Current dialysis devices are not able to react when unexpected changes occur during dialysis treatment, or to learn about experience for therapy personalization. Furthermore, great efforts are dedicated to develop miniaturized artificial kidneys to achieve a continuous and personalized dialysis therapy, in order to improve patient’s quality of life. These
innovative dialysis devices will require a real-time monitoring of equipment alarms, dialysis parameters and patient-related data to ensure patient safety and to allow instantaneous changes of the dialysis prescription for assessment of their adequacy. The analysis and evaluation of the resulting large-scale data sets enters the realm of Big Data and will require real-time predictive models. These may come from the fields of Machine Learning and Computational Intelligence, both included in Artificial Intelligence, a branch of engineering involved with the creation of devices that simulate intelligent behavior. The incorporation of Artificial Intelligence should provide a fully new approach to data analysis, enabling future advances in personalized dialysis therapies. With the purpose to learn about the present and potential future impact on medicine from experts in Artificial Intelligence and Machine Learning, a scientific meeting was organized in the Hospital of Bellvitge (Barcelona, Spain). As an outcome of that meeting, the aim of this review is to investigate Artificial Intelligence experiences on dialysis, with a focus on potential barriers, challenges and prospects for future applications of these technologies.Postprint (author's final draft
