Second-Generation Digital Health Platforms: Placing the Patient at the Center and Focusing on Clinical Outcomes

Artificial intelligence (AI) digital health platforms have drawn much attention over the last decade. However, their implementation into medical practice occurs at a much slower pace than expected. This paper reviews some of the achievements of first-generation AI algorithms, and the barriers facing their implementation into medical practice. The development of second-generation AI platforms is discussed with a focus on overcoming some of these obstacles. Second-generation algorithms are aimed at focusing on a single subject and on improving patients’ clinically meaningful endpoints. A personalized closed-loop system designed to improve end-organ function and the patient's response to chronic therapies is presented. The system introduces a platform which implements a personalized therapeutic regimen and introduces quantifiable individualized-variability patterns into its algorithm. The platform is designed to achieve a clinically meaningful endpoint by ensuring that chronic therapies will have sustainable effect while overcoming compensatory mechanisms associated with disease progression and drug resistance. Second-generation platforms are expected to assist patients and providers in adopting and implementing of these systems into everyday care.