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

Abstract

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.

Introduction

Forensic toxicology, a critical field at the nexus of science and justice, faces increasing complexity due to the rise of novel psychoactive substances, environmental toxins, and diverse poisons. Traditional analytical methods like GC-MS and LC-MS/MS generate vast, complex data sets, challenging human interpretation. Artificial Intelligence (AI) and machine learning (ML) have emerged as transformative tools, enabling rapid analysis, pattern recognition, and prediction of toxic effects.

AI applications in forensic toxicology include:

• Data Analysis: ML models classify toxins and unknown compounds with improved accuracy.

• Plant Toxins: High-resolution mass spectrometry combined with AI helps detect rare plant poisons in complex biological samples.

• Postmortem Toxicology: AI models assist in interpreting toxin redistribution and degradation after death, improving case accuracy.

• Pesticide Toxicity: AI accelerates identification of mixed pesticide exposures in biological samples.

• Novel Psychoactive Substances (NPS): AI adapts to rapidly evolving drug landscapes, supporting proactive detection and monitoring.

Emerging trends involve integrating chemometrics with AI, automating spectral library curation, predictive toxicology modeling ("future poisoning"), digital twin biological systems, AI-assisted histopathology, and multi-omics data integration for comprehensive toxic risk assessment.

Practical forensic applications include spectrum deconvolution, rapid drug/poison classification, natural language processing for case data, and automation through AI-guided robotics.

Challenges remain, notably data quality and standardization across labs, the need for transparent and explainable AI ("black box" issue), costs, and legal acceptance. Efforts focus on collaborative data sharing and developing interpretable AI models to ensure forensic validity and courtroom trust.

Conclusion

AI is not intended to substitute forensic toxicologists; it is intended to empower forensic toxicologists. By augmenting the data surcharge burden, AI actually gives forensic toxicologists much more time to spend on what forensic toxicologists think is important, which is interpreting the results in the context of justice and human life. The future forensic toxic science will likely be hybrid, where human expertise and an AI in mind are working in tandem. They might work together to provide more rapid, more accurate and more accessible toxicology testing for both science and society.

References

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Copyright

Copyright © 2025 Ms. Nikita Sisodia, Mr. Kiran R Dodiya. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.