Olivier Elemento, Ph.D., Sean Khozin, M.D., and Cora N. Sternberg, M.D.
Artificial intelligence (AI) has the potential to transform cancer therapeutic decision-making by improving diagnostics and personalizing treatments. This review explores the current and future impact of AI in oncology, focusing on its applications in radiology and pathology and the potential of large language models in treatment selection. Despite significant advancements, AI integration into clinical workflows is limited due to challenges such as data quality, model accuracy, and lack of validation through clinical trials. We propose key strategies to address these challenges, including developing robust multicenter datasets, promoting practical AI model development, researching workflow integration and human�AI collaboration, leveraging lessons from AI in medical imaging, establishing evaluation guidelines, and incentivizing prospective clinical trials. By implementing these strategies, AI can significantly enhance cancer care and patient outcomes, paving the way for its effective integration into oncology practice.