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

The integration of advanced computational models into legal investigations remains largely unexplored within existing academic literature. No prior work had resolved the full scope of how automated systems might influence traditional investigative workflows. That uncertainty drove researchers to examine whether machine learning could mitigate inherent human subjectivity during evidence analysis. Prior research has shown that manual methods for biological profiling often suffer from significant inter-observer variability. This gap motivated a comprehensive assessment of current technological capabilities versus established manual practices. It was already known that automated tools might offer speed advantages in complex diagnostic tasks. However, the reliability of these digital solutions in high-stakes legal contexts requires rigorous evaluation. This study addresses the pressing need to synthesize scattered evidence regarding the utility of these emerging technologies.

The aim of this systematic review is to explore the emerging application of computational intelligence within various forensic disciplines. This study seeks to address the significant knowledge gap regarding the practical utility of these advanced technologies in legal investigations. The authors intend to highlight the potential benefits and inherent limitations of using automated systems to solve long-standing forensic problems. By providing a critical overview, the research attempts to clarify how these tools might overcome traditional human subjective bias. The investigation focuses on multiple branches, including anthropology, odontology, pathology, and genetics, to provide a holistic perspective. This work also aims to identify the existing open questions that currently hinder the widespread adoption of these methods. The researchers provide procedural notes to assist readers in understanding the technical aspects of these digital solutions. Ultimately, the study serves as a useful instrument for practitioners to evaluate the current state and future directions of this field.

The review approach follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines to ensure methodological rigor. Investigators conducted an exhaustive search across multiple databases to identify relevant peer-reviewed literature. The team categorized findings into five distinct branches, including anthropology, odontology, pathology, and genetics. This systematic strategy allowed for a structured comparison of various computational methodologies applied to legal evidence. The authors evaluated the performance of these digital tools by synthesizing data from diverse experimental contexts. They scrutinized procedural notes and technical specifications provided in the original studies to assess feasibility. The design focuses on highlighting both the practical advantages and the existing limitations of current implementations. This comprehensive synthesis provides a clear overview of how these automated systems function within complex investigative scenarios.

Key findings from the literature demonstrate that automated systems effectively reduce human subjectivity in tasks such as age estimation and sex prediction. The review indicates that these technologies provide significant speed advantages during the identification of diatom taxonomy in pathology. Evidence shows that machine learning models successfully assist in evaluating third molar development stages within forensic odontology. The authors report that these digital tools are increasingly applied across five major branches, including anthropology and genetics. Data synthesis reveals that while accuracy is generally high, there are persistent issues regarding the transparency of algorithmic decision-making. The results highlight that current applications often struggle with the transition from controlled laboratory environments to real-life scenarios. The literature suggests that these models provide a useful instrument for standardizing diagnostic procedures that were previously reliant on individual expertise. The findings underscore that the integration of these systems is currently in an emerging phase, requiring further standardization.

The authors suggest that machine learning models offer significant potential to standardize complex diagnostic procedures across various forensic disciplines. Synthesis and implications indicate that while these tools reduce human subjectivity, they introduce new challenges regarding data transparency and algorithmic accountability. The review highlights that automated systems currently excel at pattern recognition tasks like diatom identification or dental development staging. Researchers propose that future implementation must prioritize the validation of these models against diverse, real-world datasets to ensure reliability. The findings emphasize that human oversight remains a necessary component of the investigative process despite technological advancements. The authors argue that clear procedural guidelines are required to integrate these digital assets into standard legal practice. This synthesis provides a framework for understanding the current limitations that hinder widespread adoption in judicial environments. The evidence suggests that a balanced approach combining human expertise with computational efficiency represents the most viable path forward.