This study by Pedersen et al. analyzes the role Artificial Intelligence (AI) has on clinical decision support in Neurology, outlining the key benefits and focusing on the key areas of diagnostic accuracy, prognosis, and personalized treatments. The study first introduces AI, mentioning how AI emulates human intelligence and human neural networks through computational models, particularly deep learning, enabling efficient extraction of helpful information from large and complex data. The study also highlights methods to increase AI diagnostic efficacy, including the addition of multimodal data, such as imaging data, genetic data, and clinical records, and the use of transfer learning, where a previously trained AI model can be used as the starting point for new models. However, AI validation is crucial to maintain that AI models have strong performance on novel data. Additionally, the study discusses augmented intelligence, which combines artificial intelligence with human interaction, emphasizing its importance on maximizing clinical effectiveness under human oversight. This research illustrates AI’s importance and its ability to improve diagnostic precision, detect abnormalities in imaging scans, and create personalized treatment plans. However, there are still challenges with the usage of AI, including the need for quality datasets, ethical considerations, and the need for clinical validation before AI can be implemented in a clinical fashion.