Review Article: The Efficacy and Implementation of Artificial Intelligence in Crime Scene Investigation: A Systematic Review of Meta-Analyses

Abstract

Background: Artificial Intelligence (AI) is rapidly transforming forensic science, offering new paradigms for evidence analysis and victim identification. A synthesis of high-level evidence is required to guide its integration into routine forensic practice.
Aims: This systematic review aims to consolidate findings from existing meta-analyses to evaluate the performance, implementation requirements, and limitations of AI applications specifically within crime scene investigation contexts.
Materials and Methods: We conducted a systematic search using the online engine, screening 498 papers against stringent criteria focusing on quantitative effectiveness, rigorous study design, and direct relevance to crime scene investigation. Data from four included meta-analyses (encompassing 32-39 primary studies each) were extracted and synthesized.
Results: AI applications demonstrated high efficacy across diverse forensic domains. In forensic odontology, AI algorithms for dental identification and age estimation surpassed traditional methods, while gender determination from orthopantomograms achieved a pooled accuracy of 88.66% (up to 99.20% in individual studies). Computed Tomography (CT) for death investigation provided accuracy comparable to autopsy, serving as a vital non-invasive alternative. Crime pattern analysis effectively utilized machine learning to identify spatial-temporal trends in government data. Critical success factors identified include the necessity for high-quality, diverse training data, substantial hardware investment (e.g., CT scanners >$80,000), and the need for standardized protocols and specialist training.
Conclusion: AI holds significant promise for enhancing the accuracy and efficiency of forensic investigations. However, its successful implementation is contingent upon overcoming challenges related to data bias, cost, and the need for robust regulatory frameworks and expert oversight. Future work must prioritize standardized validation studies and interdisciplinary collaboration between forensic experts and data scientists.