Applications of artificial intelligence in forensic sciences: Current potential benefits, limitations and perspectives
- PMID: 36507961
- DOI: 10.1007/s00414-022-02928-5
Applications of artificial intelligence in forensic sciences: Current potential benefits, limitations and perspectives
Abstract
In recent years, new studies based on artificial intelligence (AI) have been conducted in the forensic field, posing new challenges and demonstrating the advantages and disadvantages of using AI methodologies to solve forensic well-known problems. Specifically, AI technology has tried to overcome the human subjective bias limitations of the traditional approach of the forensic sciences, which include sex prediction and age estimation from morphometric measurements in forensic anthropology or evaluating the third molar stage of development in forensic odontology. Likewise, AI has been studied as an assisting tool in forensic pathology for a quick and easy identification of the taxonomy of diatoms. The present systematic review follows the PRISMA 2020 statements and aims to explore an emerging topic that has been poorly analyzed in the forensic literature. Benefits, limitations, and forensic implications concerning AI are therefore highlighted, by providing an extensive critical review of its current applications on forensic sciences as well as its future directions. Results are divided into 5 subsections which included forensic anthropology, forensic odontology, forensic pathology, forensic genetics, and other forensic branches. The discussion offers a useful instrument to investigate the potential benefits of AI in the forensic fields as well as to point out the existing open questions and issues concerning its application on real-life scenarios. Procedural notes and technical aspects are also provided to the readers.
Keywords: Artificial intelligence; Deep learning; Forensic anthropology; Forensic genetics; Forensic pathology; Forensic sciences.
© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
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References
-
- Shapiro SC (1992) Artificial intelligence. In: Shapiro SC (ed) Encyclopedia of Artificial Intelligence, vol.1, 2nd edn. Wiley, New York, pp 89–93
-
- Mesejo P, Martos R, Ibáñez Ó, Novo J, Ortega M (2020) A survey on artificial intelligence techniques for biomedical image analysis in skeleton-based forensic human identification. Appl Sci 10:4703. https://doi.org/10.3390/app10144703 - DOI
-
- Peña-Solórzano CA, Albrecht DW, Bassed RB, Burke MD, Dimmock MR (2020) Findings from machine learning in clinical medical imaging applications - lessons for translation to the forensic setting. Forensic Sci Int 316:110538. https://doi.org/10.1016/j.forsciint.2020.110538 - DOI
-
- Khanagar SB, Vishwanathaiah S, Naik S, A Al-Kheraif A, DevangDivakar D, Sarode SC, Bhandi S, Patil S (2021) Application and performance of artificial intelligence technology in forensic odontology - a systematic review. Leg Med (Tokyo) 48:101826. https://doi.org/10.1016/j.legalmed.2020.101826 - DOI
-
- Taylor D, Powers D (2016) Teaching artificial intelligence to read electropherograms. Forensic Sci Int Genet 25:10–18. https://doi.org/10.1016/j.fsigen.2016.07.013 - DOI
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