The use of artificial intelligence in forensic toxicology

Abstract

Objectives

The integration of artificial intelligence (AI) into forensic toxicology has the potential to revolutionize this domain by enhancing efficiency, and interpretability of toxicological analyses. AI technologies, including machine learning (ML), deep learning, generative AI and expert systems offer advanced data analysis capabilities that can significantly improve the way forensic toxicologists will work in the future. These systems can enhance efficiency by automating time-intensive laboratory processes, offering significant advancements in the detection of compounds, and processing of data, whilst reducing human error. Techniques such as natural language processing (NLP) are employed to extract relevant information from scientific literature, enhancing the knowledge base available to forensic toxicologists. Other AI models are trained on extensive datasets comprising chemical structures, toxicological outcomes, and biological assay results, to finally predict the toxicity of new compounds, automate the identification of substances in biological samples, and assist in the interpretation of complex toxicological data.

Methods

The opportunities and challenges associated with implementing the different AI technologies in forensic toxicology, with a focus on generative AI, expert systems, and ML will be reviewed. In addition, a summary of the aims and tasks of ‘The International Association of Forensic Toxicologists’ (TIAFT) Task Force concerning AI will be discussed.

Results

This summary provides a review of the current integration of AI into forensic toxicology, highlighting its potential to revolutionize the field by increasing efficiency, enhancing interpretability, and supporting forensic investigations. Key findings include (a) the potential of NLP and generative AI to automate literature reviews and report generation, (b) use of expert systems for rapid drug identification in biological samples, and (c) ML models for interpreting complex toxicological data, such as drug interactions and biomarker detection.

Discussion

Despite the clear benefits of AI, the integration of AI in forensic toxicology faces several challenges. These include ensuring data quality and accessibility, addressing ethical concerns related to data privacy and algorithmic bias, and achieving regulatory acceptance of AI-driven methodologies. Transparent and explainable AI models are crucial for gaining trust within the forensic community and ensuring responsible use of technology.

Conclusion

The incorporation of AI into forensic toxicology holds promise for enhancing the accuracy, efficiency, and reliability of toxicological analyses. By leveraging AI technologies, forensic toxicologists can improve their ability to detect and interpret toxic substances, ultimately contributing to more effective forensic investigations and public safety. Machines will not replace toxicologists, but toxicologists using AI will likely replace those not using it.