AI-Assisted Species Identification in Forensic Entomology: Advancements and Applications

Forensic entomology fundamentally depends on the precise identification of insect species to estimate postmortem intervals in criminal investigations. However, traditional identification methods are often time-intensive and require specialized expertise. This chapter examines recent advancements in artificial intelligence (AI)-driven tools, including deep learning models such as convolutional neural networks (CNNs) and automated imaging systems, which significantly enhance species classification from photographs or specimens, achieving accuracy rates exceeding 95% or above. Notable innovations include mobile applications for field use and integration with DNA barcoding for hybrid verification, which reduces identification time from days to minutes. These applications extend to casework in homicide and wildlife crime scenes, thereby improving evidentiary reliability and supporting global databases of rare species. Future research directions should emphasize the ethical use of AI, bias mitigation, and the development of scalable training datasets to democratize forensic entomology on a global scale. The identification of forensic species in human remains is central to criminal investigations, providing critical evidence regarding the victim and the circumstances of their death. In forensic entomology, insects attracted to decomposing tissues, particularly dipterans, remain essential resources for the identification and reconstruction of the death environment. This chapter elucidates significant advances in AI-assisted species identification within forensic entomology, highlighting the application of advanced solutions to aid criminal investigations through streamlined identification of insects on remains.