Artificial Intelligence in Forensic Toxic Science: Emerging Trends and Analytical Techniques

Forensic toxicology has long been tasked with addressing fundamental questions of causation in medico-legal investigations, such as whether death resulted from poisoning or drug use. Although advanced analytical platforms, including liquid chromatography–tandem mass spectrometry (LC–MS/MS) and gas chromatography-mass spectrometry (GC–MS), provide highly sensitive and specific data, the interpretation of these complex datasets remains a significant challenge. In recent years, artificial intelligence (AI) has emerged as a transformative tool in this domain, offering not only enhanced analytical speed but also the capacity to generate deeper, data-driven insights into toxicological findings. This review critically examines the application of AI and machine learning techniques within forensic toxicology. Key areas of focus include predictive toxicology, the development of AI-driven spectral libraries, automation of analytical workflows, and the integration of multi-omics data for comprehensive toxicological profiling. Furthermore, the review discusses the current challenges of ensuring robustness, transparency, and admissibility of AI-derived evidence in legal contexts, and outlines potential future directions for the incorporation of AI in forensic practice.