AI Healthcare Grants

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.

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.

PRIMED-AI is a new NIH Common Fund programme, Precision Medicine with AI: Integrating Imaging with Multimodal Data, which combines medical imaging with other health data to build AI-powered clinical decision support tools for personalised medicine. This opportunity, RFA-RM-27-014, funds the programme's Validation Center: a dedicated hub that independently evaluates and characterises the AI-enabled, image-based multimodal clinical decision support tools developed elsewhere in the consortium. The structural logic matters for applicants. NIH has separated tool-building from tool-validation and is paying separately for the second, which signals institutional recognition that self-reported performance claims from developing teams are not sufficient evidence for clinical AI. The Center's remit covers verification, validation, interoperability and uncertainty quantification, so the deliverable is a reproducible evaluation capability rather than a set of papers. It operates as a U54 cooperative agreement, meaning NIH staff are substantively involved and the Center must work in concert with the PRIMED-AI Logistics Center and the Data-to-Model and Model-to-Clinic award recipients. The RFA was released on 30 June 2026 with applications due 2 October 2026. For academic groups that have built credible AI evaluation methodology, particularly in radiology, imaging informatics or biostatistics, this is an unusually direct route to becoming the reference evaluator for a major federal AI health programme, and the position carries influence over how clinical AI performance gets measured well beyond the life of the award.

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.

GenScript Life Science Research Grant Program (Global Pioneer Grant, Rising Star Grant, AI Drug Discovery Frontier Grant, Spark Pitch Grant) is sponsored by GenScript. This global initiative supports life science innovation, including projects that contribute to critical areas of life science research such as AI-drug discovery, antibody development, cell and gene therapy, vaccines, diagnostics, and biologics.

GenScript Life Science Research Grant (LSRG) is sponsored by GenScript. The GenScript Life Science Research Grant is a global initiative providing annual funding to support researchers shaping the future of life science. It supports projects that can significantly contribute to critical areas of life science research, including AI-drug discovery, antibody development, cell and gene therapy, vaccines, diagnostics, and biologics.

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.

Precision Medicine with AI: Integrating Imaging with Multimodal Data (PRIMED-AI) Development and Testing of a Multi-use Frameworks Playbook for Responsible Multimodal Health AI (RFA-RM-27-011) is sponsored by NIH Common Fund. This opportunity supports the development and testing of a multi-use frameworks playbook for responsible multimodal health AI, addressing the responsible-AI and governance strand of the NIH Common Fund's PRIMED-AI program.

Model-to-Clinic, RFA-RM-27-013, is the translational arm of the NIH Common Fund's PRIMED-AI programme. It funds teams to take AI-enabled, image-based multimodal clinical decision support models and move them toward actual use in clinical settings, addressing the gap that has defined clinical AI for a decade: models that perform well retrospectively and then fail to change practice. The award uses a two-phase UG3/UH3 cooperative agreement, with a milestone-gated preparatory phase capped at 450,000 US dollars in direct costs per year followed, on successful transition, by a phase capped at 1,000,000 US dollars in direct costs. Clinical trials are optional, so teams can pursue prospective evaluation or focus on workflow integration, interoperability and clinician-facing deployment without being forced into a trial design. PRIMED-AI deliberately splits the pipeline across five opportunities, and M2C sits downstream of the Data-to-Model academic-industrial partnerships and alongside an independent Validation Center that will evaluate tools the programme produces, so successful applicants should expect their models to be externally scrutinised rather than self-assessed. The RFA was released on 30 June 2026 with applications due 19 October 2026. For academic medical centres with both AI development capability and real clinical implementation reach, this is one of the few federal mechanisms that pays specifically for the translation step rather than treating it as dissemination.

The FY2026 Emerging Health Innovators Initiative, Notice ID ARPA-H-SOL-26-150, is ARPA-H's dedicated funding route for early-career principal investigators and carries two explicitly AI-focused topics among its 16 anticipated topic areas: Causally-Grounded Alignment for Clinical Generative Artificial Intelligence Tools, and Protecting Patient Data in Life Science AI. The first targets the core failure mode of clinical generative AI, which is that models aligned on correlational patterns in clinical text produce fluent recommendations that are not causally grounded in the underlying disease mechanism; the topic asks for alignment approaches anchored in causal structure rather than human preference ratings. The second addresses privacy-preserving computation over life-science datasets, where the tension between model utility and patient re-identification risk currently blocks multi-institution training. The structure is two-stage and the first gate is the binding one: a required Solution Summary is due 30 October 2026 at 2:00 PM ET, and only proposers who clear that stage are positioned for the full proposal deadline of 10 February 2027 at 2:00 PM ET. An optional virtual Proposers' Day was held on 13 October 2026. ARPA-H anticipates 20 or more awards per year at up to 350,000 dollars per year for two years with an optional third year. Submission is through the ARPA-H Solutions portal at solutions.arpa-h.gov after retrieving the solicitation and its attachments from SAM.gov under the notice ID.

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.

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 Advantage Grant is sponsored by Commonwealth of Pennsylvania. This reimbursement grant program provides assistance to small businesses (100 or fewer employees) to undertake energy efficiency, pollution prevention, or natural resource protection projects. While not directly behavioral health AI, a small business in this field might be able to leverage AI to optimize operations related to energy efficiency or pollution prevention within their facilities.

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.

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.

Advancing Fair & Effective AI for Older Adults is sponsored by The SCAN Foundation (in partnership with Coalition for Health AI - CHAI). This initiative, in partnership with the Coalition for Health AI, focuses on laying the foundation for equitable, effective AI solutions for older adults, particularly those in underserved and marginalized communities.

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.

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.

HHS Small Business Innovation Research (SBIR) Program is sponsored by U.S. Department of Health & Human Services (HHS). This program encourages small businesses to engage in federal research and development with potential for commercialization, including healthcare solutions. It covers 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.

Evidence for AI in Health (EVAH) is a 60 million US dollar, three-year initiative launched jointly by Wellcome, the Gates Foundation and the Novo Nordisk Foundation to answer a question that the current wave of health AI funding largely skips: do AI-enabled clinical decision support tools actually improve care when deployed in the settings that need them most. It funds evaluation rather than development. Eligible studies assess AI tools that are already deployed or ready for deployment in primary and community health care in Sub-Saharan Africa, South Asia and South-East Asia, supporting frontline health workers with clinical tasks such as triage, diagnosis and referral. The methods in scope are implementation research, randomised controlled trials, economic evaluation and acceptability studies, and the initiative gives explicit priority to technologies designed for resource-limited settings and trained on data that genuinely reflects the populations they will serve, which rules out much of the evaluation-by-transplantation that characterises global health AI. The request for proposals is administered by the Abdul Latif Jameel Poverty Action Lab (J-PAL), which brings a randomised-evaluation methodological culture to the review process, and proposals are expected to be led by institutions based in the regions of focus rather than by Northern universities with regional partners. The Spring 2026 round closed on 1 April 2026 with decisions anticipated by mid-July 2026; because the initiative runs for three years with rolling requests for proposals, further rounds are expected and prospective applicants should monitor the J-PAL EVAH pages for the next window.

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.

Texas Small Business Digital Growth Program is sponsored by Texas Small Business Agency. This program provides financial support to small businesses in Texas to adopt digital technologies, implement CRM/ERP tools, and optimize e-commerce solutions. While not specifically for AI, a consumer health AI app could fall under adopting digital technologies to enhance business operations.

UNICEF Venture Fund - Digital Public Goods is sponsored by UNICEF Venture Fund. The UNICEF Venture Fund invests in early-stage, for-profit technology startups leveraging frontier technologies like AI, machine learning, and data science to create open-source solutions that improve children's health, nutrition, and mental health globally. The fund prioritizes solutions that strengthen systems in remote areas, improve access to data, skills, and services, and empower young people. While international, it aligns with consumer health AI apps and is open to for-profit entities.

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.

The Oberndorf AI Medical Catalyst Grant funds University of Florida medical students to carry out research applying artificial intelligence to medical problems, with each award set at 5,000 US dollars. It sits within the Oberndorf AI Medical Scholarship Program, endowed by a donation from Lou and Rosemary Oberndorf and run jointly by the UF College of Medicine Office of Research and the Intelligent Clinical Care Center, known as IC3. The programme's design reflects a specific view of what limits clinical AI training: it does not require applicants to have prior AI experience, but it does require that each project have an AI mentor who is a current IC3 member or has membership pending and who brings extensive AI experience. In other words, the money follows the mentorship rather than the credential, which makes the award genuinely accessible to clinically oriented students who have the interest but not yet the technical background. Eligible project scope spans clinical decision-making, diagnostics, research and healthcare management, and past awardees have worked on large language models over electronic health record data, predictive analytics for kidney disease, and AI image segmentation in medical imaging. The most recently published cycle closed on 5 January 2026 with submission through the University of Florida InfoReady platform. No deadline is recorded here because the next cycle's date has not been published. For UF medical students, this is the designated on-ramp into AI research; for everyone else it is a useful model of how an institution can lower the barrier to clinical AI training.

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.

AI and Health Safety Grant is sponsored by Wellcome Trust. The Wellcome Trust funds research on the safe and responsible application of AI in health and biomedical sciences. Grants support projects ensuring that AI-driven health tools are safe, equitable, and clinically reliable, with focus areas including algorithmic bias in medical AI, privacy-preserving health analytics, and robust validation of clinical AI systems. The program bridges AI safety research with real-world healthcare deployment challenges.

Wellcome Discovery Awards for Bold Biomedical and Health AI Research is sponsored by Wellcome Trust (international, but explicitly welcomes AI and computational approaches to biomedical challenges). These awards provide flexible funding for established researchers and teams pursuing bold, creative research that can deliver significant shifts in understanding human life, health, and wellbeing.

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.

Fondation Botnar: Child and Adolescent Health & AI Research Funding is sponsored by Fondation Botnar. Fondation Botnar funds research and implementation work at the intersection of child and adolescent health, artificial intelligence and digital technology, and urban living, with a focus on children and adolescents (10-24 age range) in rapidly urbanizing cities.

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.

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.

Genomic Surveillance for Antimicrobial Resistance in Animals is sponsored by Cornell University College of Veterinary Medicine (funded by NIH and NSF). This program, supported by NIH and NSF, focuses on real-time monitoring of antimicrobial resistance in animals to protect public health. AI and data science approaches are highly relevant for analyzing genomic data and predicting resistance patterns.

EIT Health Call for applications to support high-potential European start-ups developing innovative solutions in biotech, medtech, digital health, AI, and biomarkers or diagnostics is sponsored by EIT Health. This call supports high-potential European micro, small, and medium-sized enterprises (SMEs) developing innovative healthcare solutions in biotech, medtech, digital health, AI, and biomarkers or diagnostics.

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.

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.

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.