AIM-AHEAD Program for Artificial Intelligence Readiness (PAIR)

AIM-AHEAD PAIR Phase 1 — Institutional AI Readiness Grant

Funds institutional readiness in under-resourced hospitals, tribal colleges, and community centers to launch AI health research.

The AIM-AHEAD Program for Artificial Intelligence Readiness (PAIR) is a flagship North Star IV initiative under NIH's AIM-AHEAD consortium — the Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity, launched by NIH in July 2021. PAIR operates as a two-phase capacity-building program specifically designed to help institutions with limited AI/ML infrastructure — including Minority-Serving Institutions, HBCUs, Tribal Colleges, smaller community hospitals, and Federally Qualified Health Centers — establish in-house AI/ML Health Research Labs capable of competing for national research funding.

Phase 1 provides up to $100,000 per institution, with Cohort 1 issuing 15 awards. In addition to direct lab-establishment funding, awardees receive grant-writing coaching and connection to AIM-AHEAD's expert network and data resources. Top Phase 1 performers are eligible for Phase 2 follow-on funding, though Phase 2 award amounts are not published on public pages. The program is directed by Gordon Gao, PhD, MBA; Toufeeq Syed, PhD; and Harlan Jones, PhD. Awards are issued as sub-awards under the AIM-AHEAD Coordinating Center's Other Transactions Agreement (OT2OD032581) via Research Opportunity Announcements (ROAs), not through NIH grants.gov. Cohort 1 Phase 1 and Phase 2 are complete.

Applicants pursuing future cohorts should monitor aim-ahead.net for new ROA postings. Because eligibility is explicitly gated on limited existing AI/ML infrastructure, established R1 research universities are not the target. The three program goals — strengthening institutional AI readiness, accelerating AI-driven health research through grant-writing support, and deepening collaboration across the AIM-AHEAD clinical AI network — map directly to PAIR's scoring criteria. Institutions demonstrating infrastructure gaps and a credible plan to stand up an AI/ML Health Research Lab are best positioned to compete.