Abstract
Artificial intelligence could enhance chimeric antigen receptor T-cell therapy outcomes through optimization of all steps, from target identification, vector design, and manufacturing to personalized data-driven clinical decisions. In this report, we highlight steps toward unlocking this potential, including the need for standardized, comprehensive data repositories as a way for addressing barriers to artificial intelligence learning, such as data heterogeneity and patient privacy.