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Applications of artificial intelligence in forensic sciences: Current potential benefits, limitations and perspectives

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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.

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Data Availability

The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

AI :

Artificial intelligence

ANN :

Artificial neural network

BCNN :

Bayesian convolutional neural network

BPNN :

Backpropagation neural network

CBCT :

Cone beam computed tomography

CNN :

Convolutional neural network

DCNN :

Deep convolutional neural network

DL :

Deep learning

DT :

Decision tree

EPG :

Electropherogram

GAN :

Generative adversarial network

GRNN :

General regression neural network

ISFC :

International Society for Forensic Genetics

k-NN :

K-nearest neighbor

ML :

Machine learning

MRI :

Magnetic resonance imaging

OPGs :

Orthopantomograms

PCR :

Polymerase chain reaction

PMCT :

Post-mortem computed tomography

PMI :

Ost-mortem interval

RF :

Random forest

STR :

Short tandem repeats

SVM :

Support vector machine

SWGDAM :

Scientific Working Group on DNA Analysis Methods

ROIs :

Region of interests

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Authors and Affiliations

  1. Healthcare Accountability Lab, Institute of Legal Medicine of Milan, University of Milan, Via Luigi Mangiagalli 37, 20133, Milan, Italy

    Nicola Galante, Rosy Cotroneo, Domenico Furci, Giorgia Lodetti & Michelangelo Bruno Casali

  2. Department of Biomedical Sciences for Health, Institute of Legal Medicine of Milan, University of Milan, Via Luigi Mangiagalli 37, 20133, Milan, Italy

    Nicola Galante, Rosy Cotroneo, Domenico Furci & Giorgia Lodetti

  3. Department of Oncology and Hemato-Oncology (DIPO), University of Milan, Via Luigi Mangiagalli 37, 20133, Milan, Italy

    Michelangelo Bruno Casali

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  1. Nicola Galante
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  2. Rosy Cotroneo
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  3. Domenico Furci
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Galante, N., Cotroneo, R., Furci, D. et al. Applications of artificial intelligence in forensic sciences: Current potential benefits, limitations and perspectives. Int J Legal Med 137, 445–458 (2023). https://doi.org/10.1007/s00414-022-02928-5

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