AI models have enormous potential to leverage data from previously conducted toxicology studies to improve the interpretation of current studies or possibly reduce the need to conduct certain studies in the safety assessment of new drug products. This presentation will provide an overview of currently available use cases for artificial intelligence (AI) in regulatory toxicology followed by an exposition of the challenges that will need to be overcome to realize the transformational opportunities that AI will enable in the future. The following learning objectives with be covered: (1) identification of currently mature AI use cases, (2) Cultivation of an informed and realistic perspective on developing AI use cases, (3) understanding of the challenges associated with the development of these use cases and potential paths forward to overcome these challenges, and (4) awareness of ongoing efforts to collaboratively address these challenges.
