An up-and-coming phenomenon, artificial intelligence (AI) technologies have the potential to transform many aspects of patient care, as well as administrative processes within provider, payer, and pharmaceutical organizations. There are several types of AI, including machine learning, which learned from experience without being explicitly programmed, and deep learning, which learns from raw/nearly raw data without the need for feature engineering.
Buffalo Neuroimaging Analysis Center (BNAC) researchers are pioneers in developing AI-like algorithms to enhance patient care for multiple sclerosis (MS). At the ACTRIMS Forum, Michael Dwyer, PhD, discussed the use of AI and MRI for MS care, covering areas like MR acquisition, image segmentation, diagnosis, and prognosis. Dwyer also explored the potential benefits of AI in tracking disease progression and identifying patterns. Regarding AI education, Dwyer emphasized the need for clinicians and the public to differentiate reliable AI tools from unreliable ones. He highlighted the importance of guidelines and expert review. AI’s ability to monitor disease progression lies in supervised and unsupervised learning, enabling the identification of subtypes and the integration of diverse data points for predictions. Deep learning stands out by learning from raw data, uncovering subtle insights that traditional methods might miss. While cautious validation is crucial, the field holds immense potential.