Key Dates

Related Announcements

• January 18, 2024 – Modular R01s in Cancer Control and Population Sciences (R01 Clinical Trial Optional). See NOFO PAR-24-122
• December 20, 2023 – Stephen I. Katz Early Stage Investigator Research Project Grant (Clinical Trial Not Allowed). See NOFO PAR-24-075
• December 15, 2023 – Cancer Prevention and Control Clinical Trials Grant Program (R01 Clinical Trial Required). See NOFO PAR-24-072
• July 12, 2023 – PHS 2023-2 Omnibus Solicitation of the NIH for Small Business Technology Transfer Grant Applications (Parent STTR [R41/R42] Clinical Trial Required). See NOFO PA-23-233
• July 12, 2023 – PHS 2023-2 Omnibus Solicitation of the NIH for Small Business Technology Transfer Grant Applications (Parent STTR [R41/R42] Clinical Trial Required). See NOFO PA-23-232
• July 12, 2023 – PHS 2023-2 Omnibus Solicitation of the NIH for Small Business Technology Transfer Grant Applications (Parent STTR [R43/R44] Clinical Trial Not Allowed). See NOFO PA-23-230
• July 12, 2023 – PHS 2023-2 Omnibus Solicitation of the NIH, CDC and FDA for Small Business Innovation Research Grant Applications (Parent SBIR [R43/R44] Clinical Trial Not Allowed). See NOFO PA-23-231
• April 11, 2023 – Exploratory Clinical Neuroscience Research on Substance Use Disorders (R61/R33 Clinical Trial Optional). See NOFO PAR-23-157
• January 11, 2023 – Innovations to Optimize HIV Prevention and Care Continuum Outcomes (R01 Clinical Trial Optional). See NOFO PAR-23-062
• January 11, 2023 – Innovations to Optimize HIV Prevention and Care Continuum Outcomes (R21 Clinical Trial Optional). See NOFO PAR-23-061
• January 11, 2023 – Formative and Pilot Intervention Research to Optimize HIV Prevention and Care Continuum Outcomes (R34 Clinical Trial Optional). See NOFO PAR-23-060
• September 09, 2022 – NINR Areas of Emphasis for Research to Optimize Health and Advance Health Equity (R01 Clinical Trial Optional). See NOFO PAR-22-230
• September 09, 2022 – NINR Areas of Emphasis for Research to Optimize Health and Advance Health Equity (R21 Clinical Trial Optional). See NOFO PAR-22-231
• September 08, 2022 – Bioengineering Research Grants (BRG) (R01 Clinical Trial Not Allowed). See NOFO PAR-22-242
• September 08, 2022 – Bioengineering Research Grants (BRG) (R01 Clinical Trial Optional). See NOFO PAR-22-243
• June 21, 2022 – Advancing Research on Alzheimer's Disease (AD) and AD-Related Dementias (ADRD) (R41/R42 Clinical Trial Optional). See NOFO PAS-22-197
• June 21, 2022 – Advancing Research on Alzheimer's Disease (AD) and AD-Related Dementias (ADRD) (R43/R44 Clinical Trial Optional). See NOFO PAS-22-196
• January 28, 2022 – Notice of Special Interest (NOSI): Validation of Digital Health and Artificial Intelligence Tools for Improved Assessment in Epidemiological, Clinical, and Intervention Research. See NOFO NOT-CA-22-037 (Reissue) 
• December 22, 2021 – Research on Current Topics in Alzheimer's Disease and Its Related Dementias (R21 Clinical Trial Optional). See NOFO PAR-22-094
• March 02, 2021 – Confirmatory Efficacy Clinical Trials of Non-Pharmacological Interventions for Mental Disorders (R01 Clinical Trial Required). See NOFO PAR-21-132
• March 02, 2021 – Development of Psychosocial Therapeutic and Preventive Interventions for Mental Disorders (R33 Clinical Trial Required). See NOFO PAR-21-134
• March 02, 2021 – Pilot Effectiveness Trials for Treatment, Preventive and Services Interventions (R34 Clinical Trial Required). See NOFO PAR-21-131
• March 02, 2021 – Development of Psychosocial Therapeutic and Preventive Interventions for Mental Disorders (R61/R33 Clinical Trial Required). See NOFO PAR-21-135
• May 07, 2020 – NIH Exploratory/Developmental Research Grant Program (Parent R21 Clinical Trial Required). See NOFO PA-20-194
• May 07, 2020 – NIH Exploratory/Developmental Research Grant Program (Parent R21 Clinical Trial Not Allowed). See NOFO PA-20-195
• May 05, 2020 – NIH Research Project Grant (Parent R01 Clinical Trial Not Allowed). See NOFO PA-20-185
• May 05, 2020 – NIH Research Project Grant (Parent R01 Clinical Trial Required). See NOFO PA-20-183

Issued by

National Cancer Institute (NCI)

National Heart, Lung, and Blood Institute (NHLBI)

National Institute on Aging (NIA)

National Institute on Drug Abuse (NIDA)

National Institute of Mental Health (NIMH)

National Institute of Nursing Research (NINR)

All applications to this funding opportunity announcement should fall within the mission of the Institutes/Centers. The following NIH Offices may co-fund applications assigned to those Institutes/Centers.

Office of Behavioral and Social Sciences Research (OBSSR)

Office of Data Science Strategy (ODSS)

Purpose

The purpose of this Notice of Special Interest (NOSI) is to encourage grant applications to support the evaluation of the utility and validity of digital health and artificial intelligence/machine learning (AI/ML) tools and technologies in biomedical and behavioral research. The intent is to support the addition of new measurement modalities to evaluate existing and recently developed but not yet validated digital health and AI tools such as sensor technologies, smartphone applications, software as a medical device (SaMD), and AI/ML algorithms.

Background

Digital health and AI/ML technologies have grown exponentially in the past two decades. Digital health technologies include mobile devices, health information technology, wearable devices, sensors, telehealth and telemedicine, and internet of things (IoT). AI/ML tools include computational technology integrated within devices or platforms as well as stand-alone AI/ML technologies such as SaMD, risk assessment and prognosis algorithms, and software for informing health care used in the cure, mitigation, treatment, recovery, or prevention of health conditions without being part of a hardware medical device.

The range of health research and practice affected by the technological revolution is quite broad, including use in disease surveillance and public health research as well as for medical screening and diagnostic purposes where they can provide tools that can reach diverse users including individuals living in rural and underserved areas and low- and middle-income countries. However, many of these recently released tools are unregulated and their analytical validity, clinical validity, and/or reliability, utility for research, practice, and clinical care have not been examined. Widespread use and adoption of these technologies requires more rigorous evaluation across research and clinical settings, diverse populations, and health contexts including an understanding of how to optimize the implementation of these tools and technologies. Through this NOSI, NIH strives to capitalize on current research projects that collect outcomes to validate digital health and AI/ML tools.

This is a reissue of NOT-CA-22-037, with modified scientific priorities.

Research Objectives

Research supported by this NOSI is expected to provide support for analytical and/or clinical validation of recently developed digital health and AI/ML technologies. Digital health and AI/ML technologies are defined broadly to include any health technology leveraging mobile health, health information technology, wearable devices, sensors, telehealth and telemedicine, internet of things (IoT), SaMD and/or related AI/ML algorithms and tools to monitor and manage health across the life course. As noted previously, the purpose of this NOSI is not to support the development of new tools or technologies.

Applicants should clearly justify the importance and implications of validating digital health and AI/ML tools and technologies. Studies should apply rigorous research methods to evaluate the analytical and/or clinical validity of any proposed digital health and AI/ML applications including the use of gold-standard comparators. Projects that validate digital health or AI/ML tools for a new context of use, including another disease area or specific populations (e.g., low income, minority group), and research projects that evaluate the reliability of the tool and its sensitivity are encouraged.

Studies proposing secondary analysis should address the sufficiency of existing datasets to validate technologies and deepen the evidence base of digital health and AI/ML applications while also addressing potential bias, including plans for risk mitigation that ensure safety, privacy, and effectiveness among diverse individuals and populations.  For Food and Drug Administration (FDA) regulated devices, activities included under this notice should follow FDA guidance and support appropriate activities leading to a future marketing submission to the FDA.

Investigators must carefully review the specific research interests of NIH Institutes and Centers (ICs) that are participating in this NOSI.

All NOSIs must include the following Application and Submission Information. 

IC Specific Application and Submission Information

NIH ICs have separately advanced notices of funding opportunities (NOFOs) relevant to this NOSI. These NOFOs might be specific to each IC mission area. Applicants must select the IC and associated NOFO to use for submission of an application in response to the NOSI. The selection must align with the IC requirements listed in order to be considered responsive to that NOFO. 

National Cancer Institute (NCI)

The National Cancer Institute is interested in the analytical and clinical validation of digital health tools and AI/ML technologies that are currently or have the potential to be adopted and implemented in real-world settings across the cancer continuum including: cancer risk assessment, screening, early detection and prevention; diagnosis and treatment; cancer control and epidemiology including assessing cancer incidence, prevalence, prognosis, survival, and health disparities. Appropriate topics include, but are not limited to the following:

• Validating digital health technologies and algorithms for measuring cancer-related risk factors across the lifespan. Risk factors include but are not limited to physical activity, sleep, sedentary behavior, weight management, diet/nutrition, sun exposure, cannabis use, and alcohol use.
• Assessing validity and reliability of digital technologies (e.g., sensors, devices, wearables) and AI/ML technologies developed for general purpose but not validated for use in cancer-related contexts and/or in demographic populations impacted by cancer.
• Validating digital health, algorithms, or AI/ML platforms used in cancer screening, risk assessment, early detection, diagnosis, and treatment.
• Validating digital health and/or AI/ML technologies that monitor treatment related adverse events, improvements, and outcomes.
• Validating digital and AI/ML platforms that collect, harmonize and aggregate cancer incidence prevalence, and survival.
• Validating digital and AI/ML platforms used in cohort identification for clinical studies.
• Evaluating and improving AI/ML algorithms to eliminate bias and reduce cancer disparities.
• Validating computational models and AI/ML methods related to measurement error in the assessment and monitoring of cancer risk factors and outcomes.
• Assessing the validity and reliability of biomarkers derived from digital and AI/ML technologies such as for monitoring cancer risk factors, prediction and diagnosis of cancer and early recurrence, and identification and monitoring of symptoms and/or toxicities.
• Validating digital tools, platforms, and technologies to enhance equity and access to cancer care, support clinical decision-making, and evaluate effects on cancer outcomes.

Applicants are strongly encouraged to contact the NCI Scientific Contact (below) well in advance of submitting applications to discuss alignment with NCI priorities and requirements

National Heart, Lung, and Blood Institute (NHLBI)

The NHLBI encourages research that addresses the NHLBI Strategic Vision/Research Priorities and is aligned with these recommended areas for domestic and global HLBS research. Appropriate topics include, but are not limited to following mentioned below:

• Devices and AI/ML algorithms developed for general purposes but not validated for clinical use or in specific disease or demographic populations impacted by HLBS diseases.
• Assessing the validity and reliability of a general-purpose computing technology to diagnose a HLBS condition or recommend treatment using on-board hardware (e.g., the tri-axial accelerometer that simultaneous measurements in three orthogonal directions, for actigraphy or biophysical measurements) or other sensor(s) that operates with embedded processor(s) of a consumer digital device (e.g., a camera, a smartwatch, or a wearable device).
• Evaluation and improvement of AI/ML algorithms, including for the identification and elimination of bias, and on the robustness and resilience of these algorithms to withstand changing clinical inputs and conditions.
• Evaluation of AI/ML algorithms used for cardiovascular image reconstruction and image post-processing.
• Evaluation of AI/ML image pattern recognition tools and for the detection and diagnosis of lung and cardiovascular disease.
• Validation of AI/ML algorithms for computer assisted diagnosis.
• Use of point-of-care testing and smart device remote monitoring and management of sickle cell anemia.
• Validation of computational approaches to monitoring, diagnosing and/or managing HLBS disease
• Assess and validate sleep-wake scoring using machine learning approaches for the detection and diagnosis of sleep disordered breathing, and other sleep disorders, in adult and pediatric populations.
• Use of home monitoring and wearable devices to study the relationship between activity level and sleep health.
• Use of wearables and digital tools to study the relationship between glycemic state, physical activity, and sleep health in patients with Type 1 and Type 2 diabetes.

Testing and validations of interventions must use the NHLBI NOFOs to address the safety, efficacy, and effectiveness of preventive, therapeutic, and services interventions, selecting the one most appropriate for their application based on the stage of development of the intervention. PIs are strongly encouraged to contact NHLBI Scientific/Research staff well in advance of submitting applications to discuss the match to NHLBI priorities and requirements.

National Institute on Aging (NIA)

NIA supports the evaluation of digital health and AI/ML tools and technologies to ensure robustness and reliability of AI/ML methodologies across different datasets with variable resolution, quality of data, and heterogeneous data types. NIA support includes research at: molecular and cellular levels; social, behavioral, psychological, and economic research on processes at the individual and societal levels; and clinical and translational research across the human lifespan. NIA also encourages research that addresses the Alzheimer’s Disease and Related Dementias Milestones (see Milestones 11C and 11D under “Biomarkers & Diagnosis”). NIA seeks to facilitate improvement of digital tools to signal the onset and/or progression of AD/ADRD and to allow remote monitoring of real-time data for clinical research.

Research may emphasize, but is not limited to, the following areas:

• Optimization of digital tools to assess and maintain health across transitional care settings (e.g., home health, hospital, nursing home) to improve continuity of care.
• Refinement of AI/ML technology to increase accuracy of survival prediction for clinical decision-making, hospice referral, and other health-related decisions.
• Utilization of machine learning to harness routinely collected big data (from electronic health records, wearables, etc.) to enhance personalized/precision quality of life assessment and symptom management.
• Enhancement of virtual reality as an education, training, and decision-making support tool to improve palliative and end-of-life care.
• Optimization and validation of digital technologies (e.g., accelerometer, digital pen) for collection of data on biometrics, sleep, physical activity, emotion or social activity, cognitive functions in population/epidemiological studies
• Development and validation of non-invasive digital technologies for real time measurement of caloric intake, energy expenditure as clinical endpoints and metabolite profiles as biomarkers in clinical studies
• Evaluations and validations of the clinical effectiveness of digital technologies for management of persons with multiple chronic conditions, particularly for self-management, in care coordination, and when applied in algorithms to support clinical decision-making.
• Development and/or validation of digital technology or AI/ML methods for stratifying all individuals at-risk for AD/ADRD across diverse populations.
• Validate digital neuro-markers in MCI and people with dementia to establish sensitivity and specificity and their ability to transmit a detectable signal.
• Assessing the validity, reliability (rigor and reproducibility) and effectiveness of digital health and AI/ML technologies on data substantiating molecular, cellular, and physiological mechanisms that regulate aging.
• Improving the robustness and objectivity of digital health or AI/ML systems that focus on the identification and specificity of aging and longevity biomarkers.
• Thoroughly test digital health technologies and their associated AI/ML tools in pre-clinical animal studies and patient-oriented, phase I or phase II trials.
• Assessing the validity, reliability, and effectiveness of collecting data using smartphones and mobile devices and other sensors in order to reduce respondent burden, with a focus on needs of older adults.
• Developing and improving digital and AI/ML tools to measure social and environmental determinants of health in real time (e.g., geocoding, air quality measures)
• Determining the utility and validity of digital health or AI/ML tools for use in longitudinal and cross-sectional studies (and inter-applicability of cross-sectional and longitudinal data) to measure dynamically changing parameters in big and/or heterogeneous types of datasets.

National Institute on Drug Abuse (NIDA)

NIDA encourages research that addresses the institute’s Priority Focus Areas and is specifically interested in the validation of digital health tools and AI/ML technologies that can be used for prevention, diagnosis, and treatment of drug use and its health and social consequences across the spectrum, from occasional use to problematic use and substance use disorders (SUDs). NIDA SBIR/STTR applications will be accepted if market need is clearly defined, and the proposed product has potential for commercialization. Applicants are strongly encouraged to contact NIDA Program staff well in advance of submitting applications to discuss the match to NIDA priorities and requirements.

Some examples of areas of interest include:

• Using technology to inform our understanding of both behavioral and neurobiological components of drug use that are strongly influenced by diverse environmental and social factors.
• The development and validation of technologies to help individuals gather, manage, and use data and information related to drug use and their personal health.
• Diagnostic/monitoring tools and technology platforms to optimize drug use interventions.
• The validation of commercialized digital health tools in both research and real-world settings that either have an already developed functional prototype for SUD indication or have been previously developed for other indications and might be repurposed for the SUD population (a strong rationale supporting feasibility and commercialization potential in the SUD field should be provided, as well as a validation plan appropriate to the SUD-specific indication)
• The validation of activities for AI/ML as they relate to drug use and consequences across the spectrum.

National Institute of Mental Health (NIMH)

NIMH encourages research that addresses institute priorities and is aligned with these recommended areas for domestic, global, and AIDS-related mental health research. Research may include, but is not limited to, the following areas of interest:

• Research validating digital health and AI/ML tools and technologies to increase our understanding of the role of social, economic, and structural factors (e.g., structural racism and discrimination, health care access) on the uptake of HIV testing, preexposure prophylaxis (PrEP) and other biomedical HIV prevention strategies among individuals from high-incidence populations.
• Evaluation and improvement of digital health and AI tools to measure social, community, and environmental factors on HIV prevention and treatment in real time (e.g., geospatial methods).
• Validating digital health tools and AI/ML algorithms, including the identification, mitigation, and elimination of biases, used to optimize PrEP access, uptake, and retention in hard-to-reach populations.
• Validating digital health technologies and AI/ML applications used to assess, monitor, and support antiretroviral adherence and treatment that considers privacy, confidentiality, and equitable access for people with HIV.
• Research to optimize the implementation of validated digital health tools and technologies to support HIV prevention, treatment, and care needs in domestic and global settings.
• Validating and refining AI/ML algorithms and tools to promote the use of mental health services in low- and middle-income countries (LMICs).
• Assessing the validity of digital technologies to improve the availability and accessibility of mental health services in LMICs.
• Testing and validating AI/ML algorithms and tools to improve the cost-effectiveness and utilization of mental health services in LMICs.
• Validating digital technologies and AI/ML in supporting mental health and well-being among vulnerable populations such as migrants and individuals in natural or human-caused crises.
• Validating digital technologies and AI/ML in supporting the scalability and sustainability of mental health care in LMICs (such as in supporting non-specialist healthcare providers for task shifting and scaling up mental health care).
• Assessing the implementation of validated digital technologies and AL/ML tools for preventing mental illnesses and enhancing or promoting well-being in LMICs.

NIMH has established additional trial design requirements for testing and validating interventions. Applicants are strongly encouraged to contact NIMH Scientific Contact (below) well in advance of submitting applications to discuss the match to NIMH priorities and requirements (see NOT-MH-18-031).

National Institute of Nursing Research (NINR)

The National Institute of Nursing Research (NINR) supports research aligned with our mission and strategic priorities, conducted by scientists from any discipline. NINR discovers solutions to health challenges through the lenses of health equity, social determinants of health, population and community health, prevention and health promotion, and systems and models of care. Drawing on the strengths of nursing’s holistic, contextualized perspective, core values, and broad reach, NINR funds multilevel and cross-sectoral research that examines the factors that impact health across the many settings in which nurses practice, including homes, schools, workplaces, clinics, justice settings, and the community. 

Office of Data Science Strategy (ODSS)

ODSS encourages applications that advance capabilities in AI assessment for biomedicine by, for example, sharing the results of validation testing, attributes and provenance for the AI system or device, and any limitations or recommendations for use. ODSS is particularly interested in foundational assessments and transparent documentation that take into account risks of widening health disparities, identifying foreseeable harms, misunderstandings, and technical and sociotechnical limitations.

Application and Submission Information

Applicants must select the IC and associated NOFO to use for submission of an application in response to the NOSI. The selection must align with the IC requirements listed in order to be considered responsive to that NOFO. Non-responsive applications will be withdrawn from consideration for this initiative. In addition, applicants using NIH Parent announcements will be assigned to those ICs on this NOSI that have indicated those NOFOs are acceptable and based on usual application-IC assignment practices.

This notice applies to due dates on or after April 05, 2024, and subsequent receipt dates through July 06, 2025. This NOSI expires on July 06, 2025.

Investigators are strongly encouraged to reach out to the relevant contacts listed in the Inquiries section of this NOSI to determine whether the NOFO and funding mechanism selected are appropriate for the proposed research. Participating NIH Offices may consider co-funding meritorious applications depending on the alignment with office-specific missions and priorities and the availability of funds.

The following ICs accept applications to the NOFOs below or their subsequent reissued equivalents:

All instructions in the SF424 (R&R) Application Guide and the listed funding opportunity announcements must be followed, with the following additions:

• For funding consideration, applicants must include “NOT-CA-24-031” (without quotation marks) in the Agency Routing Identifier field (box 4B) of the SF424 R&R form. Applications without this information in box 4B will not be considered for this initiative.

Applications nonresponsive to terms of this NOSI will be withdrawn from consideration for this initiative.

Inquiries

Please direct all inquiries to the contacts in Section VII of the listed notice of funding opportunity with the following additions/substitutions:

Dana Wolff-Hughes, PhD
National Cancer Institute (NCI)
Telephone: 240-620-0673
Email: dana.wolff@nih.gov

John Haller, PhD
National Heart, Lung, and Blood Institute (NHLBI)
Telephone: 301-827-7702
Email: john.haller@nih.gov

Nalini Raghavachari, PhD
National Institute on Aging (NIA)
Telephone: 301-496-6942
Email: nalini.raghavachari@nih.gov

Susan Wright, PhD
National Institute on Drug Abuse (NIDA)
Telephone: 301-402-6683
Email: susan.wright@nih.gov

Mary Elizabeth (Libbey) Bowen, PhD
National Institute of Nursing Research (NINR)
Telephone: 301-841-5345
Email: libbey.bowen@nih.gov

Lori A. J. Scott-Sheldon, Ph.D.
National Institute of Mental Health (NIMH)
Telephone: 301-792-2309
Email: lori.scott-sheldon@nih.gov