  Artificial intelligence (AI) has shown great promise in revolutionizing the
field of digital health by improving disease diagnosis, treatment, and
prevention. This paper describes the Health Guardian platform, a
non-commercial, scientific research-based platform developed by the IBM Digital
Health team to rapidly translate AI research into cloud-based microservices.
The platform can collect health-related data from various digital devices,
including wearables and mobile applications. Its flexible architecture supports
microservices that accept diverse data types such as text, audio, and video,
expanding the range of digital health assessments and enabling holistic health
evaluations by capturing voice, facial, and motion bio-signals. These
microservices can be deployed to a clinical cohort specified through the
Clinical Task Manager (CTM). The CTM then collects multi-modal, clinical data
that can iteratively improve the accuracy of AI predictive models, discover new
disease mechanisms, or identify novel biomarkers. This paper highlights three
microservices with different input data types, including a text-based
microservice for depression assessment, a video-based microservice for
sit-to-stand mobility assessment, and a wearable-based microservice for
functional mobility assessment. The CTM is also discussed as a tool to help
design and set up clinical studies to unlock the full potential of the
platform. Today, the Health Guardian platform is being leveraged in
collaboration with research partners to optimize the development of AI models
by utilizing a multitude of input sources. This approach streamlines research
efforts, enhances efficiency, and facilitates the development and validation of
digital health applications.
