In this study, Zakariaee et al investigate the most effective method of training that enables machine learning (ML) and artificial intelligence (AI) to identify the time since stroke (TSS) onset. TSS is a crucial factor that establishes the eligibility for a patient experiencing a stroke to receive intravenous thrombolysis, the main, time-sensitive treatment method for ischemic stroke. Originally collecting 347 articles, the researchers narrowed the list to 23 studies, which underwent meta-analysis to establish the foundation for evaluating TSS classification methods in 9030 stroke patients. DWI-FLAIR mismatch refers to the MRI method where stroke lesions appear on diffusion weighted imaging (DWI) but not on fluid-attenuated inversion recovery (FLAIR); this misalignment suggests that the TSS was <4.5 hours. To assess whether patients were within the critical <4.5-hour TSS window, Zakariaee et al began by determining the accuracy of human readings of DWI-FLAIR mismatch. Importantly, the results from the review confirmed that the human analysis of the DWI-FLAIR mismatch had relatively low performance: area under the curve (AUC) was 0.71, sensitivity was 0.62, and specificity was 0.78. However, it was proposed that ML has a greater potential for significant accuracy after examining how the ML models that were trained on CT radiomic features performed better (AUC was 0.89, sensitivity was 0.85, and specificity was 0.86). Zakariaee et al emphasize that more studies need to be conducted to delve into the extent of clinical implementation of the ML models, but the authors offer the idea that incorporating AI into recognizing scans can prove reliable and highly effective.