Artificial Intelligence and Machine Learning in Aneurysmal Subarachnoid Hemorrhage: Future Promises, Perils, and Practicalities

Aneurysmal subarachnoid hemorrhage (SAH) is a subtype of hemorrhagic stroke with thirty-day mortality as high as 40%. Patients develop a myriad of short and long-term complications, which peak around days four to fourteen. The use of Machine Learning (ML) and Artificial intelligence (AI) methods (such as computer vision, Convolutional Neural Networks (CNN), Natural Language Processing (NLP), and drug discovery approaches among others) are emerging in healthcare and can help a patient population desperately in need of an integrated AI system that detects, segments, and provides clinical decision support based on severity for emergency treatments using CT brain imaging and clinical informatics.

This review aims at 1) synthesizing the current state of the art of AI and ML tools available for the management of aneurysmal SAH patients, and 2) providing an up-to-date account of future horizons in patient care.