Abstract
Artificial intelligence (AI), mainly machine learning and deep learning algorithms, have advanced remarkably in the multiple domains of medical sciences. Clinical toxicology is a branch of toxicology that explores the adverse effects resulting from exposure to the various harmful chemicals on humans. In this chapter, we explored a broad understanding of AI methods with a special focus on their applications in clinical toxicology. The future of clinical development hugely relies on the convergence of the latest digital data resources as well as the advanced computing capabilities by efficiently utilizing AI and machine learning algorithms. The advancement of computational and AI-based methods for virtual screening and in silico drug design has made significant progress over the last decade. Also, the use of the deep neural network in conjunction with data-driven and mechanistic modeling for clinical toxicology evaluation has emerged as a promising field for research and development. We describe the fundamental concepts of clinical toxicology and AI in this chapter. Machine learning architectures are used to analyze and learn from publicly accessible biomedical and clinical trial datasets, real-world information from sensors, and health records are briefly covered. Recognition of complex patterns in toxicological data using advanced AI methods and its assistance in detection, characterization, and monitoring of clinical diseases are also elaborated. The future of AI and its impact on clinical toxicology is also discussed and summarized in detail.
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Acknowledgments
The authors thank the Council of Scientific and Industrial Research, New Delhi and CSIR-Indian Institute of Toxicology Research, Lucknow (Manuscript communication number 3749) for the research funding support and computational resources. MS thanks the Department of Science and Technology, Government of India, New Delhi for the INSPIRE fellowship.
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Sinha, M., G., P., Sachan, D.K., Parthasarathi, R. (2022). Artificial Intelligence in Clinical Toxicology. In: Lidströmer, N., Ashrafian, H. (eds) Artificial Intelligence in Medicine. Springer, Cham. https://doi.org/10.1007/978-3-030-64573-1_137
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DOI: https://doi.org/10.1007/978-3-030-64573-1_137
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