NIH Highlighted Topic: Digital Health and Artificial Intelligence Tools for Biomedical and Behavioral Research: Validity and Utility

Karen Cielo | Posted on July 01, 2026

This topic encourages evaluation of the validity and clinical utility of digital health and artificial intelligence (AI) tools and technologies in biomedical and behavioral research. In this context, digital health technologies include computing platforms, software, sensors, mobile and wearable device-based technology, health information technology, connectivity, telehealth, telemedicine, and internet of things when used for healthcare or health-related research. AI tools include integrated and stand-alone computational technologies, risk assessment and prognosis algorithms, and software used for health promotion, disease prevention, and treatment of health conditions.

Rapidly evolving use of digital and AI technologies in research and health care has revealed both promises and perils. These technologies have accelerated scientific breakthroughs in multiple domains, expanded clinical reach, and surpassed human performance on several task-specific benchmarks (e.g., information extraction and image generation). However, their analytical validity, clinical validity, reliability, and/or utility in research and clinical care settings have often not been thoroughly examined. Optimizing dissemination and implementation and ensuring widespread use of these technologies requires more rigorous evaluation across research, community, and clinical settings, as well as in different populations and health contexts.

NIH encourages research projects that evaluate reliability and sensitivity, as well as projects that validate digital health and/or AI tools for use in research and clinical settings.  NIH sees these technologies as important for assessing chronic and mental health conditions and identifying factors that can impact or predict morbidity and mortality. It is critical to determine the utility of using emerging technologies to improve health outcomes across all populations, particularly among those with limited access to traditional healthcare. Technical factors that might affect the use of these tools at different stages of the lifespan should also be assessed. Applicants are strongly encouraged to justify the impact of validating the specific tool or technology in the context or population under study.

Projects should include rigorous risk assessments, data security, and AI governance. Studies proposing secondary analysis should address the sufficiency of existing datasets for rigorous validation. Investigators are strongly encouraged to design projects that will deepen the evidence base for digital health and AI applications and to employ techniques that minimize potential imbalances or biases in data sources. Applications should include plans for risk mitigation that ensure safety, privacy, and effectiveness for all individuals. Before submission, applicants are strongly encouraged to review the specific research interests of participating NIH Institutes and Centers and to direct inquiries to the listed Scientific Contacts.

See more information here, including participating NIH Institutes/Centers and their specific interests relating to the Highlighted Topic.

Apply to a highlighted topic through an appropriate NIH Parent Funding Announcement or another broad NIH opportunity available on Grants.gov. Learn how to interpret and use Highlighted Topics.

Please contact Dr. Karen Cielo, Director of Research Development, if you are interested in applying or would like to discuss the opportunity.