In this study, Shen et al. reviewed various AI algorithms that work to detect ischemic lesions in the brains of patients who are at risk of having an ischemic stroke. By reviewing various algorithms developed for this purpose, Shen et al. wanted to show the potential AI has in predicting ischemic strokes in patients before they occur. Shen et al. completed this review by conducting a systematic search across various research journals, looking for studies conducted with the purpose of developing AI models that were programmed to detect ischemic lesions in brain scans. The researchers selected studies that adhered to Cochrane diagnostic standards, screening them to ensure they were sensitive and specific to the subject at hand. After choosing the studies that would participate in the review, Shen et al. extracted study details - author, publication date, title, etc. - , general information about the algorithm, and performance metrics of the AI models. Statistical analyses and comparisons of the extracted data allowed Shen et al. to create hierarchical rankings for various aspects of the AI diagnoses like specificity and sensitivity, and accuracy. The researchers found that the pooled sensitivity and specificity of the AI algorithms came out to be 91.2% and 96% respectively, meaning that the algorithms’ performance in detecting AI is acceptable and usable in a clinical setting. While this review of different experiments has its limitations in that the homogeneity among the studies may have compromised the results, the work of this study found that AI can efficiently be used to detect ischemic lesions and prevent ischemic strokes in patients, and implementing these algorithms into the diagnostic process could aid in the effective clinical management of IS patients.