The Multiple Sclerosis Society of Canada has awarded CA$1 million to a project helping doctors who treat multiple sclerosis (MS) patients make more personalized treatment decisions through the use of artificial intelligence (AI). The society awarded the five-year grant (worth about $814,800) to Douglas Arnold, MD, a neurologist with Neuro (the Montreal Neurological Institute-Hospital) at McGill University, with expertise in using magnetic resonance imaging (MRI) to assess MS and Alzheimer's disease. "We are entering a new era in which'Big Data' and increasing computer power are making it possible to develop artificial intelligence methods capable of predicting how individual MS patients will do in the future and how they will respond to specific treatments," Arnold said in a press release. "Clinicians cannot make such predictions at present," he added. "Integrating AI into the clinic will allow clinicians to adapt treatments to each individual patient's unique circumstances, to help ensure a better outcome."

In health care, two exciting uses of artificial intelligence -- in the clinic for patient care and in the laboratory for drug discovery -- are remarkably different applications. That perhaps explains why, though it's still early days for both, they are developing at different rates. In the clinical setting, AI works with known parameters, typically running through a classification process based on experiences of what works and what doesn't for different types of patients. The potential of AI here is significant, and the early successes are truly exciting. The opportunity is equally compelling in drug discovery, particularly in areas of high unmet need such as rare and hard-to-treat cancers and neurodegenerative conditions.

Arthritis is not just an ailment of old age--it can affect children too, causing lifelong pain and disability in its most severe forms. Fortunately, some kids grow out of it. Knowing which patients will develop milder forms of disease could spare them unnecessary treatment and potential medication side effects but currently doctors have no way of predicting disease course or severity. That could now change thanks to a machine learning tool developed by Quaid Morris, a professor of computer science at the Donnelly Centre for Cellular and Biomolecular Research at the University of Toronto, Dr. Rae Yeung, Professor of Paediatrics, Immunology and Medical Science at the University of Toronto, and their recently-graduated, co-supervised student Simon Eng. Morris is also faculty in the Vector Institute for Artificial Intelligence and is an inaugural AI Chair by the Canadian Institute for Advancement of Research.

Multiple Sclerosis is a degenerative condition of the central nervous system that affects nearly 2.5 million of individuals in terms of their physical, cognitive, psychological and social capabilities. Researchers are currently investigating on the use of patient reported outcome measures for the assessment of impact and evolution of the disease on the life of the patients. To date, a clear understanding on the use of such measures to predict the evolution of the disease is still lacking. In this work we resort to regularized machine learning methods for binary classification and multiple output regression. We propose a pipeline that can be used to predict the disease progression from patient reported measures. The obtained model is tested on a data set collected from an ongoing clinical research project.