1,000+ Opportunities
Find the right grant
Search federal, foundation, and corporate grants with AI — or browse by agency, topic, and state.
Advancing Data Science Approaches to Address Health Disparities Through Artificial Intelligence (AI), and Machine Learning (ML), and Community-Engaged Research is sponsored by National Institutes of Health (NIH) - National Institute of Mental Health (NIMH), National Institute on Minority Health and Health Disparities (NIMHD). This topic supports 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 continu…
Get a weekly digest of new grants like this
A free weekly digest of new foundation and federal funding opportunities as they're added to Granted. Unsubscribe anytime.
Or search similar grants →Extracted from the official opportunity page/RFP to help you evaluate fit faster.
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
According to the current listing, eligibility includes: Investigator-initiated applicants through appropriate NIH Parent Funding Announcements or other broad NIH opportunities. Projects should be embedded in clinical and/or community settings. Confirm the full requirements in the official notice before applying.
Applications for Advancing Data Science Approaches to Address Health Disparities Through Artificial Intelligence (AI), and Machine Learning (ML), and Community-Engaged Research are due July 20, 2028. Build your timeline backwards from this date to cover registrations, approvals, and final submission checks.
Advancing Data Science Approaches to Address Health Disparities Through Artificial Intelligence (AI), and Machine Learning (ML), and Community-Engaged Research is funded by National Institutes of Health (NIH) - National Institute of Mental Health (NIMH), National Institute on Minority Health and Health Disparities (NIMHD). Verify program details on the funder's official page before applying.
Start from the official opportunity page linked in this listing — it carries the sponsor's submission instructions.
PCORI Cycle 2 2026 Methods Funding Announcement specifically prioritizes Methods to Improve the Use of Artificial Intelligence (AI) and Machine Learning (ML) in Patient-Centered Comparative Effectiveness Research (CER). The program funds studies addressing high-impact methodological gaps, with AI/ML topics including applications of AI/ML to augment or transform research methodologies or processes and approaches using AI/ML to enhance health communication. Additional priority areas include Methods to Support Use of Real-World Data in Multi-Site Patient-Centered CER and Methods to Improve Study Design. Awards provide up to $750,000 in direct costs for up to 3 years from a total program budget of $12 million. Applicants must address PCORI Foundational Expectations for Partnerships in Research, ensuring patients and stakeholders meaningfully contribute lived experience. Letter of Intent deadline is April 28, 2026, with full applications due September 1, 2026.
Summary: The fiscal year 2026 (FY26) Duchenne Muscular Dystrophy Research Program (DMDRP) Idea Development Award (IDA) promotes new ideas that are still in the early stages of development and have the potential to yield impactful data and new avenues of investigation. This award supports impactful, high-risk/high-reward research that could lead to critical discoveries or major advancements that will accelerate progress in improving outcomes for individuals with Duchenne muscular dystrophy (DMD) in the near term. Applications should include a well-formulated, testable hypothesis based on strong scientific rationale. The DMDRP strongly encourages research projects investigating therapies designed to demonstrate efficacy cross the life span, including infants, toddlers and nonambulatory individuals.Distinctive Features: The FY26 DMDRP IDA mechanism offers three eligibility career categories:• The Established Investigator category is for independent investigators at all academic levels, or equivalent• The New Investigator – Early-Stage category is for independent investigators early in their careers (i.e., within 10 years of their first faculty appointment or equivalent). Applicants in this category will be reviewed separately from Established Investigators.• The New Investigator – Transitioning category is for independent investigators at all academic levels, or equivalent, in an area other than muscular dystrophy who are seeking to transition to a career in DMD, thereby bringing their expertise to the field. Applicants in this category will be reviewed separately from Established Investigators.Preliminary data relevant to DMD that supports the feasibility of the research hypotheses and research approaches are required for all applications. Clinical trials or clinical trial aims are not allowed. Funding Opportunity Number: HT942526DMDRPIDA. Assistance Listing: 12.420. Funding Instrument: G. Category: ST. Award Amount: $2.5M total program funding.
The Health and Extreme Weather highlighted topic went live September 1, 2026 with eleven awarding institutes and expires May 1, 2028. There is no set-aside, no separate deadline, and no review criteria — which makes the institute-by-institute language the only real signal, and it is not uniform.
Read articleThe NOURISH Autoimmunity Digital Health Challenge runs three phases to August 2028: 10 winners at $20,000, then 5 at $30,000, then 3 at $100,000. It is a prize competition, not a grant — no indirect costs, no cost reimbursement, and a rule that quietly disqualifies the obvious applicant.
Read articleElevance Health Foundation's maternal/infant health RFP closes July 31, 2026, part of a five-year, $150 million commitment. Last cycle it awarded 29 grants totaling $6.5M across the pregnancy continuum. Here is what the funder actually rewards — measurable disparity reduction, a 15% indirect-cost cap, and scalable models — plus how nonprofits in the 10 priority states should frame a competitive proposal.
Read article