Machine Learning for Medical Diagnosis

Dr. Gary Fogel, CEO of Natural Selection Inc from United States participates in Risk Roundup to discuss Machine Learning for Medical Diagnosis. OVERVIEW Medicine, medical care, disease care and healthcare are rapidly emerging to be a human — machine collaboration that may ultimately become a cyborg relationship. This symbiotic relationship is still at an early stage. At this point, we see both humans and machines are performing tasks at which they are good at and are effective in their objectives. However, as machine learning and deep learning systems develop and evolve, it is expected that machines will increasingly assist humans with those tasks at which they are not very good. So, what are we humans good at? We, the humans, are good at processing information from our senses including vision and are very good at perceiving human emotions. But we are not so good at remembering things, searching for and organizing data, and not too good at correlating and reasoning about that data as well. This is where machine learning systems will add tremendous value. Machine learning systems will make physicians, providers and practitioners faster and smarter in their diagnoses and reduce uncertainty in their decisions, thereby reducing costs and risks and saving valuable time. This is welcoming as there is an increasing concern that manual medical diagnostic practices, that are driven largely by human intelligence, are no longer sufficient to effectively perform complex disease diagnostic tasks on its own, in a timely and cost-effective manner. From across nations, there are numerous reports emerging that machine learning has convincingly penetrated complex processes of medicine, especially medical diagnosis. As machine learning seems to be on its way to transforming the world of medicine and medical diagnosis, it is changing the fundamentals of not only disease diagnosis and care, but also healthcare. MACHINE LEARNING GOING MAINSTREAM When Artificial Intelligence (AI) powered, consumer-facing, disease diagnostic applications that individual humans can download onto their phones have started going mainstream, the fundamentals of medicine, medical care and healthcare seems to have changed forever. It is therefore important to evaluate- * Whether we are witnessing a surge in initiatives on not only AI initiatives but machine learning initiatives for medical diagnosis * What trends are driving the deep machine learning revolution for medicine in general * How would an intelligent machine or non-human intelligence bring fundamental transformation of medical disease diagnosis MANUAL MEDICAL DIAGNOSTIC PRACTICES Across medical diagnosis, large amounts of data are being generated and transferred. Thus, it is getting difficult for human intelligence to monitor broad or specific medicine threats with the currently available tools. This is probably one of the reasons why potential risks of disease diagnosis go unnoticed and puts patients’ lives at risk. Amidst that, machine learning promises to help physicians and practitioners make near-perfect disease diagnoses. Not only that, machine learning also helps to choose the best medications for patients and predict readmissions. It also helps identify patients that are at high-risk for poor outcomes. While on surface, machine learning help improve patients’ health while minimizing costs, it is important to evaluate- * Why is there a need for machine learning driven medical diagnosis? * Without the help of machine learning system, can the doctors or medical practitioners resolve medical diagnosis issues in a timely manner? * What does machine learning promise medical practitioners? * Why does clinical diagnosis still rely mostly on doctors’ expertise and intuition? * What needs to be done for the machine learning technology to be accepte...