Artificial Intelligence in Forensic Toxicology: A Systematic Review of Emerging Trends, Analytical Techniques, and Future Directions

Abstract

An essential component of medico-legal investigations, forensic toxicology is being revolutionised by AI. Complex data generated by sophisticated analytical tools like LC-MS/MS and the ongoing appearance of new psychoactive substance (NPS) are two of the many obstacles that the sector must overcome. Machine learning (ML) and artificial intelligence (AI) are changing the face of toxicology by solving problems in areas like predictive toxicology, deconvolution of complicated datasets, AI-assisted spectral library curation, and the integration of multi-omics methods for thorough toxicological profiling. Artificial intelligence (AI)-powered plant toxin detection, postmortem drug redistribution modelling, pesticide categorisation, and NPS monitoring are some concrete examples. There are still a number of problems that need fixing with AI, such as the "black box" problem with algorithmic decision-making, limits on data quality and standardisation, and ethical and legal worries about the admissibility of evidence obtained from AI in court. Personalised toxicology, cloud-based platforms to increase accessibility, and federated learning for collaborative model creation are some of the promising new advancements in the near future. While artificial intelligence (AI) cannot fully replace forensic toxicologists, it is a valuable tool that can greatly improve their analytical accuracy, efficiency, and prediction powers. This, in turn, strengthens the credibility and value of forensic toxicology in the judicial system.