JMIR Publications

ABSTRACT

Since the last decade most of our daily activities have turned out to be digital. Digital health takes into account the ever increasing synergy between advanced medical technologies, innovation and digital communication. Thanks to Machine Learning we are not limited anymore to a descriptive analysis of the data, as we can obtain greater value through the identification and prediction of patterns resulting from inductive reasoning. Machine Learning software that disclose the reasoning behind a prediction allow for “what-if” models by which it is possible to understand if and how, by changing certain factors, one may improve the outcomes, thereby, identifying the optimal behavior. Today diabetes care is facing several challenges: the decreasing number of diabetologists, the increasing number of patients, the reduced time allowed for medical visits, the growing complexity of the disease, both from the clinical and the patient-care standpoint, the difficulty of achieving the relevant clinical targets, the growing burden of disease management for both the healthcare provider and the patient, the healthcare accessibility and sustainability. In this context, the new digital technologies and the use of the artificial intelligence, certainly are a great opportunity. Herein we report the result of a careful analysis of the current literature and represents the vision of the Italian Association of Medical Diabetologists (AMD) on this controversial topic that , if well used, may be the key for a great scientific innovation. AMD believes that the use of artificial intelligence will allow to turn data (descriptive) into knowledge of the factors that “affect” the behavior and correlations (predictive), thereby identifying the key aspects that may establish an improvement of the expected results (prescriptive). Artificial Intelligence can therefore become a tool of great technical support to help diabetologists, to become fully responsible of the individual patient, assuring customized and precise medicine. This, in turn, will allow for comprehensive therapies built in accordance with the evidence criteria that should always ground any therapeutic choice.