Harnessing CURATE.AI as a Digital Therapeutics Platform by Identifying N‐of‐1 Learning Trajectory Profiles

Abstract

Conventional therapeutic interventions, which range from drug treatment to learning and training regimens, are often given at a fixed dose/intensity. This often leads to sub-optimal responses, or even none at all. Similarly, fixed intensity training can lead to plateaus in learning trajectories and training outcomes. This barrier will impact the field of digital therapeutics, where drug-based therapies may be complemented or replaced by learning and training platforms. A potential solution is to optimize training by identifying N-of-1 (single subject) training profiles that can then enhance learning trajectories through individualized training regimens. In this study, CURATE.AI, a mechanism-independent and indication-agnostic artificial intelligence (AI) platform, is used to identify N-of-1 learning trajectory profiles for healthy volunteers trained on the Multi-Attribute Task Battery (MATB), a flight deck simulator developed by the National Aeronautics and Space Administration and United States Air Force. By leveraging modulated MATB training intensity in a prospective study, CURATE.AI successfully develops N-of-1 learning trajectory profiles that may actionably mediate training optimization on the single-subject level, by dynamically identifying training inputs that drive the best possible scoring outcome. Therefore, CURATE.AI-guided training may serve as a powerful optimization platform for digital therapy, student learning, cognitive decline prevention, and other indications. and non-pharmacologic, often involve fixed or static magnitudes of treatment. [18][19][20][21][22][23][24][25][26] These treatments may be represented by dosage in the pharmacologic setting, or training intensities in learning-related domains. While fixed-dose drug treatment and fixed-intensity training are often used as a standard of care,

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