AI Healthcare Grants

The Evidence for AI in Health (EVAH) initiative is a $60 million joint investment by the Gates Foundation, Novo Nordisk Foundation, and Wellcome Trust to support rigorous, country-led evaluations of AI health tools in low- and middle-income countries. Delivered in partnership with J-PAL and the African Population and Health Research Center, EVAH funds evaluations of AI-enabled clinical decision support tools in primary and community healthcare settings across Sub-Saharan Africa, South Asia, and Southeast Asia. Pathway A supports early-deployment evaluations focusing on usability, workflow integration, and safety for up to $1 million. Pathway B funds randomized controlled trials, economic analyses, and implementation science studies of tools ready for deployment at scale for up to $3 million. The initiative addresses a critical evidence gap about whether AI diagnostic and clinical decision support tools actually improve health outcomes in resource-limited settings.

University of Oxford Institute for Ethics in AI Accelerator Fellowship Programme is sponsored by University of Oxford Institute for Ethics in AI. Flexible, self‑directed research fellowship for individuals across sectors to develop ethical, policy‑relevant AI research under one of core themes (e. g. , AI and Mental Health, AI and Humanity)

Small Business Programs (SBIR & STTR) (Omnibus Solicitation) is sponsored by National Institutes of Health (NIH). While primarily for small businesses, universities can partner with small businesses through STTR programs. This omnibus solicitation supports research and development in biomedical/behavioral research areas, including health tech/AI. It covers AI for digital health, such as AI diagnostics or mobile health apps, and AI techniques for characterizing and minimizing errors in health-related datasets. This could be relevant for early-stage validation of commercial AI for fracture detection.

Small Business Innovation Research (SBIR) and Small Business Technology Transfer (STTR) Programs (Omnibus Solicitation) is sponsored by National Institutes of Health (NIH). This program provides non-dilutive funding for small business entrepreneurs to conduct early-stage research and development in biomedical technology. It supports research and development in biomedical/behavioral research areas, including health tech/AI for digital health, AI diagnostics, mobile health apps, and AI techniques for characterizing and minimizing errors in health-related datasets.

Wellcome Discovery Awards provide long-term flexible funding for established researchers and teams pursuing bold, creative research that can deliver significant shifts in understanding of human life, health, and wellbeing. AI and computational approaches to biomedical challenges are explicitly welcome, including machine learning for drug discovery, AI-driven clinical diagnostics, computational genomics, and data science for health equity. The world's second largest private funder of medical research (endowment approximately £14.5 billion), Wellcome supports interdisciplinary teams tackling fundamental health questions using novel methodologies. Discovery Awards are discipline-agnostic and value transformative potential over incremental advances, making them ideal for ambitious AI-health intersection research.

RFA-LM-26-004 is the National Library of Medicine's institutional training grant program for building the research workforce in biomedical informatics, data science, and artificial intelligence. NLM funds universities and research institutions to run predoctoral and postdoctoral training programs that prepare researchers to apply computational and AI/ML methods to health and biomedical problems - clinical decision support, medical imaging analysis, electronic health record modeling, drug discovery informatics, and public health data science among them. The program is one of the most durable pipelines in health AI: NLM institutional training grants have seeded a large share of the academic biomedical informatics faculty in the United States. The FY2026 competition makes $12,000,000 available across approximately 25 awards, with individual awards from $500,000 to $775,000. Because this is a T-series institutional mechanism rather than a research project grant, the application is built around program design, mentor quality and depth, trainee recruitment and retention plans, and curriculum integration across computer science, statistics, and biomedical disciplines - not around a specific research aim. Institutions with an existing informatics or computational biology program and a demonstrable mentor pool are the realistic applicants. The estimated post date is 2 June 2026 with applications due 25 September 2026, awards in January 2027, and project start on 1 July 2027.

This Digital Europe Programme call funds the deployment of AI-powered image screening tools in medical centres across the European Union for cancer and cardiovascular disease detection. Part of a €63.2 million package of seven Digital Europe calls published on April 21, 2026, this specific topic focuses on integrating validated AI diagnostic tools into clinical workflows to improve early detection rates and screening efficiency. Projects should demonstrate scalable AI-powered diagnostic imaging solutions that can be deployed across multiple medical centres and health systems within the EU. The call supports the European Health Data Space initiative and aligns with the EU's digital health strategy.

Under the European Commission's GenAI4EU initiative and the Digital Europe Programme, this call (DIGITAL-2026-AI-PILOTING-10-SCREENING) supports the development, deployment, and large-scale clinical validation of cloud-based AI systems for medical image screening across European hospital networks. Funded projects will integrate AI diagnostic tools into routine clinical workflows for cancer (mammography, lung CT, dermatology, pathology) and cardiovascular imaging, establish multi-country networks of AI-powered screening centres, demonstrate clinical performance on large patient datasets, address EU AI Act and MDR compliance, and deliver health-economic evidence to support reimbursement and scale-up. Cybersecurity, data protection under GDPR, and federated learning architectures for cross-border data governance are emphasized.

The PRIMED-AI Logistics Center is the coordinating hub for the NIH Common Fund's Precision Medicine with AI consortium, and it is the least research-like and most operationally demanding of the program's five announcements. The Center runs three integrated cores - Administration, Evaluation and Outreach - and its responsibilities are concrete: convening consortium meetings, developing and maintaining consortium policies, administering and distributing restricted funds for collaborative cross-site projects, conducting program evaluation, and building a public web portal that showcases the AI tools developed by PRIMED-AI awardees. The budget shape is the detail most likely to be misread. Year one is capped at $575,000 in direct costs, while years two through five allow up to $2,500,000 per year; that increase is not an expanding administrative budget but the restricted collaborative-project funds the Center passes through to consortium members, so applicants should build the budget narrative to make that distinction explicit. Only one award is expected, making this a single-winner competition, and the selection will favor institutions with a demonstrated record of running multi-site research consortia, managing subaward distribution, and sustaining public-facing scientific web infrastructure over those offering primarily scientific expertise in medical AI. Foreign organizations and foreign components are ineligible. The October 2, 2026 deadline is shared with the Validation Center announcement (RFA-RM-27-014); the two roles are complementary but distinct, with the Validation Center handling technical assessment of tools and the Logistics Center handling coordination, policy and dissemination. Organizations weighing which to pursue should choose based on whether their comparative strength is benchmarking science or consortium operations.

Joint NSF/NIH interagency program (NSF 25-542) supporting transformative, high-risk/high-reward advances in AI and advanced data science for biomedical and public health research. Funds interdisciplinary teams developing novel methods to collect, sense, connect, analyze, and interpret health data from individuals, devices, and systems. Six priority themes: fairness and trustworthiness in health AI systems, transformative analytics using AI/ML for biomedical research, multimodal wearable and implantable biomarker sensing systems, cyber-physical systems for closed-loop health interventions, robotics for health outcomes, and biomedical image interpretation combining human perception with computational analysis. All proposals require a mandatory Collaboration Plan demonstrating cross-disciplinary integration.

Precision Medicine with AI: Integrating Imaging with Multimodal Data (PRIMED-AI) Development and Testing of Multi-use Frameworks Playbook (U01 Clinical Trial Not Allowed) is sponsored by National Cancer Institute (NCI) (part of NIH Common Fund). This program supports the development and testing of standardized frameworks for multimodal AI clinical decision support tools. It aims to integrate imaging with multimodal data for precision medicine, directly relevant to AI diagnostics, including breast cancer.

This is the responsible-AI and governance strand of the NIH Common Fund's PRIMED-AI program, and it is by a wide margin the most accessible of the program's five announcements - two years, up to $300,000 in direct costs per year, roughly five awards, and no requirement to build a clinical tool or assemble an industrial partnership. What it funds instead is methodological infrastructure: standardized, reusable frameworks that address the recurring problems in multimodal AI clinical decision support, drawn from a defined list of topics including data quality, data harmonization across heterogeneous sources, model transparency, patient privacy, and regulatory navigation. A specific structural requirement shapes the whole application - proposals must develop and test two or more distinct frameworks on separate topics, so a deep single-topic proposal will not satisfy the announcement. The word testing carries real weight here as well; NIH is not funding position papers or consensus documents but frameworks that are empirically exercised against realistic multimodal health data and imaging scenarios, so applicants should plan concrete validation work into a compressed two-year timeline. Because the output is meant to serve the wider PRIMED-AI consortium and eventually the field, teams that combine informatics or AI methods expertise with genuine regulatory, bioethics or data-governance depth are better positioned than pure methods groups. The award mechanism is U01 with clinical trials not allowed, and foreign entities are ineligible. For research groups interested in trustworthy and responsible health AI but lacking the scale for the program's multi-million dollar center or tool-development awards, this is the natural entry point into PRIMED-AI, and the modest budget makes it realistic for a single well-composed interdisciplinary team.

Examining the Impact of Artificial Intelligence (AI) on Healthcare Safety (R18 PA-24-261 for Evaluating Deployed Clinical AI Systems) is sponsored by Agency for Healthcare Research and Quality (AHRQ). Funds research to determine (1) whether and how breakthrough uses of AI systems affect patient safety in real clinical environments and (2) how AI systems can be safely implemented and used in healthcare delivery. This NOFO explicitly does not support development of new AI systems but rather the rigorous post-deployment evaluation of AI in live clinical settings. Topics can include AI-driven diagnostic safety and equity impacts of clinical AI, which could be applied to breast cancer imaging AI.

AHRQ PA-24-261 funds research to determine (1) whether and how breakthrough uses of AI systems affect patient safety in real clinical environments and (2) how AI systems can be safely implemented and used in healthcare delivery. The R18 mechanism funds evaluation - this NOFO explicitly does not support development of new AI systems but rather the rigorous post-deployment evaluation of AI in live clinical settings, complementing AHRQ's broader digital healthcare portfolio. Topics include AI failure mode analysis, alert fatigue from clinical AI, AI-driven diagnostic safety, equity impacts of clinical AI, and human-AI team performance in care delivery. Strong fit for health services researchers, patient safety researchers, and health systems studying AI deployment outcomes.

Teaching Future Doctors to Team with AI: A Social Science Approach to Developing and Evaluating Training Methods for Clinical-AI Collaboration Across the ARiSE Network is sponsored by The Macy Foundation. Aims to train doctors to work effectively with AI, enhancing patient safety and adapting to new medical technologies through social science methodologies.

UT Research, Engineering, and Application Laboratory for Healthcare Artificial Intelligence (UT-REAL-Health-AI) is sponsored by Texas State Government (The University of Texas System). This systemwide initiative aims to advance AI across all UT health campuses, establishing a coordinated infrastructure to improve patient care and safety, strengthen clinician efficiency, advance research, and prepare the workforce for an AI-enabled future.

ADVOCATE (Agentic AI-EnableD CardioVascular CAre TransfOrmation) is sponsored by ARPA-H. The ADVOCATE program aims to transform advanced cardiovascular disease management with an agentic AI system that can provide 24/7 holistic clinical care. It seeks to develop clinical AI agents that can be trusted to autonomously adjust changes in appointments, medications, diet, and exercise, and a supervisory AI "overseer" to monitor these agents for continued safety and efficacy. The program also encourages proposals from small businesses.

Agentic AI-EnableD CardioVascular CAre TransfOrmation (ADVOCATE) is sponsored by Advanced Research Projects Agency for Health (ARPA-H). This program aims to develop and implement artificial intelligence tools for cardiovascular disease management. It seeks to use AI to connect to patient records, assist in scheduling appointments, provide diet and physical therapy recommendations, and write and modify prescriptions. The program also involves developing a 'supervisory agent' to support surveillance of clinical AI agents for safe and effective recommendations.

Advancing Medical Artificial Intelligence with Foundation Models is sponsored by Not specified (UK based, but indicative of relevant research areas for US universities). This project aims to drive significant advancements in medical Artificial Intelligence (AI) by developing versatile and efficient medical foundation models. While the example provided is UK-based, the focus on medical AI and foundation models is highly relevant to US universities working on quantitative imaging and self-supervised learning for MRI denoising.

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.

NIH Bridge2AI Stage 2 represents the next phase of the NIH Common Fund Bridge to Artificial Intelligence program building on $130 million invested in Stage 1 to create ethically sourced AI-ready biomedical datasets. Stage 2 shifts focus from data generation to building tools devices and safety frameworks that translate those datasets into clinical and research applications. Two interconnected initiatives are funded: Innovation Funnels supporting teams that use Stage 1 AI-ready datasets to create practical tools including diagnostic algorithms drug discovery platforms and clinical decision support systems that demonstrate measurable health impact and a Network for AI Health Science developing safety measures validation protocols and responsible-use frameworks for AI in health research. The program values interdisciplinary teams combining computational scientists with domain experts in specific disease areas. Stage 2 Requests for Applications are expected by mid-2026. This is distinct from ARPA-H programs which fund specific high-risk clinical AI applications and from AHRQ healthcare AI safety grants which examine existing AI impact on healthcare systems.

Discovering the Future of AI grants program is sponsored by Penn AI (University of Pennsylvania). Provides faculty with resources to pursue research and education in AI and its applications, fostering synergies between AI advances and novel applications across disciplines. Focus areas include AI + Education, AI + Health, AI + Science, and AI + Society.

The Smart Health and Biomedical Research in the Era of Artificial Intelligence and Advanced Data Science (SCH) program is a joint NSF-NIH solicitation (NSF 25-542) supporting high-risk, high-reward advances in AI, machine learning, and data science for fundamental biomedical and public health research. Projects must cross disciplinary boundaries, pairing computer and data scientists with clinicians, public health researchers, or biomedical experts. Priority areas include AI-driven diagnostics, clinical decision support, sensing and imaging, and trustworthy health AI. Awards reach up to $1.2 million (with some larger multi-year projects), and NSF invests $15-20 million per cycle.

ARPA-H's PRECISE-AI (Performance and Reliability Evaluation for Continuous Modifications and Useability of Artificial Intelligence) program develops techniques to detect when AI-enabled medical tools used in real-world clinical settings fall out of alignment with their underlying training data and auto-correct these tools to maintain peak performance. The program addresses a critical challenge in healthcare AI deployment: ensuring AI diagnostic and clinical decision support tools remain accurate over time as patient populations, disease patterns, and clinical practices evolve. Performer teams of ML experts, health information specialists, and clinicians will develop capabilities for establishing accurate diagnostic ground truth, monitoring AI performance autonomously, determining root causes of performance degradation, enabling AI models to communicate uncertainty to clinicians, and creating data infrastructure for sharing findings among healthcare stakeholders.

AI & Digital Health Innovation (AI&DHI) Mental Health Research Grants is sponsored by National Institute of Mental Health (NIMH) (via AI & Digital Health Innovation at University of Michigan). This grant supports the development of new precision medicine approaches to improve mental health care access and outcomes, leveraging AI and digital health.

NSF SBIR Phase I - Artificial Intelligence is sponsored by National Science Foundation. NSF SBIR funds high-risk AI innovation and R&D. A bike repair shop could only qualify if developing a novel AI technology for commercialization (e. g. , custom AI diagnostic tool for bike repairs), not for adopting existing AI tools.

Lacuna Fund is a first-of-its-kind multi-funder collaboration formed in 2020 (and transferred to Global South leadership in July 2025) to fill gaps in data used to train Machine Learning models, making ML and AI more representative, accurate, equitable, and accessible to underserved communities worldwide. The Fund supports grantees to create high-quality, openly accessible machine-learning datasets that serve urgent problems in Africa, Asia, and Latin America across four thematic areas: agriculture (crop monitoring, smallholder farming, soil and weather datasets); language (low-resource language NLP datasets, including text, speech, and machine translation across 29+ African languages and indigenous Latin American languages); health (clinical AI datasets, disease surveillance, epidemiology); and climate (climate adaptation, weather prediction, ecosystem monitoring). Following the July 2025 leadership transition, the Fund is now governed by ACTS (African Centre for Technology Studies), CENIA (Chile's National Centre for AI), Masakhane, and the University of Pretoria's Data Science for Social Impact Research Group — putting Global South institutions firmly in control of priorities, calls, and grantmaking decisions. Calls are issued in cohorts by thematic area; the 2026 cohort emphasizes climate datasets and continued investment in African language NLP.

The Clinton Health Access Initiative offers a single-partner catalytic grant of $700,000 to develop and deploy AI-assisted cervical visual triage for cervical cancer screening in low-resource settings. The solution must provide offline functionality and same-encounter results so that women can be triaged and treated in a single visit. The award supports AI diagnostics that expand access to cervical cancer screening in global health contexts.

Colorectal Cancer Research Program (CRCRP) is sponsored by U.S. Department of Defense, Congressionally Directed Medical Research Programs (CDMRP). The CRCRP supports high-impact biomedical research focused on reducing morbidity and mortality of colorectal cancer, with a unique prioritization of early-onset colorectal cancer. It covers a broad spectrum of research, including prevention, early detection, precision oncology, tumor biology, molecular/genetic risk factors, treatment optimization, survivorship, and technology-enabled patient monitoring. Priority topics include digital risk-stratification tools, telehealth-assisted diagnostics, novel biomarkers, surgical decision-support platforms, adaptive clinical trial methodologies, and disparities research. Eligibility is broad, including academic centers, universities, non-profits, federally funded R&D centers, and for-profit entities like biotechnology and AI diagnostic firms.

NIH Small Business Innovation Research and Small Business Technology Transfer Programs is sponsored by National Institutes of Health (NIH). NIH's SBIR and STTR programs support research and development in small businesses with a focus on biomedical/behavioral research areas, including health tech/AI. This could involve AI for digital health, such as AI diagnostics or mobile health apps, and AI techniques for characterizing and minimizing errors in health-related datasets.

Discovering the Future of AI Grant Program is sponsored by Emory University - Office of the Vice Provost For Research. Provides faculty with resources to pursue paradigm-shifting research and education in AI and its applications, fostering collaboration between AI experts and domain experts. Focuses on AI Foundations, AI + Business, AI + Education, AI + Health, AI + Science, and AI + Society.

UT REAL Health AI (systemwide initiative) is sponsored by University of Texas System. This systemwide initiative aims to responsibly accelerate artificial intelligence in health across all UT health campuses. It establishes a coordinated infrastructure to improve patient care and safety, strengthen clinician efficiency, advance research, and prepare the workforce for an AI-enabled future.

Call for proposals to support high-potential European micro, small, and medium-sized enterprises (SMEs) developing innovative healthcare solutions is sponsored by European Commission (EIT Health) (via EU Funding Portal). This call targets high-potential European micro, small, and medium-sized enterprises (SMEs) developing innovative healthcare solutions in biotech, medtech, digital health, AI, and biomarkers or diagnostics.

USAID Broad Agency Announcement for Global Health Challenges is sponsored by United States Agency for International Development (USAID). USAID invites applicants to co-create, co-design, co-invest, and collaborate in the research, development, piloting, testing, and scaling of innovative, practical, and cost-effective interventions to address pressing problems in global health. This broad announcement can encompass digital health AI solutions for clinical decision support in global health contexts like Kenya and Somalia.

Southeastern Health AI Consortium - Collaborative Grants (Track 1 Novel Health Insights) is sponsored by Medical University of South Carolina (MUSC) and University of Florida Clinical and Translational Science Institute (UF CTSI). This track supports research collaborations between MUSC and UF that leverage artificial intelligence to address significant health issues in South Carolina and Florida. The consortium aims to accelerate discovery, improve care, and transform health across the Southeast by supporting interdisciplinary teams using AI and large clinical datasets.

The Patrick J. McGovern Foundation Grants (various AI-related projects) is sponsored by The Patrick J. McGovern Foundation. The foundation supports a range of initiatives leveraging AI and data for social good. Examples of recent grants include support for AI fluency programs, AI-driven tools for debt relief, AI-driven triage systems for displaced populations, and enhancing AI-driven tools for tracking environmental impacts.

AI for Health Equity, Analytics, and Diagnostics (AHEAD) Center (seed funding) is sponsored by New York State (Governor Kathy Hochul and SUNY Upstate Medical). This seed funding supports the establishment of the AI for Health Equity, Analytics, and Diagnostics (AHEAD) Center at SUNY Upstate Medical, as part of a broader New York State initiative to advance AI for the public good.

AI Support for Texas Healthcare Needs is sponsored by Texas State Government (Texas Department of State Health Services). This initiative focuses on the integration of AI technology into emergency healthcare response in Texas. The program aims to streamline emergency services across the state, optimizing response times and resource allocation specific to Texas' varied demographics, and will support projects that develop innovative AI applications tailored to real-time data management in emergency medical services. Universities within the UT System have received legislative approval and funding to accelerate AI in health.

AI and Health Seed Funding Program is sponsored by UC Davis (Center for Information Technology Research in the Interest of Society and the Banatao Institute (CITRIS), Office of Research, College of Engineering, AI Center in Engineering, School of Medicine Office of Research, Betty Irene Moore School of Nursing, Healthy Aging in a Digital world initiative and CITRIS Health). This program strengthens interdisciplinary connections between schools and colleges on the Davis campus and schools of health on the Sacramento campus, aiming to position UC Davis as a leader in interdisciplinary AI-enabled health care technology research.

Southeastern Health AI Consortium - Collaborative Grants (Track 2 Methodologic Innovation) is sponsored by Medical University of South Carolina (MUSC) and University of Florida Clinical and Translational Science Institute (UF CTSI). This track provides seed funding to MUSC-UF teams to develop new approaches in health services research, biomedical informatics, or data science. The initiative balances innovation with practical clinical application and supports projects with an AI-focused component or those that lay the groundwork for future AI applications.

The Evidence for AI in Health (EVAH) initiative is a $60 million joint commitment from the Bill & Melinda Gates Foundation, Novo Nordisk Foundation, and Wellcome Trust, announced at the AI Impact Summit in New Delhi in February 2026. EVAH funds locally-led evaluations of AI health tools in sub-Saharan Africa, South Asia, and Southeast Asia, with the goal of generating high-quality real-world evidence on the clinical, equity, and economic impacts of AI-enabled diagnostics, decision support, triage, screening, and population health tools in low- and middle-income country (LMIC) health systems. The initiative emphasizes researcher leadership from LMICs themselves, capacity-building for local evaluation infrastructure, and producing evidence that informs procurement, regulation, and scale-up decisions by ministries of health and global health funders. EVAH addresses a critical gap: rapid AI tool deployment in LMIC settings without rigorous evidence on safety, effectiveness, and equity.

Microsoft's AI for Earth program awards Azure cloud compute credits to support AI projects addressing environmental challenges. Compute credit grants are tiered at $5,000, $10,000, or $15,000 depending on project scope, focused on four key program areas: agriculture (precision farming, crop monitoring, soil health AI); biodiversity (species detection, habitat mapping, wildlife conservation AI); climate change (emissions modeling, carbon accounting, climate adaptation AI); and water (water quality, drought prediction, watershed health AI). Beyond compute credits, grantees receive machine learning expertise, collaboration with Microsoft Research AI for Good Lab, mentorship, and access to the Microsoft Planetary Computer platform with petabytes of geospatial Earth observation data and APIs. Successful applicants typically have a demonstrated background in environmental science and/or technology, at least one team member with strong technical/ML skills, and are close to or finished with data collection ready for computation and model building. Applications are evaluated on quarterly rolling basis.

Maximizing Investigators' Research Award for Early State Investigators (MIRA) (R35) - Pediatric Sepsis Program is sponsored by National Institute of General Medical Sciences (NIGMS). This MIRA grant supports the establishment of a comprehensive sepsis program at Children's National Hospital. The program includes an AI-based, biomarker-enhanced platform for early recognition of pediatric sepsis and extracellular vesicle-based therapeutics to decrease mortality and long-term sequelae.

Discovering the Future of AI Grant Program is a grant from the Office of the Vice Provost for Research at the University of Pennsylvania that funds faculty pursuing paradigm-shifting research and education in artificial intelligence and its applications. The program supports one-year projects aligned with PennAI's strategic thrusts: AI Foundations, AI and Business, AI and Education, AI and Health, AI and Science, and AI and Society. Proposals from joint teams of two co-principal investigators from different schools are especially encouraged, pairing core AI/ML experts with domain specialists. Eligible applicants are University of Pennsylvania faculty. Successful projects demonstrating broader university impact may be eligible for additional funding in subsequent years.

Open Data Micro-Grant Call (Health Equity Award) is a grant from the MICCAI Society that funds the creation of original, publicly available open datasets in medical imaging and clinical AI, with an emphasis on health equity, diversity, and under-represented populations. The program offers two grant tiers: four small grants of $650 and four large grants of $1,200. Eligible costs include data preparation and curation, annotations and quality control, and documentation for public data release; travel costs are not eligible. Eligible applicants are researchers from academia, hospitals, or industry—individuals or teams—with no geographic or career-stage restrictions. Datasets must be released with an open license, and funding is released after paper submission (acceptance is not required). Ethical approval must be obtained prior to application.

Innovative Health Initiative Joint Undertaking (IHI JU) is a grant from the European Commission that funds collaborative, cross-sector health research projects spanning pharmaceutical, digital, IT, medical devices, and diagnostics industries. The IHI JU aims to translate health research and innovation into tangible patient benefits by supporting breakthroughs that cross traditional sector boundaries, such as medical device-drug combinations and AI diagnostics. Projects span the full spectrum of care from prevention to diagnosis and treatment, with a focus on unmet public health needs and making Europe's health industries globally competitive. Eligible applicants include health-related industries, SMEs, academic institutions, clinical organizations, patient organizations, and other health research entities from EU member states. Award amounts vary by project call.

NYS Department of Health AI Solutions in Healthcare (part of FY27 Budget initiatives) is sponsored by New York State Department of Health (NYSDOH). As part of the FY27 Enacted Budget, Governor Hochul's initiatives include incentivizing partnerships between safety net hospitals and other healthcare partners to implement AI solutions that improve quality of care and strengthen operations in New York State.

Small Business Innovation Research (SBIR) and Small Business Technology Transfer (STTR) Programs (Omnibus) is sponsored by National Institutes of Health (NIH), Centers for Disease Control and Prevention (CDC), Food and Drug Administration (FDA). This program provides non-dilutive funding to U.S. small businesses developing innovative health, life sciences, biomedical, public health, and FDA-relevant technologies. It supports projects from early-stage feasibility through later-stage R&D and commercialization activities. The focus areas include biomedical/behavioral research, digital health (such as AI diagnostics or mobile health apps), and AI techniques for characterizing and minimizing errors in health-related datasets. This is a broad opportunity for technologies aligning with the missions of participating NIH Institutes, CDC Centers, or FDA Centers. NIDDK (National Institute of Diabetes and Digestive and Kidney Diseases) participates in this program and is interested in projects that include robust timelines for commercialization, requisite fundraising, and all required regulatory milestones, particularly those supporting completion of research for an Investigational New Drug (IND) application or Investigational Device Exemption (IDE).

Discovering the Future of AI Grant Program (AI + Health; AI + Science) is sponsored by University of Pennsylvania (Office of the Vice Provost For Research). This program provides faculty with resources to pursue paradigm-shifting research and education in AI and its applications, including revolutionizing healthcare through AI-driven diagnostics, personalized medicine, computational biology, and applying AI to accelerate discovery.

Small Business Innovation Research (SBIR) Program (NCATS) is sponsored by National Institutes of Health (NIH) / National Center for Advancing Translational Sciences (NCATS). The SBIR program provides funding to U. S. -owned and operated small businesses for research and development with commercialization potential. This includes health-related technologies and research innovations, such as AI diagnostics.