NIH Highlighted Topic: Advancing Data Science Approaches to Address Health Disparities Through Artificial Intelligence (AI), and Machine Learning (ML), and Community-Engaged Research
Introduction
This topic will support the development, implementation, and evaluation of community-engaged AI/ML interventions that convert routinely collected clinical and community-linked data into timely actions to improve screening completion, treatment adherence, disease control, and continuity of care in populations experiencing health disparities.
The main question is no longer whether AI/ML can generate accurate predictions in retrospective datasets. The critical question is whether engaging community in AI/ML systems can improve real-world outcomes when prospectively integrated into care delivery, and workflows that community organizations and health systems can sustain. Research should therefore move beyond model development alone and test complete intervention pathways on whether outcomes improve, including:
- What data are required?
- Which predictions are actionable?
- What service is triggered?
- Who delivers it?
- How patients respond?
See more information here, including participating NIH Institutes/Centers and their specific interests relating to the Highlighted Topic.
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