Artificial Intelligence and Computer Vision in Forensic Sciences: Transforming Violence-Related Evidence Analysis
Organized by: International Forensic Scientist Awards
Website: forensicscientist.org
15th Edition of Forensic Scientist Awards 27-28 October 2025 | Paris, France
Introduction
The fusion of Artificial Intelligence (AI) and Computer Vision (CV) has sparked a technological revolution in forensic sciences. These tools are reshaping how experts interpret and analyze evidence — especially in cases involving violence-related injuries. By enabling faster, more objective, and highly precise evaluations, AI and CV are driving the next generation of digital forensic analysis.
Background
Traditional forensic methods, while effective, often depend on subjective human interpretation. This can introduce inconsistencies, especially in complex injury assessments. The integration of AI algorithms and computer vision models enhances precision and objectivity by automating the detection, classification, and reconstruction of forensic evidence such as wounds, fractures, and physical trauma.
Objective of the Study
This review aims to evaluate how AI and CV are being applied to violence-related forensic evidence analysis, focusing on recent advancements, implementation challenges, and potential future directions in forensic technology.
Materials and Methods
A comprehensive search was conducted across major scientific databases — PubMed, Scopus, and Web of Science — covering publications from 2020 to 2025. Using relevant keywords and MeSH terms linked to AI, computer vision, and forensic sciences, the study identified 206 initial records. After screening with ASReview software and expert consultation, 21 high-quality studies met the inclusion criteria.
Key Findings
The 21 selected studies highlighted the application of AI across six major forensic domains:
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Wound and Injury Classification
AI models can distinguish between different types of injuries — helping forensic experts identify the cause and nature of trauma. -
Head and Brain Injury Analysis
Computer vision aids in detecting and assessing internal head trauma using medical imaging data. -
Bone Fracture Identification
AI-driven radiographic analysis enhances accuracy in determining fracture type and severity. -
Process Enhancement and Scene Reconstruction
Machine learning algorithms enable automated reconstruction of crime scenes and injury mechanisms. -
Injury Degree Appraisal
Deep learning tools assist in quantifying the extent of damage or force involved in injuries. -
Physical Abuse Detection
AI helps identify patterns of repeated trauma, assisting in domestic violence and child abuse investigations.
Despite these advancements, challenges persist — such as limited real-world validation, class imbalance, and reliance on simulated datasets.
Conclusion
The integration of AI and computer vision technologies offers unprecedented opportunities for the objective evaluation of trauma-related evidence in forensic sciences. However, to ensure real-world reliability, there is a pressing need for standardized datasets, cross-disciplinary collaboration, and validated protocols. Future efforts should focus on developing generalizable, interpretable AI models that can be seamlessly integrated into forensic workflows — ensuring justice is delivered with both speed and scientific rigor.
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