Key Dates

Related Announcements

• August 28, 2023 - Notice of Pre-Application Webinar for NIH National Science Foundation (NSF) Initiative: Smart Health and Biomedical Research in the Era of Artificial Intelligence and Advanced Data Science. See Notice NOT-OD-23-167.
• August 14, 2023 - Smart Health and Biomedical Research in the Era of Artificial Intelligence and Advanced Data Science (SCH). See NSF Program Announcements & Information NSF 23-614.
• November 24, 2020 - Notice of NIH Participation in Smart Health and Biomedical Research in the Era of Artificial Intelligence and Advanced Data Science. See Notice NOT-OD-21-011.

Issued by

Office of Data Science Strategy (ODSS)

Office of AIDS Research (OAR)

National Eye Institute (NEI)

National Heart, Lung, and Blood Institute (NHLBI)

National Institute on Aging (NIA)

National Institute of Allergy and Infectious Diseases (NIAID)

National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS)

National Institute of Biomedical Imaging and Bioengineering (NIBIB)

Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)

National Institute on Deafness and Other Communication Disorders (NIDCD)

National Institute of Dental and Craniofacial Research (NIDCR)

National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)

National Institute on Drug Abuse (NIDA)

National Institute of Environmental Health Sciences (NIEHS)

National Institute of Mental Health (NIMH)

National Institute of Neurological Disorders and Stroke (NINDS)

National Institute of Nursing Research (NINR)

National Institute on Minority Health and Health Disparities (NIMHD)

National Library of Medicine (NLM)

National Center for Complementary and Integrative Health (NCCIH)

National Center for Advancing Translational Sciences (NCATS)

National Cancer Institute (NCI)

Office of The Director, National Institutes of Health (OD)

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.

Division of Program Coordination, Planning and Strategic Initiatives, Office of Disease Prevention (ODP)

Office of Behavioral and Social Sciences Research (OBSSR)

Office of Research on Women's Health (ORWH)

Office of Data Science Strategy (ODSS)

Office of Nutrition Research (ONR)

Sexual and Gender Minority Research Office (SGMRO)

NIH BRAIN Initiative (https://braininitiative.nih.gov/)

Purpose

The purpose of this Notice is to announce the collaboration between the NIH and the National Science Foundation (NSF) on an interagency funding opportunity, NSF-23-614, Smart Health and Biomedical Research in the Era of Artificial Intelligence and Advanced Data Science. The Smart Health program supports innovative, high-risk/high-reward research with the promise of disruptive transformations in biomedical and public health research. Proposals submitted must make fundamental contributions to two or more disciplines, such as computer or information sciences, engineering, social, behavioral, biomedical, cognitive and/or economic sciences, to improve the fundamental understanding of biomedical and health related processes and address a key health problem. Traditional disease-centric medical, clinical, pharmacological, biological, or physiological studies and evaluations are outside the scope of this solicitation. In addition, fundamental biological research with humans that also does not advance other fundamental science or engineering areas is out of scope for this program.

The solicitation aims to address technological and data science challenges that require fundamental research and development of new tools, workflows, and methods across many dimensions including, but not limited to:

• Fairness and Trustworthiness
• Transformative Analytics in Biomedical and Behavioral Research
• Next Generation Multimodal and Reconfigurable Sensing Systems
• Cyber-Physical Systems
• Robotics
• Biomedical Image Interpretation
• Unpacking Health Disparities and Health Equity

The general interests of the participating NIH Institutes, Centers, and Offices are outlined below:

National Cancer Institute (NCI)

NCI welcomes applications aligned with the NCI mission and scientific priorities and centered on the development of smart health technologies, tools, and analytic approaches to improve outcomes across the cancer continuum (i.e., cancer etiology and risk factors, detection and prevention, diagnosis and treatment, survivorship ,and cancer control and epidemiology). NCI is particularly interested in projects aiming to:

Develop digital health tools, technologies, and platforms that:

• Address unmet needs of high-risk, understudied, and/or underserved cancer populations through tools to enhance equity and access to cancer care. Cancer populations of interest include individuals with rare cancers, pediatric cancer survivors, older cancer patients with comorbidities, and rural patient communities
• Assess cancer risk factors and/or improve outcomes including physical activity, UV radiation/sun exposure, cannabis use, tobacco and alcohol use, sleep, and circadian disruption) and social determinants of health (e.g., built environment, social context and connectedness)
• Provide continuous personalized monitoring of physiological status and/or inform treatment selection, optimized dosing, and adaptive designs. This may include technologies for monitoring social-emotional well-being, environmental contexts and exposures, and behavioral patterns to identify and predict cancer- and treatment-related symptoms, sequalae, and/or outcomes (e.g., pain, cognitive impairment, and adverse events)
• Support clinical decision-making to enable earlier clinical interventions or self-management protocols

Leverage novel artificial intelligence, deep learning, and other data science tools supporting:

• Implementation and adoption of digital health technologies in oncology
• Improved data curation, integration, and meaningful clinical interpretation of cancer-related data
• Mitigation of algorithmic bias and reduction of cancer disparities
• Identification, development, and evaluation of digital phenotypes for improved cancer care and outcomes

Optimize engagement and performance of cancer survivors, caregivers, clinicians, and healthcare providers through innovative technologies and resources that:

• Enhance patient-provider communication and care coordination
• Facilitate accessible, equitable, and cost-effective decentralized clinical trials
• Assess and monitor participant engagement
• Improve surveillance, reporting, and management of symptoms, adverse events, and late effects of cancer and its treatments

Conduct human visual cognition studies to:

• Inform the development and deployment of augmented reality, virtual reality, and/or 3D printing in surgical interventions
• Develop methods to mitigate or reduce inter- and intra-observer variability in cancer image interpretation, particularly for modalities with relatively high levels of variability (e.g., ultrasound)
• Develop new methods to study human performance in large digital image sets (e.g., whole slide imaging, 3D CT/MRI)

National Eye Institute (NEI)

NEI is interested in applications that aim to stimulate the development of smart health technologies, tools, methods, and artificial intelligence models to leverage data science to facilitate transformative research for improving vision health and understanding of the visual system. Specific areas of research interest include, but are not limited to:?

• Develop new and innovative analytical and software platforms to improve the ability of researchers to process complex samples and data types to facilitate data analysis, interpretation, and integration
• Advance uniform standards for data collection to improve data interoperability, integration, curation, and analysis, and to enhance data sharing and reproducibility
• Develop new method(s) for machine learning or deep learning to improve cross-modality data analysis (e.g., imaging, electronic health records, functional testing, and multi-omics), or to explore ways to train models using federated learning
• Develop innovative data management and sharing platforms to support data sharing and data harmonization across visual science
• Develop digital health tools and technologies to assist people living with blindness or low vision, to advance their independence and improve quality of life

National Heart, Lung, and Blood Institute (NHLBI)

Blood

• Multiscale modeling of blood flow, blood cell damage, sickle cell anemia and thrombosis
• Statistical image analysis tools for non-invasive monitoring of anemia
• Biomechanics of bleeding
• 3D models of the blood-brain barrier
• Deep learning tools for the automated analysis of hematopathology
• Prognostic algorithms for hematopoietic stem cell transplant recipients
• Point of care ultrasound detection for postoperative vascular thrombosis
• Portable non-invasive blood testing device for hemoglobin monitoring
• Wearable sensors for chronic monitoring of venous thromboembolism

Lung and Sleep

Creation of AI/ML-based federated databases that show:

• Lung Disease of Prematurity
• Down Syndrome demographics across the Life Span
• Characterization of rare disease genotypes and phenotypes (e.g., Sarcoidosis, Idiopathic pulmonary fibrosis, Lymphangioleiomyomatosis)
• Longitudinal lung structure and function with growth in height and development with pulmonary function testing to obtain indices of pulmonary health
• Development of AI/ML-based technology/solutions in managing and regulation of treatment strategies for pulmonary (see Learn More Breathe Better) and sleep diseases or disorders
• AI/ML- feedback-based regulation in supplemental gas delivery (e.g., O2, NO) for potential target of 1.5 million+ people who are on supplemental oxygen for a variety of pulmonary indications
• Monitoring biophysical readings to help diagnose and detect signs of exacerbations
• Automated/deep learning tools for phenotyping pulmonary diseases (e.g., CT, MRI) based on existing NHLBI repositories (e.g., TOPMED, BioData Catalyst, Spiromics, COPDgene)
• Automated/deep learning tools for phenotyping pulmonary diseases and sleep and circadian disorders (e.g., CT, MRI, EEG) based on existing NHLBI repositories (e.g., TOPMED, BioData Catalyst, COPDgene, National Sleep Research Resource (NSRR))

Cardiovascular Disease and Trans-NHLBI

Develop AI/ML based tools/solutions for:

• Clinical decision support (e.g., software, telehealth, mobile app, digital twin) for assessing and optimizing HLBS diseases or disorders, as well as to mitigate bias in diagnosis
• Risk assessment, EHR, patient stratification, precision medicine, and targeted treatment including those for HLBS conditions disproportionately affecting women
• Human-AI collaborations to improve accuracy and adherence to treatments
• Continuous learning and improvement of personalized interventions at individual and community levels
• Building partnerships between a patient and a medical team
• Precision prevention and resilience building, especially for chronic disease/conditions management and treatment such as hypertension and obesity
• Identification, phenotyping, subtyping, and stratification of patients at a greater risk of maternal mortality and morbidity (MMM); development of multi-level interventions to address racial disparities in MMM; and clinical decision-making that considers social and cultural biases in women health
• Multi-omics analysis, system biology, image processing, and cardiac imaging analysis.

Develop solutions that engage individuals and communities in testing, validation, and feedback on improving the existing NHLBI data repositories and tools

Develop new data science methods and workflows for building robust and interpretable predictive models to address problems with critical societal impact, such as disease heterogeneity, disease prevention, resilience, drug response, clinical decision support, and behavioral and social economic inequality

Develop tools that engage individuals from underserved communities with low computational expertise in providing input into formulating research questions

Develop multimodal and reconfigurable sensing systems to support whole person women health research, risk identification, maternal digital twins, and clinical decision making

National Institute on Aging (NIA)

NIA is interested in funding research centered on the use of big data analytics, smart and connected health technologies, and methods to facilitate efficient and effective collection, analysis, and interpretation of health information to improve age-related outcomes, understand the mechanisms of aging, decrease health disparities, and improve care delivery for older adults. NIA is particularly interested in the development of new technologies to address various research gaps in Alzheimer’s Disease and related dementias. Advances in technology, analytics, and modeling are needed to enhance outcomes across the continuum of care (monitoring, prevention, early detection of cognitive decline and other age-related disease, treatment trajectory, and utilizing data to improve end-of-life care), and caregivers. Illustrative topic areas in the context of aging include:

• Collection of behavioral and biological digital biomarkers from sensing technologies and the application of AI on these markers to enhance preclinical and clinical trials, and cohort studies to monitor age-related functional changes, cognitive decline, and disease progression
• Technology development for imaging modalities and visualization tools as applied to the aging process and the hallmarks of aging
• Data science, AI, imaging, and systems biology approaches that may or may not be integrated with data processing platforms to obtain and analyze molecular and cellular data from human or animal studies to identify novel biological mechanisms of aging and their interactions during the lifespan
• Apply big data science approaches to develop clinical decision support tools that help physicians caring for older adult patients
• Tools, AI, and human-computer interaction (e.g., socially assistive robots) for aging, gerontology, and in-place self-management of multiple conditions including cognitive decline, and to improve healthcare delivery, decision making and/or assistance and care coordination for individuals with dementia and their caregivers
• Methods for assessing and monitoring financial activity, including evaluating scam awareness among older adults experiencing cognitive decline
• Development of individual or integrated AI approaches utilizing cognitive instruments, digital biomarkers, and electronic health records for the early detection of cognitive decline or monitoring of outcomes at point-of-care
• Development of applications that incorporate AI approaches and behavioral economics principles to assist care providers, caregivers, or older individuals
• The application of Large Language Models to develop solutions for problems associated with the aging process

All applicants, regardless of area of focus, need to clearly articulate how the application of AI and technology will mitigate health disparities (i.e., training databases should be adequately powered across race/ethnicity/gender), this can include improving access and delivery of care in rural areas. NIA welcomes applications aligned with NIA’s strategic mission. Applications addressing topics related to Alzheimer’s disease and Alzheimer’s disease related dementias (AD/ADRD) are strongly encouraged.

National Institute of Allergy and Infectious Diseases (NIAID)

The NIAID mission is to conduct and support basic and applied research to better understand, treat, and ultimately prevent infectious, immunologic, and allergic diseases. NIAID is interested in applications proposing research in the following priority areas:

Use of mobile technologies, including personal mobile devices to:

• To detect, diagnose, and track infectious diseases and immune-mediated disorders, including potential exposures to epidemic and pandemic pathogens
• To measure, and improve adherence to treatment, such as antibiotic treatment and combination antiviral and anti-TB treatments, to avoid resistance and improve clinical outcomes
• To enrich electronic medical records with personal health information related to infectious- and immune-mediated diseases

Use of electronic medical records, social media data, and/or other real-world data to detect and predict:

• Occurrence of infectious- and immune-mediated disease in the population
• Emerging epidemics, pandemics, and allergic diseases
• Adverse events, adherence, sentiment and potential misinformation about vaccines, therapeutics, and non-pharmaceutical interventions against infectious- and immune-mediated diseases, including epidemic pathogens and pathogens of pandemic potential

Curate social media and other real-world data into testbeds and gold standard data to train machine-learning algorithms that can enable improvements in detection, treatment, and prevention of infectious- and immune-mediated diseases.

Rapidly curating and publishing new knowledge and information related to emerging epidemics and pandemics, in machine-actionable format that can be widely accessed by the biomedical community.

Develop new privacy-preserving methods or applications, such as block-chain based methods, to enable using social media and other real-world data for computational research, while protecting privacy, security, and addressing societal and ethical concerns, such as equity and unconscious biases.

National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS)

NIAMS is interested in applications proposing to develop technologies, analytics, and models that will advance research into the causes, treatment, prevention, and socio-behavioral aspects of arthritis, musculoskeletal, and skin diseases. Additionally, NIAMS is interested in similar applications aimed at enhancing basic research on the normal structure and function of bones, joints, muscles, and skin. NIAMS will not support clinical trials through this initiative.

National Institute of Biomedical Imaging and Bioengineering (NIBIB)

The mission of NIBIB is to improve health by leading the development and accelerating the application of biomedical technologies. NIBIB has broad interests in the development of biomedical technologies to improve human health and address health disparities. Program areas of particular relevance include: data science and health information technologies, telehealth, mHealth, point-of-care technologies, medical image interpretation or perception, medical data/image visualization, artificial intelligence, machine learning and deep learning, clinical decision support system, rehabilitation engineering, robotics, next generation predictive models, and integrating the physical and engineering sciences with the life sciences to advance basic research and medical care. The Institute is interested in the development of novel technologies and in advances that enable effective utilization of new or existing technologies.

Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)

NICHD encourages technologies, analytics and models that have the potential to transform health and medicine to promote healthy pregnancies, healthy children, and healthy and optimal lives. NICHD supports biological, behavioral, and clinical research related to conception and pregnancy, normal and abnormal development in childhood, reproductive health, population dynamics across the lifespan, and people with intellectual, developmental, and physical disabilities. NICHD seeks applications that align with the NICHD Strategic Plan themes of 1. understanding the molecular, cellular, and structural basis of development, 2. promoting gynecologic, andrologic, and reproductive health, 3. setting the foundation for health pregnancies and lifelong wellness, 4. improving child and adolescent health and the transition to adulthood, and 5. advancing safe and effective therapeutics and devices for pregnant and lactating women, children, and people with disabilities.

Areas of interest to NICHD include, but are not limited to:

• Utilization of digital technologies, electronic health records, and large-scale datasets to measure exposure responses to therapy and devices and the linkage of mother-infant or familial records
• Modeling healthy, disease, and injury states and simulating patient response to therapy while accounting for individual and environmental heterogeneity
• Integration of genomic, nutritional, social, and behavioral exposure data and modeling approaches to support the use of pharmacotherapies in pregnant and lactating women, children, and people with intellectual and physical disabilities
• Understanding how technology exposure and media use affect developmental trajectories, health outcomes, and parent-child interactions
• Development of safe and effective therapeutics and devices (assistive and medical) used by pregnant and lactating women, infants, children, adolescents, and people with intellectual, developmental, and physical disabilities
• Platforms that include real-time use of disease registries, including registries that track infectious diseases such as HIV/AIDS and intellectual and developmental disabilities such as Autism, to inform surveillance or predictions of outcomes across the lifespan
• Modeling the health effects of climate change on NICHD priority populations, including vector-borne and infectious diseases, nutrition, and population dynamics such as fertility and morbidity and mortality
• Design of digital technologies or platforms to reduce health disparities or inequities and bridge access to quality care in NICHD priority populations living in diverse settings, including rural areas across the life course

National Institute on Drug Abuse (NIDA)

NIDA is specifically interested in applications that support scientific research on drug use and its health and social consequences across the spectrum, from occasional use to problematic use and drug use disorders (SUDs). Some examples of areas of interest include:

• Using technology and advanced statistical methods to inform our understanding of any aspect of behavioral and neurobiological components of drug use that are strongly influenced by diverse biological, environmental, and social factors
• The development and validation of technologies, analytics, and models to help individuals gather, manage, and use data and information related to drug use and their personal health
• Methods and algorithms for aggregation of data including but not limited to electronic health records (EHRs), laboratory generated data, environmental, and/or behavioral data
• Diagnostic/monitoring tools and technology platforms to optimize drug use prevention and interventions
• Development of new statistical methods, algorithms, and tools to integrate diverse data types to better understand biology at the cellular, tissue, organism, and/or population level
• Testing the effectiveness and scalability of approaches to improve equitable access to and retention in prevention and treatment programs for drug use disorders, such as with provider and patient-centered decision supports informed by AI-generated risk prediction or adaptive intervention methods
• Developing methods to address algorithmic bias, transparency, sustainability, and transportability in clinical risk prediction modeling to improve identification, prevention, and treatment for drug use disorders, as well as reducing resultant health inequities
• Employing innovative big data analytics and data linkage to explain heterogeneity in drug use disorder treatment outcomes, including long-term recovery trajectories
• Using innovative technology and advanced computational methods to facilitate the rapid translation of epidemiological findings to drug use and addiction prevention interventions
• Using advanced modeling, simulation, and data analytics to track and predict risk and protective factors to improve prevention and treatment interventions

National Institute on Deafness and Other Communication Disorders (NIDCD)

NIDCD encourages studies that improve the diagnosis, treatment, and management of disorders of hearing, balance, taste, smell, voice, speech, and language. For more information about areas of interest, please visit our home page at http://www.nidcd.nih.gov/ and the NIDCD Strategic Plan website (https://www.nidcd.nih.gov/about/strategic-plans). Potential applicants are encouraged to contact the program staff noted below early in the process of preparing the application.

National Institute of Dental and Craniofacial Research (NIDCR)

NIDCR is interested in supporting innovative, multi-disciplinary, and transformative research to improve dental, oral, and craniofacial (DOC) health and to address oral health disparities and inequities. Successful applications should be guided by the TRUST and FAIR principles. Examples of areas of interests include, but are not limited to, the following:

• Development and implementation of novel and cost-effective devices for the collection of DOC phenotypes and health information at point of care or at home to inform clinical decision-making
• Development and implementation of algorithms, tools, and methods for the mining, harmonization, integration, and analyses of diverse and multidimensional DOC data, including electronic dental and medical records, to facilitate data-intensive translational research. Applicants are encouraged to find ways to identify and mitigate bias in data analyses

National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)

NIDDK encourages innovative and integrated research on conditions relevant to NIDDK’s mission areas, such as diabetes and other endocrine and metabolic diseases; digestive diseases, nutritional disorders, and obesity; and kidney, urologic, and hematologic diseases; to improve people’s health, quality of life, and to reduce disparities. Examples of projects of interest may include, but are not limited to, those that develop technology, analytics, and tools in the following areas:

• Integration of data repositories, biobanks, and Electronic Health Records (EHR) in support of precision medicine to better characterize diversity and heterogeneity in patient populations, endotypes in diseases, and to assist diagnosis, design of treatment and prevention protocols, risk assessments and prevention of complications
• Integration of clinical decision support technologies, such as automated medical image interpretation, into clinical workflows; or integration of multiple data types, such as image, omics, and social/contextual data types, using new algorithmic, statistical, and mathematical approaches in support of data-driven discovery
• Sensors, connected delivery systems (such as closed loop systems), monitoring devices and smart phone applications to enhance patient care, and patient data collection for research and care, including the collection of measures of biological, physiological, and perceived health status; health-related behaviors (e.g., diet, physical activity, smoking, health care seeking behaviors, adherence to prescribed medical treatments); and social determinants of health
• Utilization and integration of data from connected devices, handheld and wearable devices, and social media apps to enhance patient-centered care, patient participation and self-management, and to improve medical and behavioral interventions, including in the situation of virtual care and telemedicine
• Integration of systems to monitor, prevent, and/or reduce disparities originating from or exacerbated by technology design, selection of training datasets, and information dissemination, and to effectively communicate social and medical information across the clinical, community, and home environments where individuals seek care and services

National Institute of Environmental Health Sciences (NIEHS)

NIEHS's mission is to discover how the environment affects people, to promote healthier lives. NIEHS supports research aimed at discovering and explaining how factors, including chemical, physical, and synthetic agents; social stressors; climate-related events; and our own microbiomes, among others, affect biological systems. Data types relevant to Environmental Health Sciences (EHS) research include high-dimensional data (e.g., genomic, metabolomic, exposomic), demographic, questionnaire, qualitative, electronic health record, imaging, geospatial, and toxicological data, as well as exposure data from water, soil, sediment, air, and from wearable/personal exposure monitors. Within the goals of this funding opportunity, NIEHS is interested in projects that develop and/or apply artificial intelligence and advanced data science tools or methods to topics that align with the NIEHS Strategic Plan (see https://www.niehs.nih.gov/about/strategicplan/), including the exposome, precision environmental health, co-exposures, predictive toxicology, individual susceptibility to exposures, and environmental health disparities. Specific interests include, but are not limited to the following areas:

• Advancing fairness and trustworthiness of environmental health-related datasets, models, and artificial intelligence and machine learning (AI/ML) systems
• Developing and applying AI/ML, natural language technologies (NLT), information science, or related computational approaches to better understand the impact of environmental exposures on health outcomes at all stages across the lifespan
• Designing next generation multimodal sensing systems to improve environmental exposure surveillance, generate predictive and personalized models of environmental exposures, and support precision environmental health goals for individualized risk assessment and interventions to prevent disease
• Promoting interoperability, aggregation, and harmonization of multi-level and multi-scale environmental health data to enable research and public health applications (e.g., integrating spatiotemporal data from climate, satellite, or air monitoring sources with disease surveillance, population health, or other molecular or clinical outcome data)
• Developing data-driven AI/ML or mathematical models to measure, reduce, and mitigate environmental determinants of health and environmental health disparities

National Institute of Mental Health (NIMH)

NIMH is interested in deep phenotyping through the development of technologies to capture and analyze fine-grained, multimodal data from individuals with mental disorders and healthy controls, for the purpose of identifying novel biological and behavioral patterns that can (1) add to our understanding of specific mental health constructs and domains of function; (2) reveal causal links between environmental factors and mental functions; (3) uncover developmental trajectories; (4) better predict outcomes; and (5) improve the specificity and timeliness of clinical interventions. Technologies of interest to NIMH include, but are not limited to:

• Sensors tailored to infer subjective mental states (e.g., mood, thought process, risk of self-harm, abnormal perceptions) from objectively observable behaviors (e.g., speech, movement, social interactions). This includes the ability to identify subgroups of patients with psychiatric disorders objectively, to enable more targeted treatments to be implemented in those subgroups
• Sensors adapted to monitor mental health related outcomes across the lifespan, in special populations, and within diverse settings (e.g., young children, geriatric populations, nonverbal individuals, assisted living environments)
• Platforms for the delivery of nonpharmacological interventions (e.g., cognitive behavioral, psychosocial, stimulation-based) in real-world settings
• Sensors to measure outcomes of mental health interventions, including demonstrations of sensitivity to change and correspondence to conventional clinical assessments
• Technology allowing simultaneous, temporally synchronized neurophysiology measurements and quantification of behavior, with high spatial and temporal precision, using either invasive or noninvasive methods, toward the long-term goal of closing the loop between real-time behavioral measurements and delivery of targeted interventions in real-world settings
• Technologies targeting improvements in mental health care delivery systems, including:
• Development and testing of methods for fusion and analysis of personal, behavioral, social, contextual, environmental, and organizational data to support development of innovative predictive models for simulation especially for low base-rate events/conditions that are difficult to identify, treat, and/or manage (e.g., suicide)
• Application of big data analytics and/or algorithm development to EHRs to inform real-time clinical decision making and measurement-based care associated with the delivery of mental health services
• Test approaches to increase engagement in individuals to effectively participate in their own mental health treatment, such as personalized information systems, accessing and visualizing health data and knowledge
• Develop and test strategies to increase adherence, utilization and sustainability for evidence based digital mental interventions/technologies
• Technology platforms that include real-time use of disease registries, measurement-based care, feedback systems, and quality improvement processes as part of a continuously learning healthcare system
• Research to improve designs, measures, and statistical approaches to support testing of system improvement efforts, including information and communication technologies
• Technology platforms which can be utilized across a range of systems (e.g., primary care, schools, criminal justice system, child welfare agencies) to optimize the delivery of effective mental health interventions
• Development of innovative technologies to facilitate adoption, implementation, sustainability, and scalability of best practices, or conversely, technologies to de-implement low value mental health services

National Institute on Minority Health and Health Disparities (NIMHD)

NIMHD’s mission is to lead scientific research to improve minority health and reduce health disparities. The Social Determinants of Health (SDOH) include individual and structural factors, such as demographics, education, access to care, economic stability, social and community context, which have a significant impact on health outcomes and healthcare quality and costs for underserved populations. NIMHD is interested in the development and evaluation of innovative technologies, analytics, statistical methods and models that:

• Standardize the collection, enable the communication, and facilitate the integration of real time actionable SDOH data into the existing workflows of clinicians and community-based care providers, to enable decision support, foster resource and information sharing, and improve patient care and clinical care coordination for underserved populations, making it easier to connect individual patients with resources that can address their social needs
• Facilitate the analysis and integration of spatially linked SDOH information with community and population health data, to inform the design of effective multi-level interventions aimed at reducing and encouraging elimination of disparities in health outcomes and quality of care
• Foster recruitment and enrollment for statistically valid representation of racial/ethnic minority and other populations with health disparities in clinical research studies and especially in therapeutic clinical trials and disease states for which disparities are observed
• Dissect potential racial and ethnic, socioeconomic status, privilege, and disability biases that may be introduced into prediction algorithms and machine learning based clinical decision support tools to prevent further discrimination and to foster appropriate care for all population groups

National Institute of Neurological Disorders and Stroke (NINDS)

Within the goals of this funding opportunity, NINDS is particularly interested in research that advances technologies and systems with the potential to decrease the burden of neurological disorders and stroke. Examples of areas of interest include:

• Collaborative projects that bring data science approaches to neurobiological problems to better understand fundamental neural mechanisms, circuits, or systems and behavior
• Tools for harmonization and integration of biomedical data repositories including the use of cloud environments and/or for visualizing and interacting with neuroscience datasets that may include integration of spatial and temporal features
• Proposals that include related legal, social, or ethical research, since socio-cultural, economic, legal, and ethical implications can amplify or mitigate technical challenges of achieving the Smart Health vision. This includes, for example, broader societal implications of the uses of data and technologies, such as potential benefits, burdens, or risks that might accrue differentially to different populations
• The development and validation of invasive and non-invasive devices, diagnostic and digital monitoring tools, advanced imaging techniques, computational models, and tissue engineering techniques
• Projects seeking to translate engineering products or devices to patient use, including those that require regulatory approval, would be best directed to other programs targeting that stage of development in the NINDS Division of Translational Research

National Institute of Nursing Research (NINR)

The National Institute of Nursing Research (NINR) supports research to solve pressing health challenges and inform practice and policy - optimizing health and advancing health equity into the future. 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.

National Library of Medicine (NLM)

NLM is interested in the development of technologies, analytics and models that utilize novel informatics and data science approaches to support biomedical discovery and data-powered health, integrating streams of complex and interconnected research outputs that can be translated into scientific insights, clinical care, public health practices, and personal wellness. NLM's research focuses on new and innovative computational methods and approaches to foster data driven discovery in the biomedical and clinical health sciences as well as domain-independent, scalable, generalizable, and reusable/reproducible approaches to discovery, curation, analysis, organization, and management of health-related digital objects. These approaches should support FAIR principles of data management.

National Center for Complementary and Integrative Health (NCCIH)

NCCIH is committed to the rigorous investigation of complementary and integrative health approaches, which include mind and body approaches such as meditation, hypnosis, acupuncture, massage, spinal manipulation/mobilization, yoga, tai-chi, qigong, and music and art therapy, and natural products such as botanicals, probiotics, dietary supplements, and special diet to determine their usefulness, safety, and biological mechanisms. Through this solicitation, NCCIH is particularly interested in research that advances technologies, analytics, and models of these complementary and integrative health approaches for the following applications:

• Pain management
• Improving sleep and reducing sleep disturbances
• Reducing symptomatic conditions, such as those associated with menopause and chronic fatigue syndrome
• Improving management of mental health conditions commonly managed in primary care such as mild to moderate depression or anxiety
• Adopting and sustaining healthy behaviors such as healthy eating, smoking cessation, and physical activity
• Promoting emotional well-being and whole person health

National Center for Advancing Translational Sciences (NCATS)

The mission of the National Center for Advancing Translational Sciences (NCATS) is to catalyze the generation of innovative methods and technologies that will enhance the development, testing and dissemination of effective medical/behavioral interventions diagnostics and therapeutics across a wide range of human diseases and conditions. The NCATS Clinical and Translational Science Awards (CTSA) Program supports a consortium of CTSAs located at medical research institutions across the Nation (see https://ncats.nih.gov/ctsa/about). The focus of this consortium is to foster high-quality, collaborative translational science, essential to meeting the NCATS mission. This is performed through innovation in translational science, workforce development, infrastructure support, trans-consortium collaboration, and community engagement.

Digital Health Technologies (DHT) that can be adopted and implemented for clinical and translational science is of particular interest. These technologies and approaches enable generation of novel data types that can give a more robust assessment of human health as well as subsequent perturbations due to disease and/or therapeutic intervention. DHTs include wearables, mobile health (mHealth apps), ambient sensors, health information technology, and other software as a medical device solutions. Digital health approaches incorporate any combination of computing platforms, connectivity, software, and/or sensors to generate digital biomarkers. Appropriate topics include, but are not limited to, the following:

• Platforms, tools, or resources to aid in the recruitment of clinical trials: leveraging wearables and other out-of-clinic objective measures in remote or underserved communities.
• Procedures, methods, and clinical workflow integration that enable the rapid dissemination of DHT to multiple diverse communities and/or to geographically isolated, medically underserved, and otherwise vulnerable populations (e.g., community centered design).
• Projects focused on measuring the impact and health outcomes of the use of DHTs.
• DHT validation resources for using rigorous research methods (e.g., the use of gold-standard comparators) to evaluate proposed digital health solution(s) and medically actionable signal(s)
• DHT platforms, tools, or resources to enable more objective understanding, earlier mitigation, and/or longer-term monitoring for adverse events or side effects from treatment
• DHT platforms, tools, or resources for adaptive clinical trial design (proactive patient stratification) by incorporating novel (digital) endpoints/surrogate markers
• Platforms, tools, or resources to enable DHT as complement to other assays or validated biomarkers with AI/ML approaches to generate predictive correlates (e.g., digital phenotype) in measuring response to medical interventions
• Integration, predictive analysis, and visualization of data from multiple DHTs and/or informatics resources (e.g., in silico patient avatars)
• DHT platforms, tools, or resources to link, combine, and analyze multiple data streams (e.g., real-time surveys, sensors, wearables, GPS/GIS, home-based monitors, clinical trials data, etc.) to address pressing population and public health issues
• Tools, platforms and/or applications to collect and validate DHT-derived data for its use as Real-World Data and subsequent linking to other sources (e.g., other reliable clinical and/or public health data sources) to support Real-World Evidence

Applicants are strongly encouraged to collaborate or partner with the NCATS Clinical and Translational Science Awards (CTSA) Program awardees (https://ncats.nih.gov/ctsa/about)

Brain Research Through Advancing Innovative Neurotechnologies (BRAIN) Initiative

The NIH BRAIN Initiative aims to revolutionize our understanding of the human brain by accelerating the development and application of innovative technologies. Grant applications that fall into one or more of seven high-priority research areas will be considered for funding, including those that address monitoring the brain in action, demonstrating causality between brain activity and behavior, and developing new theoretical and data analysis tools to understand brain function. Research projects of particular interest include those that advance neural data science and artificial intelligence methods and tools for understanding the brain; develop innovative multimodal models for quantifying or interpreting brain behavior relationships across scales; and engineer transformative sensor systems for measuring neural, cognitive, and behavioral functions in humans or other animals.

Office of AIDS Research (OAR)

The NIH OAR Mission is to ensure that NIH HIV/AIDS research funding is directed at the highest priority research areas and to facilitate maximal return on investment. Data science is a multidisciplinary field that includes the implementation of a variety of strategies for preparing data for exploration and analysis as well as building tools and models to interpret and visualize such data. Large HIV datasets are in existence and need to be better leveraged and made accessible to the larger scientific community and any public that may solicit access. The NIH-OAR has a broad interest in projects that will leverage existing databases and data collections in HIV, to gather, harmonize and present information via appropriate interfaces for easy access for analysis, evaluation and publication of combined data. The NIH-OAR does not award nor administer grants, therefore, applications in response to this notice must also be relevant to the objectives of at least one of the participating NIH Institutes and Centers (IC). Please contact the relevant IC program contact listed for questions related to IC research priorities and funding.

Office of Disease Prevention (ODP)

The ODP is the lead office at the NIH responsible for assessing, facilitating, and stimulating research on disease prevention. In partnership with the 27 NIH Institutes and Centers, the ODP strives to increase the scope, quality, dissemination, and impact of NIH-supported prevention research. For this solicitation, ODP is interested in the application of artificial intelligence and machine learning (AI/ML) to advance disease prevention research and health equity. ODP has a specific interest in providing co-funding support for projects that use AI/ML to improve risk prediction, enhance screening, and implement preventive interventions in various settings. For additional information about ODP’s research priorities and interests, please refer to the ODP Strategic Plan for Fiscal Years 2019 2023.

The ODP does not award grants; therefore, applications must be relevant to the objectives of at least one of the participating NIH Institutes and Centers (IC) listed in this announcement. Please contact the relevant IC Scientific/Research Contact(s) listed for questions regarding IC research priorities and funding.

Office of Data Science Strategy (ODSS)

The mission of ODSS is to catalyze new capabilities in biomedical data science for modernization of the NIH data ecosystem, development of a diverse and talented data science workforce, and the development and dissemination of advanced technologies and methods. ODSS has broad interests in petabyte scale data science methodology and ethics, transparency, and bias in AI. Program areas of particular relevance include adaptive methods and algorithms to handle petabyte-size data query, analysis, and encryption; Edge-to-Fog mobile-device and data-interface tools that aggregate data to a cloud environment; FHIR applications with relevance to clinical and research data; AI applications in transparency and bias in large biomedical datasets; and methods to dynamically improve metadata with advance knowledge graphs for AI applications. Applications must also be relevant to the objectives of at least one of the participating NIH Institutes and Centers (IC). ODSS does not award grants. Please contact the relevant IC program contact listed for questions related to IC research priorities and funding.

Office of Nutrition Research (ONR)

ONR is interested in the development of engineering applications that, analogous to continuous glucose monitors (CGM), propose novel and innovative approaches to detect and measure nutrients, metabolites, and nutrition related substances (e.g., hormones) from sweat or subcutaneous space using wearable devices. ONR is also interested in the development of artificial intelligence (AI) and machine learning (ML) approaches to identify and assess regional food environments and putative solutions to nutrition disparities.

Office of Research on Women’s Health (ORWH)

ORWH welcomes applications that align closely with the ORWH mission of enhancing and expanding women’s health research to actualize the NIH vision of sex and gender integration into biomedical and behavioral research and every woman receives evidence-based personalized care tailored to their unique needs. In addition, ORWH has particular interest in interdisciplinary and transdisciplinary research approaches to unpack health disparities and (gender) health equity. Specific areas of research interest include, but are not limited to:

• Improve data science and data management practices with innovative research methods, measurements, and cutting-edge technologies to prevent and treat conditions affecting women across their lifespan
• Novel artificial intelligence, machine learning, deep learning, and other data science approaches to advance the science for the health of women
• Development of digital tools, technologies, advanced statistical modeling, data visualization, and platforms that address unmet health needs of women, with special attention to gender bias, intersectional factors, and social/structural determinants of health
• Development of methods and leveraging data sources to consider sex and gender influences or that improve the evaluation of research that is relevant to the health of women
• Creation of strategies to mitigate bias in the deployment of AI algorithms on existing datasets in which women have been classically understudied, underrepresented, and underreported
• Expand and develop innovative approaches for study design, data collection, and analysis to optimize data quality and the ability to detect the influences of sex and gender on health and disease or improve the recruitment and retention of women underrepresented in clinical research
• Utilize longitudinal and repeated measurement designs and analytic approaches to characterize the health of women over time and across the life course
• Promote data sharing, data harmonization, and interoperability practices to align with FAIR principles and enhance the utility of new and existing data on the health of women and pregnant persons
• Development of cutting-edging computational tools and technologies to facilitate multimodal disease screening, prevention, diagnosis, and treatment of conditions that affect the health of women
• Advance community engaged science (including patient-centered outcome research) across the research and practice continuum and enhance the dissemination and implementation of evidence-based solutions to improve the health of all women

Office of Science Policy (OSP)

OSP seeks to advance research that furthers bioethics essential role in promoting the conduct and translation of the highest quality biomedical and behavioral health research, critical for emerging scientific opportunities in digital health. In particular, OSP is interested in applications that identify, analyze, and/or address bioethical issues related to digital health and/or real-world data, including:

• Designing and evaluating tools or approaches to aid individual, family, and community decision-making about use of digital health technologies and/or real-world data
• Studies that examine understanding of ethical issues related to digital health and/or real-world data among researchers and/or examine issues related to disclosure of information about the use of digital health and/or real-world data for biomedical and/or health-related behavioral research
• Evaluations of practices related to using digital health technologies in research, including issues related to data privacy and data security, consent, transparency, access, and equity
• Analyzing ethical, legal, and policy issues raised by use of digital health technologies in research
• Considerations for using digital health data and/or real-world data to generate knowledge for biomedical and/or health-related behavioral research

Sexual and Gender Minority Research Office (SGMRO)

The SGMRO coordinates research and activities related to the health and well-being of sexual and gender minority (SGM; defined for NIH research in NOT-OD-19-139) communities by working directly with the NIH ICOs and serves as a liaison for the research community to ensure SGM people are considered and represented in research activities across the agency.

The SGMRO does not have grant-making authority and can only support grants deemed meritorious after review by one of the ICs participating in this announcement and after a co-funding request is initiated through the IC. Please reach out to the relevant scientific/research contact(s) identified in this announcement with any questions about IC-specific research priorities and funding. More SGM- and SGMRO-specific information is available in the NIH Strategic Plan to Advance Research on the Health and Well-being of Sexual and Gender Minorities FY 2021-2025 and on the office’s research resources webpage.

For the purposes of this notice, SGMRO is interested in the development and usage of digital tools and artificial intelligence and machine learning (AI/ML)-based methodologies to advance SGM health and research. SGMRO is particularly interested in technologies and strategies that facilitate and improve collection, harmonization/integration, disaggregation, analysis, and interpretation of data on sexual orientation, gender identity, and sex characteristics. SGMRO also encourages research to ensure that digital, smart health, and AI/ML approaches are not discriminatory against SGM and other historically marginalized populations.

Application Preparation and Submission Instructions

Proposals submitted in response to this program solicitation should be prepared and submitted in accordance with the general guidelines contained in the NSF Proposal & Award Policies & Procedures Guide (PAPPG). The complete text of the PAPPG is available electronically on the NSF website at: https://www.nsf.gov/publications/pub_summ.jsp?ods_key=pappg. Paper copies of the PAPPG may be obtained from the NSF Publications Clearinghouse, telephone (703) 292-8134 or by e-mail from nsfpubs@nsf.gov. Proposers are reminded to identify this program solicitation number in the program solicitation block on the NSF Cover Sheet for Proposal to the National Science Foundation. Compliance with this requirement is critical to determining the relevant proposal processing guidelines. Failure to submit this information may delay processing.

Budgetary Information

Projects will be funded for up to a four-year period and for up to a total of $300,000 per year. Budgets should include travel funds to attend one Smart Health PI meeting annually for the project PIs, co-PIs and other team members as appropriate from all collaborating institutions.

Inclusion of voluntary committed cost sharing is prohibited.

For NIH, indirect costs on foreign subawards/subcontracts will be limited to eight (8) percent.

NIH Process

Proposals selected for funding consideration by participating NIH Institutes and Centers will be required to be resubmitted to Grants.gov. Applicants must then complete the submission process and track the status of the application in the eRA Commons, NIH’s electronic system for grants administration. PIs invited to resubmit to NIH will receive further information on this process from the NIH.

Consistent with the NIH Policy for Data Management and Sharing, when data management and sharing is applicable to the award, recipients will be required to adhere to the Data Management and Sharing requirements as outlined in the NIH Grants Policy Statement. Upon the approval of a Data Management and Sharing Plan, it is required for recipients to implement the plan as described. All applicants planning research (funded by NIH) that results in the generation of scientific data are required to comply with the instructions for the NIH Data Management and Sharing Plan. All applications, regardless of the amount of direct costs requested for any one year, must address a Data Management and Sharing Plan.

An applicant will not be allowed to increase the proposed budget or change the scientific content of the application in the reformatted submission to the NIH. Indirect costs on any foreign subawards/subcontracts will be limited to eight (8) percent. Applicants will be expected to utilize the Multiple Principal Investigator option at the NIH (https://grants.nih.gov/grants/multi_PI/) as appropriate.

To fulfill NIH's need for a list of participating reviewers for Summary Statements without disclosing the specific reviewers of each proposal, NSF will provide an aggregated list of the full set of reviewers for the SCH program to CSR.

Following the NSF peer review, recommended applications that have been resubmitted to the NIH are required to go to second level review by the Advisory Council or Advisory Board of the awarding Institute or Center. The following will be considered in making funding decisions:

• Scientific and technical merit of the proposed project as determined by scientific peer review.
• Availability of funds.
• Relevance of the proposed project to program priorities.
• Adequacy of data management and sharing plans.

Subsequent grant administration procedures for NIH awardees, including those related to New and Early Stage Investigators (https://grants.nih.gov/policy/early-investigators/index.htm), will be in accordance with the policies of NIH. Applications selected for NIH funding will use the NIH R or U funding mechanisms. At the end of the project period, renewal applications for projects funded by the NIH are expected to be submitted directly to the NIH as Renewal Applications.

Inquiries

Please direct all inquiries to:

Inquiries are encouraged and NIH Scientific/Research contacts are listed below. Please see the NSF Smart Health website for names and contact information of participating NSF Directorates.

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

James Gao, PhD
National Eye Institute (NEI)
Telephone: 301-594-6074
Email: james.gao@nih.gov

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

Lyndon Joseph, PhD
National Institute on Aging (NIA)
Telephone: 301-496-6761
Email: lyndon.joseph@nih.gov

Office of Data Science and Emerging Technologies (ODSET)
National Institute of Allergy and Infectious Diseases (NIAID)
Email: datascience-foa@niaid.nih.gov

Aron Marquitz, PhD
National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS)
Telephone: 301-435-1240
Email: aron.marquitz@nih.gov

Qi Duan, PhD
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Telephone: 301-827-4674
Email: qi.duan@nih.gov

Samantha Calabrese, PhD
Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)
Telephone: 301-827-7568
Email: samantha.calabrese@nih.gov

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

Roger Miller, PhD
National Institute on Deafness and Other Communication Disorders (NIDCD)
Telephone: 301-402-3458
Email: millerr@nidcd.nih.gov

Noffisat Oki, Ph.D.
National Institute of Dental and Craniofacial Research (NIDCR)
Telephone: 301-402-6778
Email: noffisat.oki@nih.gov

Xujing Wang, PhD
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Telephone: 301-451-2862
Email: xujing.wang@nih.gov

Christopher Duncan, PhD
National Institute of Environmental Health Sciences (NIEHS)
Telephone: 984-287-3256
Email: christopher.duncan@nih.gov

David I Leitman
NIMH - NATIONAL INSTITUTE OF MENTAL HEALTH
Phone: (301) 827-6131
E-mail: david.leitman@nih.gov

Deborah Duran, PhD
National Institute on Minority Health and Health Disparities (NIMHD)
Telephone: 301-594-9809
Email: deborah.duran@nih.gov

Leslie C. Osborne, PhD
National Institute of Neurological Disorders and Stroke (NINDS)
Telephone: 240-921-1359
Email: leslie.osborne@nih.gov

Kristopher Bough, PhD
National Institute of Nursing Research (NINR)
Telephone: 301-496-2604
Email: kristopher.bough@nih.gov

Yanli Wang, PhD
National Library of Medicine (NLM)
Telephone: 301-594-4882
Email: yanli.wang@nih.gov

Emrin Horguslouglu, PhD
National Center for Complementary and Integrative Health (NCCIH)
Telephone: 240-383-5302
Email: emrin.horgusluoglu-moloch@nih.gov

Chris Hartshorn, Ph.D.
National Center for Advancing Translational Sciences (NCATS)
Phone: 301-402-0264
Email: christopher.hartshorn@nih.gov

Joseph Monaco, PhD
Brain Research Through Advancing Innovative Neurotechnologies (BRAIN) Initiative
Telephone: 301-402-3823
Email: joseph.monaco@nih.gov

Robert Cregg
NIH Office of AIDS Research (OAR)
Senior Supervisory Health Scientist and Analytics Advisor
Phone: 301-761-7557
E-mail: Robert.Cregg@nih.gov

Jacqueline Lloyd, PhD, MSW
Senior Advisor for Disease Prevention
Office of Disease Prevention (ODP)
Phone: 301.827.5559
E-mail: lloydj2@mail.nih.gov

Fenglou Mao
ODSS - Office of Data Science Strategy
Phone: 240-627-1111
E-mail: fenglou.mao@nih.gov

Nicholas Jury, PhD
Office of Nutrition Research (ONR)
Telephone: 301-827-1234
Email: nicholas.jury@nih.gov

Jamie White, MS
Office of Research on Women’s Health (ORWH)
Telephone: 301-496-9200
Email: jamie.white@nih.gov

Adam Berger, PhD
Office of Science Policy (OSP)
Telephone: 301-827-9676
Email: adam.berger@nih.gov

Christopher Barnhart, PhD
Sexual & Gender Minority Research Office (SGMRO)
Telephone: 301-594-8983
Email: christopher.barnhart@nih.gov