Yoo et al. developed and tested an AI-based algorithm with the ability to quantify tau PET uptake, as the propagation of tau pathologies is associated with a loss of neurons and is a biomarker for Alzheimer’s disease. Yoo et al. built a neural network model that is able to perform spatial normalization with only tau PET images, not needing them to be paired with MR images. To evaluate the efficiency of this AI model, they processed information from a database that contained 1,134 tau PET and MR images from various patients through tau PET and MR imaging spatial normalization models, as well as their new model that only required tau PET data. The researcher, then, used statistical analysis to determine how robust and efficient each model was, and determined that the model produced clinically accurate results by confirming that tau deposition is linked to low cognitive function. Yoo et al. found that the quantitative data generated by their new model from the PET images retained more morphological details than the spatial normalization done by AI models that use MR images. Additionally, statistical analysis confirmed that a higher baseline tau deposition does predict a decline of episodic memory performance in the future. Through this study, Yoo et al. determined that an AI-powered tool that relied on tau PET uptake alone would act as a tool that could expertly assist human experts’ interpretation, and help medical professionals make more informed decisions.