Next Generation Identification (NGI) Biometric Interoperability [online]. Available at: https://www.fbi.gov/how-we-can-help-you/more-fbi-services-and-information/freedom-of-information-privacy-act/department-of-justice-fbi-privacy-impact-assessments/iafis-ngi-biometric-interoperability
FBI, 2024. The integrated automated fingerprint identification system [online]. Available at: https://ucr.fbi.gov/fingerprints_biometrics/biometric-center-of-excellence/files/iafis_0808_one-pager825
Flinders University, 2024a. AI promises to ramp up PCR tests for faster DNA diagnostics and forensics [online]. 30 September. Available at: https://www.clinicallab.com/ai-promises-to-ramp-up-pcr-tests-for-faster-dna-diagnostics-and-forensics-28038
Flinders University, 2024b. AI used to upgrade DNA forensics [online]. 12 October. Available at: https://news.flinders.edu.au/blog/2024/10/12/ai-used-to-upgrade-dna-forensics/
Forensic Capability Network, 2024. AI can detect abusive messages 21 times faster than humans [online]. 16 July. Available at: https://www.fcn.police.uk/news/2024-07/ai-can-detect-abusive-messages-21-times-faster-humans
Galante, N., et al., 2023. Applications of artificial intelligence in forensic sciences: Current potential benefits, limitations and perspectives. International Journal of Legal Medicine [online], 137(2), 445–458. Available at: https://doi.org/10.1007/s00414-022-02928-5 DOI: https://doi.org/10.1007/s00414-022-02928-5
Gupta, R., et al., 2024. Voice disorder recognition using machine learning: A scoping review protocol. BMJ Open [online], 14(2), e076998. Available at: https://doi.org/10.1136/bmjopen-2023-076998 DOI: https://doi.org/10.1136/bmjopen-2023-076998
Gustafson, K., 2024. Modern forensic science technologies [online]. 7 January. Available at: https://www.forensicscolleges.com/blog/resources/10-modern-forensic-science-technologies
Hall, S. W., Sakzad, A., and Choo, K. R., 2022. Explainable artificial intelligence for digital forensics. WIREs Forensic Science [online], 4(2), e1434. Available at: https://doi.org/10.1002/wfs2.1434 DOI: https://doi.org/10.1002/wfs2.1434
Hamzah, N. H., et al., 2022. Artificial intelligence in forensic science: Current applications and future direction. Buletin Sains Kesihatan [online], 6(2), 39–46. Available at: https://myjms.mohe.gov.my/index.php/bsk/article/view/16561
Hamzelou, J., 2024. AI lie detectors are better than humans at spotting lies [online]. 5 July. Available at: https://www.technologyreview.com/2024/07/05/1094703/ai-lie-detectors-are-better-than-humans-at-spotting-lies/
Hartung, T., 2023. Artificial intelligence as the new frontier in chemical risk assessment. Frontiers in Artificial Intelligence [online], 6, 1269932. Available at: https://doi.org/10.3389/frai.2023.1269932 DOI: https://doi.org/10.3389/frai.2023.1269932
Hartung, T., and Kleinstreuer, N., 2025. Challenges and opportunities for validation of AI-based new approach methods. ALTEX - Alternatives to Animal Experimentation [online], 42(1), 3–21. Available at: https://doi.org/10.14573/altex.2412291 DOI: https://doi.org/10.14573/altex.2412291
HPCC Systems, 2024. Machine learning and the forensic application of audio classification [online]. Available at: https://hpccsystems.com/resources/machine-learning-and-the-forensic-application-of-audio-classification/
Hussain, D. A., and Jyotishana, J., 2024. The application of artificial intelligence in the forensic medicine and toxicology: Need of the hour. International Journal of Forensic Medicine [online], 6(2), 25–29. Available at: https://doi.org/10.33545/27074447.2024.v6.i2a.91 DOI: https://doi.org/10.33545/27074447.2024.v6.i2a.91
Icetana AI, 2024. Icetana AI are experts in AI CCTV Systems [online]. Available at: https://www.icetana.ai/ai-cctv-analytics
Industrial Cyber, 2025. Darktrace 2025 Report: AI threats surge, but cyber resilience grows amidst skills gap [online]. 5 March. Available at: https://industrialcyber.co/ai/darktrace-2025-report-ai-threats-surge-but-cyber-resilience-grows-amidst-skills-gap/
Jarrett, A., and Choo, K. K. R., 2021. The impact of automation and artificial intelligence on digital forensics. WIREs Forensic Science [online], 3(6), e1418. Available at: https://doi.org/10.1002/wfs2.1418 DOI: https://doi.org/10.1002/wfs2.1418
Karpuntsov, V., & Veresha, R. 2023. Legal Aspects of Virtual Assets Regulation in Ukraine. DANUBE, 14(3), 235–252. Available at: https://doi.org/10.2478/danb-2023-0014 DOI: https://doi.org/10.2478/danb-2023-0014
Kleinstreuer, N., and Hartung, T., 2024. Artificial intelligence (AI)—it’s the end of the tox as we know it (and I feel fine)*. Archives of Toxicology [online], 98, 735–754. Available at: https://doi.org/10.1007/s00204-023-03666-2 DOI: https://doi.org/10.1007/s00204-023-03666-2
Leno, S., and Hnot, T., 2024. Future of VQA: How multimodal AI is transforming video analysis [online]. 30 October. SoftServe. Available at: https://www.softserveinc.com/en-us/blog/multimodal-ai-for-video-analysis
Lim, A., 2024. TextOracle: Using AI to support forensic handwriting analysis [online]. HTX. 19 January. Available at: https://www.htx.gov.sg/whats-happening/all-news---events/all-news/2024/featured-news--textoracle--using-ai-to-support-forensic-handwriting-analysis
Maiti, D., and Das, D., 2022. Gender and hand identification based on dactyloscopy using deep convolutional neural network. In: A. K. Das et al., eds., Computational intelligence in pattern recognition: Proceedings of CIPR 2023 [online]. Cham: Springer, 145–155. Available at: https://doi.org/10.1007/978-981-99-3734-9_13 DOI: https://doi.org/10.1007/978-981-99-3734-9_13
Malwarebytes, 2024. II v kiberbezopasnosti — riski i vozmozhnosti [AI in cybersecurity: Understanding the risks] [online]. Available at: https://www.malwarebytes.com/ru/cybersecurity/basics/risks-of-ai-in-cyber-security
McDonald, C., et al., 2024. Developing a machine learning ‘smart’ polymerase chain reaction thermocycler part 2: Putting the theoretical framework into practice. Genes [online], 15(9), 1199. Available at: https://doi.org/10.3390/genes15091199 DOI: https://doi.org/10.3390/genes15091199
McShane, E. J., et al., 2022. Quantifying graphite solid-electrolyte interphase chemistry and its impact on fast charging. ACS Energy Letters [online], 7(8), 2734–2744. Available at: https://doi.org/10.1021/acsenergylett.2c01059 DOI: https://doi.org/10.1021/acsenergylett.2c01059
Mubarrat, A. A., 2024. Role of artificial intelligence and big data in modern forensic accountancy. Available at: https://doi.org/10.2139/ssrn.5078259 DOI: https://doi.org/10.2139/ssrn.5078259
Nagel, D., 2023. AI writing detection tool uses ‘forensic linguistic’ techniques to check authorship. THE Journal [online], 7 March. Available at: https://thejournal.com/articles/2023/03/07/ai-writing-detection-tool-uses-forensic-linguistic-techniques-to-check-authorship.aspx
Öztürk, S., Özyer, B., and Temiz, Ö., 2024. Detection of voices generated by artificial intelligence with deep learning methods. In: A. E. Çetin and E. Erdem, eds., 2024 32nd Signal Processing and Communications Applications Conference (SIU) [online]. Mersin: IEEE, 1–4. Available at: https://doi.org/10.1109/SIU61531.2024.10601078 DOI: https://doi.org/10.1109/SIU61531.2024.10601078
Parabon Nanolabs, 2024. The snapshot DNA phenotyping service [online]. Available at: https://snapshot.parabon-nanolabs.com/#phenotyping
Patsiouras, E., et al., 2024. Integrating AI and computer vision for ballistic and bloodstain analysis in 3D digital forensics. In: S. Livatino, P. Arpaia and L. T. De Paolis, eds., 2024 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE) [online]. St Albans: IEEE, 734–739. Available at: https://doi.org/10.1109/MetroXRAINE62247.2024.10796512 DOI: https://doi.org/10.1109/MetroXRAINE62247.2024.10796512
Prior, A., et al., 2025. Improving first responder forensic capabilities: On-site detection and quantification of explosive precursors using portable near-infrared spectroscopy and machine learning. Forensic Science International [online], 368, 112378. Available at: https://doi.org/10.1016/j.forsciint.2025.112378 DOI: https://doi.org/10.1016/j.forsciint.2025.112378
PwC, 2023. Impact of Artificial Intelligence on fraud and scams [online]. December. Available at: https://www.pwc.co.uk/forensic-services/assets/impact-of-ai-on-fraud-and-scams.pdf
Qureshi, S. M., et al., 2024. Deepfake forensics: A survey of digital forensic methods for multimodal deepfake identification on social media. PeerJ Computer Science [online], 10, e2037. Available at: https://doi.org/10.7717/peerj-cs.2037 DOI: https://doi.org/10.7717/peerj-cs.2037
Raghav, A., et al., 2025. Forensic ballistics and artificial intelligence: Legal challenges and enhancing international framework for scientific evidence. In: C. Kaunert, ed., Forensic intelligence and deep learning solutions in crime investigation [online]. Hershey: IGI Global, 163–182. Available at: https://doi.org/10.4018/979-8-3693-9405-2.ch009 DOI: https://doi.org/10.4018/979-8-3693-9405-2.ch009
Rawat, R., et al., 2023. Autonomous artificial intelligence systems for fraud detection and forensics in dark web environments. Informatica [online], 47(9), 51–62. Available at: https://doi.org/10.31449/inf.v46i9.4538 DOI: https://doi.org/10.31449/inf.v46i9.4538
Renukadevi, P., John, S., and Shivani, N., 2024. Forensic Science: AI-Powered Image and Audio Analysis. 2024 5th International Conference on Smart Electronics and Communication (ICOSEC) [online]. Trichy: IEEE, 1519–1525. Available at: https://doi.org/10.1109/icosec61587.2024.10722068 DOI: https://doi.org/10.1109/ICOSEC61587.2024.10722068
Rizvi, S., et al., 2022. Application of artificial intelligence to network forensics: Survey, challenges and future directions. IEEE Access [online], 10, 110362–110384. Available at: https://doi.org/10.1109/ACCESS.2022.3214506 DOI: https://doi.org/10.1109/ACCESS.2022.3214506
Sessa, F., et al., 2024. Artificial intelligence and forensic genetics: Current applications and future perspectives. Applied Sciences [online], 14(5), 2113. Available at: https://doi.org/10.3390/app14052113 DOI: https://doi.org/10.3390/app14052113
Siegel, D., et al., 2022. Forensic data model for artificial intelligence based media forensics-Illustrated on the example of DeepFake detection. Electronic Imaging [online], 34, 1–6. Available at: https://doi.org/10.2352/EI.2022.34.4.MWSF-324 DOI: https://doi.org/10.2352/EI.2022.34.4.MWSF-324
Sisodia, N., 2025. Artificial Intelligence in Forensic Toxic Science: Emerging Trends and Analytical Techniques. International Journal for Research in Applied Science and Engineering Technology [online], 13(9), 1571–1579. Available at: https://doi.org/10.22214/ijraset.2025.74270 DOI: https://doi.org/10.22214/ijraset.2025.74270
Snohomish County, 2022. Suspect identified in 1990 homicide cold case [online]. 30 June. Available at: https://www.snohomishcountywa.gov/Archive.aspx?ADID=6870
Sparkes, M., 2021. AI clears up images of fingerprints to help with identification. New Scientist [online], 28 June. Available at: https://www.newscientist.com/article/2282218-ai-clears-up-images-of-fingerprints-to-help-with-identification/
Starke, G., D’Imperio, A., and Ienca, M. 2023. Out of their minds? Externalist challenges for using AI in forensic psychiatry. Frontiers in Psychiatry [online], 14, 1209862. Available at: https://doi.org/10.3389/fpsyt.2023.1209862 DOI: https://doi.org/10.3389/fpsyt.2023.1209862
Taylor, M., Osborne, N., and Waltke, H., 2024. Artificial Intelligence (AI) in forensic science use case explorer [online]. National Institute of Standards and Technology (NIST). Available at: https://forensicrti.org/wp-content/uploads/2024/10/Artificial-Intelligence-AI-in-Forensic-Science-Use-Case-Explorer.pdf
Thales, 2026. CABIS. The next generation of ABIS [online]. Available at: https://www.thalesgroup.com/en/solutions-catalogue/public-security/national-security/cabis
The IoT Academy, 2024. Deepfake detection technology – Working/uses/tools [online]. 20 September. Available at: https://www.theiotacademy.co/blog/deepfake-detection/
US Bureau of Labor Statistics, 2024. Forensic science technicians [online]. Available at: https://www.bls.gov/ooh/life-physical-and-social-science/forensic-science-technicians.htm
US Department of Homeland Security, 2023. Leveraging artificial intelligence is smart for explosive detection [online]. 14 December. Available at: https://www.dhs.gov/science-and-technology/news/2023/12/14/feature-article-leveraging-artificial-intelligence-smart-explosive-detection
Valid8, 2024. The impact of AI on fraud investigations: What forensic accountants need to know [online]. Available at: https://www.valid8financial.com/guides/m/ai-and-fraud-investigations
Veresha, R. 2017. Criminal and legal characteristics of criminal intent. Journal of financial crime, 24(1), 118–128. Available at: https://doi.org/10.1108/JFC-02-2016-0013 DOI: https://doi.org/10.1108/JFC-02-2016-0013
Wang, J., et al., 2023. Breaking boundaries between linguistics and artificial intelligence. Journal of Organizational and End User Computing [online], 35(1), 1–20. Available at: https://doi.org/10.4018/joeuc.334013 DOI: https://doi.org/10.4018/JOEUC.334013
Wankhade, T. D., et al., 2022. Artificial intelligence in forensic medicine and toxicology: the future of forensic medicine. Cureus [online], 14(8), 28376. Available at: https://doi.org/10.7759/cureus.28376 DOI: https://doi.org/10.7759/cureus.28376
Zhao, H., and Li, H., 2023. Handwriting identification and verification using artificial intelligence-assisted textural features. Scientific Reports [online], 13(1), 21739. Available at: https://doi.org/10.1038/s41598-023-48789-9 DOI: https://doi.org/10.1038/s41598-023-48789-9
##submission.downloads##
Pubblicato
Come citare
Fascicolo
Sezione
Licenza
Copyright (c) 2026 Sara Bakyt, Akynkozha Zhanibekov, Aigul Paridinova, Yerbol Karzhaubayev, Nursultan Poshanov

Questo lavoro è fornito con la licenza Creative Commons Attribuzione - Non commerciale - Non opere derivate 4.0 Internazionale.
OSLS strictly respects intellectual property rights and it is our policy that the author retains copyright, and articles are made available under a Creative Commons licence. The Creative Commons Non-Commercial Attribution No-Derivatives licence is our default licence and it regulates how others can use your work. Further details available at https://creativecommons.org/licenses/by-nc-nd/4.0 If this is not acceptable to you, please contact us.
The non-exclusive permission you grant to us includes the rights to disseminate the bibliographic details of the article, including the abstract supplied by you, and to authorise others, including bibliographic databases, indexing and contents alerting services, to copy and communicate these details.
For information on how to share and store your own article at each stage of production from submission to final publication, please read our Self-Archiving and Sharing policy.
The Copyright Notice showing the author and co-authors, and the Creative Commons license will be displayed on the article, and you must agree to this as part of the submission process. Please ensure that all co-authors are properly attributed and that they understand and accept these terms.















