What are the challenges in implementing ML and AI in toxicology?
Despite their potential, several challenges exist in the integration of ML and AI into toxicology. One major issue is data quality and availability; toxicological datasets can be incomplete, biased, or inconsistent, which can affect the accuracy of AI models. Moreover, the interpretability of these models remains a concern, as complex algorithms such as deep learning are often seen as "black boxes" that provide little insight into how predictions are made. Addressing these challenges requires robust data management practices and the development of transparent algorithms.
