Artificial Intelligence in Predictive Toxicology: Identifying influential topics based on science mapping
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https://doi.org/10.61705/yn6y2c83Keywords:
Predictive Toxicology, Artificial Intelligence (AI), Science MappingAbstract
This research paper delves into the transformative landscape of Predictive Toxicology, marked by the integration of Artificial Intelligence (AI) and cutting-edge technologies. The emergence of Predictive Toxicology, driven by computational models leveraging machine learning, signifies a departure from traditional methods, promising accelerated risk assessment and enhanced comprehension of chemical-biological interactions. Through science mapping, this study explores the interconnected web of literature in this dynamic field, aiming to trace its historical evolution, identify influential research hubs, and discern collaborative networks. The dataset analysis spanning 1993 to 2023 unveils trends, emphasizing the surge in AI's prominence and the sustained relevance of foundational topics. This study not only offers a comprehensive overview but also provides a roadmap for future research in the intersection of AI and Predictive Toxicology.
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