Artificial intelligence is aiding healthcare providers in data acquisition, management, and analysis. Previous topics discussed in relation to AI have been personalized medicine, drug repurposing, and data visualization. Predictive analysis is another way to improve efficiency and diagnoses with machine learning.
Two companies, Prognos and Google, are delving into machine-learning with a focus on healthcare. Machine-learning is a branch of artificial intelligence that allows systems to learn data, identify patterns, and make decisions, for the most part, autonomously. Data can be learned through supervised or unsupervised learning, as well as other methods, where the algorithm is given a training set of labeled examples (supervised learning) or the algorithm explores the data to find an existing pattern to provide a data structure (unsupervised learning).
Prognos is a healthcare company that uses AI to predict diseases, resulting in earlier decisions in healthcare in collaboration with diagnostic, insurance, and life science companies.They have recently raised over $20 million toward their mission of predictive analytics. By combining healthcare and AI to predict disease earlier, they are able to identify patients in need of improved treatment decision-making, risk management, and quality improvement.
DeepMind Health is another AI support system, brainchild of Google, that deals with predictive analytics. With deep machine-learning, DeepMind Health is able to understand images and provide an analysis that offers feedback and data segmentation for clinicians.
Dr. Carlton Moore, CHiP core faculty member and Associate Professor at UNC School of Medicine, has done research pertaining to artificial intelligence, more specifically natural language processing.
Natural language processing is the field of computer science that combines machine learning with computational linguistics. Dr. Moore’s research focuses on extraction of certain keywords in patient electronic health records. He is also interested in researching medical errors during transitions in patient care and assessing the adequacy of abnormal tests results that are followed up after ambulatory care.
