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Review
. 2024 Sep 28;16(9):e70363.
doi: 10.7759/cureus.70363. eCollection 2024 Sep.

Artificial Intelligence in Forensic Sciences: A Systematic Review of Past and Current Applications and Future Perspectives

Ioannis Ketsekioulafis  1 , Giorgos Filandrianos  2 , Konstantinos Katsos  1 , Konstantinos Thomas  2 , Chara Spiliopoulou  1 , Giorgos Stamou  2 , Emmanouil I Sakelliadis  1
Affiliations
Review

Artificial Intelligence in Forensic Sciences: A Systematic Review of Past and Current Applications and Future Perspectives

Ioannis Ketsekioulafis et al. Cureus. .

Abstract

The aim of this study is to review the available knowledge concerning the use of artificial Intelligence (AI) in general in different areas of Forensic Sciences from human identification to postmortem interval estimation and the estimation of different causes of death. This paper aims to emphasize the different uses of AI, especially in Forensic Medicine, and elucidate its technical part. This will be achieved through an explanation of different technologies that have been so far employed and through new ideas that may contribute as a first step to the adoption of new practices and to the development of new technologies. A systematic literature search was performed in accordance with the Preferred Reported Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines in the PubMed Database and Cochrane Central Library. Neither time nor regional constrictions were adopted, and all the included papers were written in English. Terms used were MACHINE AND LEARNING AND FORENSIC AND PATHOLOGY and ARTIFICIAL AND INTELIGENCE AND FORENSIC AND PATHOLOGY. Quality control was performed using the Joanna Briggs Institute critical appraisal tools. A search of 224 articles was performed. Seven more articles were extracted from the references of the initial selection. After excluding all non-relevant articles, the remaining 45 articles were thoroughly reviewed through the whole text. A final number of 33 papers were identified as relevant to the subject, in accordance with the criteria previously established. It must be clear that AI is not meant to replace forensic experts but to assist them in their everyday work life.

Keywords: artificial intelligence; deep learning; forensic medicine; forensic pathology; forensic sciences; machine learning.

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Conflict of interest statement

Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work.

Figures

Figure 1
Figure 1. PRISMA Flowchart.
PRISMA: Preferred Reported Items for Systematic Reviews and Meta-Analyses
Figure 2
Figure 2. Hierarchical Clustering of Machine Learning Algorithms Analyzed in This Study. The Sunburst Diagram Represents the Hierarchical Clustering of Machine Learning Algorithms.
ML: Machine Learning; SL: Supervised Learning, CL: Clustering; CNN: Convolutional Neural Network; ANN: Artificial Neural Network; BPNN: Backpropagation Neural Network; k-NN: k-Nearest Neighbor; RODF: Robust Object Detection Framework; DLIAS: Deep Learning Image Analysis Software; OT: Other Techniques; FTIR: Fourier Transform Infrared Spectroscopy. Each algorithm is categorized based on its specific role within the machine learning domain, showcasing their hierarchical relationships.
Figure 3
Figure 3. Research Aims Over Time.
The horizontal axis represents the years, the vertical axis represents the specific aims of the studies, and each data point on the graph corresponds to the number of papers focusing on a particular aim during a given year. The colors in the diagram indicate the general categories of the tasks addressed in the papers. PMI: Postmortem interval
See this image and copyright information in PMC

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