Grants Advancing Community-Led AI Research to Improve Health

Grants Advancing Community-Led AI Research to Improve Health is sponsored by Various (focused on health equity). These funding opportunities aim to expand artificial intelligence and machine learning (AI/ML) research to enhance health outcomes for diverse populations.

Advancing Data Science Approaches to Address Health Disparities Through Artificial Intelligence (AI), and Machine Learning (ML), and Community-Engaged Research | Grants & Funding U.S. Department of Health and Human Services National Institutes of Health Advancing Data Science Approaches to Address Health Disparities Through Artificial Intelligence (AI), and Machine Learning (ML), and Community-Engaged Research When beginning your next investigator-initiated application, consider the following NIH highlighted topic.

The area of science described below is of interest to the listed NIH Institutes, Centers, and Offices (ICOs). This is not a notice of funding opportunity (NOFO). Apply through an appropriate NIH Parent Funding Announcement or another broad NIH opportunity available on Grants.

gov . Learn how to interpret and use Highlighted Topics . Expiration Date: July 20, 2028 Purpose: 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.

AI/ML is sufficiently advanced to support community-engaged interventions for populations experiencing health disparities. These models are most useful when used to activate specific, measurable actions rather than autonomous clinical decisions.

Recent advances in electronic health record (EHR)-based prediction, natural language processing, multimodal data integration, and mobile and remote monitoring makes it feasible to identify patients at high short-term risk of missed screening, uncontrolled chronic disease, medication interruption, avoidable acute care use, and loss to follow-up The strongest near-term opportunity is not diagnosis alone, but interventions targeting the right person to the right outreach, service, and follow-up at the right time.

For health disparities science, AI/ML should be embedded in clinical and/or community settings where implementation can improve uptake of evidence-based care.

High-value use cases include, but are not limited to: Using clinic, pharmacy, and remote monitoring data to prompt community health worker outreach for uncontrolled hypertension Identifying patients overdue for colorectal, cervical, or breast cancer screening and routing them to mailed or mobile screening options Predicting diabetes treatment interruption and prompting refill support, nutrition counseling, and home glucose monitoring Flagging pregnancy and postpartum patients at rising risk for hypertension, depression, or missed visits to activate nurse navigation and telehealth follow-up.

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: Which predictions are actionable? What service is triggered? Also of interest are use cases with short feedback loops, clear operational workflows, and measurable clinical endpoints.

National Institute on Minority Health and Health Disparities (NIMHD) NIMHD seeks co-designed interventions with patients, community organizations, federally qualified health centers (FQHCs), health systems, and public health partners – and use cases with evidence-based treatments and modifiable care gaps.

Research is needed to: Build and locally validate AI/ML models to predict near-term actionable events (e.g., uncontrolled blood pressure or HbA1c, postpartum loss, asthma exacerbation, and HIV care discontinuity) Link predictions to predefined action bundles such as community health worker outreach, scheduling assistance, mailed testing, telehealth follow-up, refill assistance, home monitoring, or specialist referral; Test interventions in prospective trials across clinics and partner organizations and produce implementation-ready tools for scale-up.

Measures may include clinical outcomes, uptake, disparity reduction, cost, and sustainability. IC may dedicate funds available to support applications in this Topic area depending upon the availability of funds, the number of meritorious applications, and competing ICO priorities. IC may give special consideration to support meritorious applications in this topic area.

Division of Clinical and Health Services Research National Institute of Mental Health (NIMH) NIMH seeks solutions-oriented AI/ML interventions in clinical/community settings to improve mental health (MH) management in populations experiencing health disparities, including people living with HIV and/or experiencing suicidal ideation and behavior.

Use of AI/ML tools to expand access to evidence-based interventions (EBI) and engage high-risk individuals, enhance provider training, analyze wearable/tracking data for early detection of prodrome signs, worsening symptoms, missed visits, medication nonadherence, crisis care use, or care disengagement, and support evidence-based follow-up Improve MH management in patients at risk of disengagement, treatment interruption, virologic non-suppression, or poor adherence through outreach and patient-caregiver connectivity Integrate AI/ML approaches to address co-occurring MH/HIV needs shaped by clinical, behavioral, social, and community factors Advance implementation and utilization of EBIs in community settings Lori A.

J. Scott-Sheldon, Ph. D.

Office of Behavioral and Social Sciences Research (OBSSR) This office does not award grants. Applications must be relevant to the objectives of at least one of the participating Institutes or Centers listed in this topic. Office of Disease Prevention (ODP) This office does not award grants.

Applications must be relevant to the objectives of at least one of the participating Institutes or Centers listed in this topic. For technical issues E-mail OER Webmaster