Student founders working on a start-up the past 2 years have created a 30-second home based AI brain health evaluation utilizing advanced imaging and neural analysis for non-invasive early disease detection. Personalized and tailored, this new gaze tracking system is non-invasive, has remote capabilities, and costs less - bringing remote detection to more providers and more patients via tele-ophthalmology. Identifying ocular biomarkers can point to several serious conditions, including Alzheimer’s and other neurodegenerative diseases.

Digital health interventions including telehealth support an increasingly broad range of improvement goals for prevention and treatment. Limitations obstructing the many digital benefits of telehealth from reaching their full potential include lack of robust usability and user centered design, regulatory policy paradigms, lack of adequate high-quality evidence and methodologies to evaluate the performance generalization and clinical robustness. Health innovation is explored in the context of different value systems and a solution is proposed to the fundamental limitations arising in the data value system, an approach to a new telehealth paradigm and incorporated intervention designs which combine clinical innovation with innovation in data resource development. Machine learning and artificial intelligence have the potential to enable circular economies for digital and health innovation, in which sustainable solutions can be offered within a data-enabled collaborative and shared digital ecosystem. Alignment of industry standards, adjustments to regulatory policies, and embracing new governance models for telehealth-based innovation are essential for this new approach to health innovation scaling, clinical adoption and social innovation. Given the trends in technological advances in the past decades, it is likely that healthcare reliance on telehealth will continue to grow.