NIH AI Grants

RFA-RM-27-011 funds the development and testing of a multi-use frameworks playbook for precision medicine with AI, within the NIH Common Fund's PRIMED-AI programme. Where other PRIMED-AI awards build tools, validate them or move them toward the clinic, this one produces the shared methodology: a reusable framework that other teams can apply when integrating medical imaging with multimodal health data to build AI-based clinical decision support. The premise is that the field repeatedly re-solves the same design problems, and that codifying frameworks for data harmonisation, model development, evaluation and clinical integration is a public good worth funding separately. Funding is up to 300,000 US dollars per year for two years, which makes this the most accessible entry point into a major NIH AI health programme for groups with strong methodological credentials but without the institutional scale to host a centre. Crucially, the deliverable is to be developed and tested, not merely written, so proposals need a concrete validation plan showing the playbook works when applied by teams other than its authors. The RFA was released on 30 June 2026 with applications due 9 October 2026, the second of the five PRIMED-AI deadlines. For informatics methodologists, implementation scientists and groups working on AI reporting and reproducibility standards, this is a rare opportunity in which methodological synthesis is the funded product rather than a byproduct.

PRIMED-AI is a new NIH Common Fund program that pairs medical imaging with other modalities of health data to build AI-powered clinical decision support tools for precision medicine, and the Data-to-Model Academic-Industrial Partnerships (D2M-AIP) component is its data-and-model engine. Issued as RFA-RM-27-012 under a UG3/UH3 phased cooperative agreement, it funds multidisciplinary academic-industrial teams to take a defined clinical problem, assemble or harmonise the multimodal data needed to address it, and produce a validated AI model intended to function as a software-based medical device. The distinguishing feature relative to ordinary NIH AI grants is the mandatory industrial partnership: NIH is explicit that the translational path from a research model to a regulated clinical tool runs through companies that can carry regulatory submission, deployment and post-market surveillance, and applications without a substantive industry partner are not what this mechanism is for. The UG3 phase establishes the data foundation and demonstrates technical feasibility; the UH3 phase, gated on milestone achievement and NIH approval, carries the model toward clinical implementation and prospective validation. Approximately six to eight awards are expected. PRIMED-AI as a whole launched five coordinated funding opportunities in July 2026 covering data-to-model partnerships, model-to-clinic translation, a validation centre, a logistics centre and a multi-use frameworks playbook, and applicants should read D2M-AIP alongside the Model-to-Clinic announcement (RFA-RM-27-013) to pick the right entry point: D2M-AIP is for teams that still need to build the model, while Model-to-Clinic is for teams that already have a validated prototype.

Bridge to Artificial Intelligence (Bridge2AI) Program is sponsored by NIH Common Fund. The Bridge2AI program aims to accelerate biomedical research by setting the stage for widespread adoption of AI to tackle complex biomedical challenges. It supports the generation of ethically sourced, machine learning-ready datasets, development of tools, and workforce development across different research communities.

D2M-AIP is the discovery-end component of PRIMED-AI, the NIH Common Fund's new Precision Medicine with AI: Integrating Imaging with Multimodal Data program, which the NIH Council of Councils approved as a Common Fund program on April 21, 2025 and which launched five coordinated funding opportunities in 2026. The specific target here is AI-enabled, image-centered, multimodal clinical decision support tools - systems that fuse clinical imaging with other health data streams such as genomics, pathology, laboratory values and electronic health records - developed explicitly as Software as a Medical Device. Two features distinguish this announcement from ordinary NIH AI funding. First, the academic-industrial partnership requirement is structural rather than decorative: projects are meant to be pre-competitive collaborations positioned for eventual commercialization, so a purely academic team without an industry partner is unlikely to be competitive. Second, the phased UG3/UH3 mechanism means the award is gated - the UG3 phase (up to $450,000 direct costs per year) funds development against defined milestones, and transition to the UH3 phase (up to $800,000 direct costs per year) depends on meeting them. Applicants should write the milestone plan as a first-class part of the proposal rather than an afterthought. The emphasis on novel data integration and new AI model development means incremental applications of existing architectures to a new dataset will read poorly; reviewers are looking for methodological advance paired with a credible regulatory and deployment path. Eligibility is broad, including foreign organizations, which is unusual among the five PRIMED-AI announcements - the Validation Center, Logistics Center and Playbook opportunities all exclude foreign entities. Teams whose tool is already a validated prototype rather than a concept should look instead at the companion Model-to-Clinic announcement, RFA-RM-27-013, which shares this deadline.

Bridge2AI Stage 2 advances NIH's flagship biomedical AI initiative from creating ethically sourced, machine-learning-ready datasets to delivering deployable AI tools for specific health challenges. Stage 2 funds Innovation Funnels that use the Stage 1 AI-ready datasets (voice biomarkers, clinical cardiology, salutogenesis, AI/ML for precision public health) to build diagnostic algorithms, drug discovery platforms, and clinical decision support systems. It also establishes a Network for AI Health Science to develop safety protocols, responsible AI implementation guidance, and ethics frameworks for biomedical AI. Strong emphasis on FAIR data principles, transparent model documentation, equity, and public trust.

The NIH AIM-AHEAD (Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity) program establishes partnerships to increase participation of underrepresented researchers in AI/ML development and enhance AI capabilities for addressing health disparities. The program funds small-scale research projects co-led by community-based organizations and academic institution researchers using community-based participatory research (CBPR) approaches. Projects advance AI/ML capacity building for communities across the US, enhance community stakeholder understanding of AI/ML methods, and build capacity for community engagement in AI/ML research. Hub-specific projects support multiple research hubs nationwide. The consortium is funded through NIH Agreement OT2OD032581 and operates through the Office of Data Science Strategy.

Bridge2AI Stage 2 for AI-Ready Health Datasets, Innovation Funnels, and AI Health Science Network is sponsored by National Institutes of Health (NIH) Common Fund. This program aims to accelerate the use of AI in biomedical and behavioral research by generating ethically sourced, machine-learning-ready biomedical datasets and the tools to use them.

Artificial Intelligence and Technology Collaboratories (AITC) for Aging Research Program - Pilot Awards is sponsored by National Institute on Aging (NIA), National Institutes of Health (NIH). The AITC program supports pilot projects that leverage advancements in artificial intelligence and related technologies to improve care, health outcomes, and overall quality of life for older adults, including those living with Alzheimer's disease and related dementias (AD/ADRD)…

Artificial Intelligence (AI) and Machine Learning (ML) approaches to advance environmental health research and decisions is sponsored by National Institutes of Health (NIH). This Funding Opportunity Announcement (FOA) solicits Phase I (R43) SBIR grant applications from small business concerns (SBCs) to develop promising methodologies using AI and ML approaches to advance environmental health research and decisions.

Artificial Intelligence and Technology Collaboratories (AITC) for Aging Research Pilot Awards is sponsored by National Institute on Aging (NIA), NIH. The NIA AITC program earmarks $40 million to fund promising AI technology pilot projects that seek to improve care and health outcomes for older Americans, including persons living with Alzheimer's disease and related dementias (AD/ADRD), and their caregivers.

Bridge to Artificial Intelligence (Bridge2AI) Program - Stage 2 Innovation Funnels and Network for AI Health Science is sponsored by NIH Common Fund. The Bridge to Artificial Intelligence (Bridge2AI) program aims to bridge the gap between biomedical and behavioral research and artificial intelligence. Stage 2 will focus on accelerating health-related research by creating reliable tools and resources specifically designed for AI systems in scientific research. It will support Innovation Funnels, creating tools, devices, and insights using AI-ready datasets, and a Network for AI Health Science, bringing together experts to develop safety measures and frameworks for responsible AI use.

Artificial Intelligence for Alzheimer's Disease (AI4AD2) is sponsored by National Institutes of Health (NIH). This renewed program focuses on advancing Alzheimer's research and treatment through AI. It aims to uncover new genetic and protein-related changes, link them to measurable changes in the brain and behavior, and ensure AI tools work well across global populations.

Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD) is sponsored by NIH Common Fund. This program focuses on building AI/ML capacity at under-resourced institutions and with underrepresented communities. Projects involving no-code AI in dentistry that address health equity and engage diverse populations would be highly relevant.

Bridge to Artificial Intelligence (Bridge2AI) Network for AI Health Science is sponsored by NIH Common Fund. The Bridge2AI program aims to accelerate the widespread use of artificial intelligence (AI) by the biomedical and behavioral research communities. Stage 2 of the program will create networks of multidisciplinary researchers to advance the science of AI science by developing necessary metrics and a framework for trustworthy, reproducible, and explainable AI-enabled biomedical and behavioral research.

The NIH Common Fund's Bridge to Artificial Intelligence (Bridge2AI) program accelerates the use of AI in biomedical and behavioral research by generating ethically sourced, AI-ready datasets and the tools to use them. On January 29, 2026, the NIH Council of Councils approved Bridge2AI to advance to Stage 2, with approximately $130 million over four years (pending appropriations). Stage 2 will fund Innovation Funnels that translate Bridge2AI's flagship datasets into validated clinical tools, and a Network for AI Health Science that develops safety, validation, and benchmarking protocols for health AI. Stage 2 RFAs had not yet been posted as of mid-2026 but are expected during 2026, with individual award amounts to be specified in those announcements.

AIM-AHEAD Program for Artificial Intelligence Readiness (PAIR) is sponsored by National Institutes of Health (NIH) Common Fund. The NIH's AIM-AHEAD (Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity) program establishes mutually beneficial partnerships to empower researchers and communities in developing AI/ML models. It enhances capabilities using electronic health record data and other datasets to address health inconsistencies, and implements training opportunities in data science, large-scale data management, cloud computing, and AI/ML analytics.

Bridge to Artificial Intelligence (Bridge2AI) Program - Stage II: Innovation Funnels and Network for AI Health Science is sponsored by NIH Common Fund. Building upon Stage 1's creation of AI-ready biomedical datasets, Stage II will support two initiatives: Innovation Funnels to create AI-enabled tools and insights to improve health, and a Network for AI Health Science to develop safety measures and a framework for responsible A…

Artificial Intelligence, Machine Learning, and Deep Learning (NIBIB) is sponsored by National Institute of Biomedical Imaging and Bioengineering (NIBIB), NIH. Supports mission-aligned projects focused on the development of transformative machine intelligence-based systems, emerging tools, and modern technologies for diagnosing and recommending treatments for a range of diseases and health conditions. This includes early-stage development of software, tools, and reusable convolutional neural networks.

Bridge to Artificial Intelligence (Bridge2AI) Program - Stage 2 is an upcoming grant from the NIH Common Fund that will build upon Stage 1 accomplishments to use AI-ready biomedical datasets, tools, and best practices to address major biomedical and behavioral health challenges. Stage 1 committed $130 million over four years to generate flagship AI-ready datasets and workforce development resources, which are now available through the Bridge2AI portal. Stage 2 will support two initiatives: Innovation Funnels using AI-ready datasets to create tools and insights that improve health outcomes, and a Network for AI Health Science to develop safety measures for responsible AI use in research. Eligible applicants and award amounts for Stage 2 have not yet been published. The program was approved for a second stage as of January 2026.

Bridge to Artificial Intelligence (Bridge2AI) is sponsored by National Institutes of Health (NIH) Common Fund. This NIH-wide program aims to build ethically sourced, AI-ready biomedical datasets. Stage 2, approved in January 2026, will move from data creation to application, using existing datasets to build trusted AI tools for real health challenges.

AI Research Grants is sponsored by National Institutes of Health (NIH). The NIH funds AI Research Grants for universities and research institutions, with a focus on supporting originality and creativity in biomedical research applications. NIH promotes the safe and responsible use of AI in biomedical research through programs that support the development and use of algorithms and models for research, contribute to AI-ready datasets that accelerate discovery, and encourage multi-disciplinary partnerships that drive transparency, privacy, and equity.

Bridge to Artificial Intelligence (Bridge2AI) Program is sponsored by National Institutes of Health (NIH) Common Fund. The Bridge2AI program will propel biomedical research forward by setting the stage for widespread adoption of artificial intelligence that tackles complex biomedical challenges beyond human intuition. It aims to build AI-ready biomedical datasets and ethical frameworks for their use, and will create tools, devices, and novel insights that use AI to improve health. The program also supports workforce development across different research communities.

NIH Common Fund Bridge2AI (Bridge to Artificial Intelligence) Program - Stage 2 is sponsored by National Institutes of Health (NIH) Common Fund. Bridge2AI is a flagship NIH program to build AI-ready biomedical datasets and ethical frameworks. Stage 2 shifts focus from dataset creation to delivering deployable tools for specific health challenges. Universities that built infrastructure under Stage 1 are well-positioned for continuations, but new applications aligning with AI-driven biomedical research, including imaging, could be relevant.

Bridge2AI, an NIH Common Fund program, invests roughly $130 million over four years to generate flagship, ethically sourced, machine-learning-ready biomedical and behavioral datasets, together with the tools, standards, and skills needed to make AI/ML widely usable across biomedical research. In January 2026 the NIH Council of Councils approved Bridge2AI's move into Stage 2, shifting from dataset creation toward delivering trusted, deployable AI tools for specific health challenges. Awards support large multi-institutional Grand Challenge data-generation projects and a cross-cutting integration, dissemination, and ethics center.

Bridge to Artificial Intelligence (Bridge2AI) Stage 2: Innovation Funnels and Network for AI Health Science is sponsored by NIH Common Fund. The Bridge2AI program aims to propel biomedical research by generating new AI-ready biomedical datasets and best practices for machine learning analysis. Stage 2 will build upon these accomplishments to use the generated data, tools, and best practices to deliver trusted solutions for major biomedical and behavioral health challenges through Innovation Funnels and a Network for AI Health Science.

NIH Artificial Intelligence and Technology Collaboratories (AITC) for Aging Research Program is sponsored by National Institute on Aging (NIA), National Institutes of Health (NIH). The AITC program serves as a national resource to promote the development and implementation of AI approaches and technology through demonstration projects to improve care and health outcomes for older Americans, including persons with dementia and their caregivers.

NIH Common Fund Bridge to Artificial Intelligence (Bridge2AI) Stage 2 for AI Tools, Devices and Safety Frameworks in Biomedical Research is sponsored by U.S. National Institutes of Health (NIH), Common Fund. Bridge2AI Stage 2 will focus on building tools, devices, and novel insights that apply AI to improve health, and will also include a Network for AI Health Science to develop safety measures for responsible AI use and research.

AIM-AHEAD Program is sponsored by National Institutes of Health (NIH). NIH's AIM-AHEAD program establishes mutually beneficial and coordinated partnerships to empower researchers and communities in the development of AI/ML models and enhance the capabilities of this emerging technology, beginning with electronic health record (EHR) data. The program aims to integrate AI/ML-focused, data science research networks with community engagement and clinical research networks.