Artificial Intelligence and the Challenge for Rural Medicine

Abstract

The past three decades have seen major advances in the field of artificial intelligence (AI), specifically in the specializations machine learning and deep learning. In the current decade, those advances have found applications that have brought the field to public consciousness; these applications include image and face recognition and close to fully autonomous vehicles. Medical science, with its traditional emphasis on the doctor-patient relationship, has been understandably slower in the adoption of these technological advances. However, in his recent review article, 1 Eric Topol catalogs recent applications of AI and deep learning to medicine, and in his book "Deep Medicine" 2 puts forward the argument that appropriately applied automation has the potential to aid the doctor-patient relationship and "make healthcare human again". In short, by delegating a greater portion of the highly technical aspects of medicine to AI, we have the potential to allow physicians to focus on the human aspect of their work, and to devote a larger aspect of the training of new medical professionals to developing their empathetic, patient-centered skills. Recent applications of AI to medicine have included automatic reading of digital medical imaging, including radiology, pathology, and CT scans. Some studies have shown that AI-based analysis of high-resolution pathology images can perform more accurately, as well as more quickly, than that by experts. 3 Moreover, combining automated AI-based image analysis with expert reading increases the accuracy over either approach used on its own. For diagnostics, the average doctor will accumulate the experience of having seen a few thousand patients during his/her career. However, with over 700,000 physicians currently practicing in the US, computer-assisted diagnostics present the potential to leverage "experience" that is orders of magnitude higher than that of an individual practitioner. These computer-assisted diagnostics leverage both AI (in the form of deep learning from millions of case descriptions available, natural language processing to automatically interpret patient and doctor descriptions of symptoms) and crowdsourcing (allowing potentially thousands of doctors the ability to confirm or refute diagnostics within minutes or hours). Platforms such as the HumanDX app have been developed that combine both of these aspects of computer-assisted diagnostics. Wearable devices have been developed, such as Kardia Mobile (AliveCor, Inc.), which generate electrocardiograms on a smartphone. AI-developed algorithms interpret these EKGs on the cell phone and can alert the patient to contact the physician if a potential abnormality is detected. While these do not yet achieve the same