Machine Learning in Forensic Toxicology: Innovations in Drug Detection and Analysis
Keywords:
Machine learning, forensic toxicology, drug detection, drug analysis, mass spectrometry, predictive models, toxicology data, deep learning, novel psychoactive substances, machine learning algorithms.Abstract
Forensic toxicology, the study of bodily fluids and tissues to detect and identify substances that could impair human functioning, has been significantly transformed by recent advancements in machine learning (ML). Machine learning techniques offer considerable improvements in drug detection, analysis, and interpretation. These innovations not only streamline toxicological testing but also enhance the accuracy and reliability of drug-related forensic investigations. This paper explores how machine learning is currently applied in forensic toxicology, with a focus on its role in improving the detection of illicit drugs, the development of predictive models for drug interactions, and automating data analysis for faster forensic outcomes. By examining case studies, algorithmic applications, and future possibilities, this research demonstrates that machine learning is a critical tool for the evolution of forensic toxicology.
