Data Science and Artificial Intelligence Approaches for Biomedical, Biobehavioral and Social Science Research

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Topic Description

Post Date: June 25, 2026

Expiration Date: June 25, 2028

Data science and artificial intelligence (AI) are increasingly useful for addressing complex biomedical problems in part through harmonizing, deciphering, and extracting complex patterns from the massive amount of multimodal, high-dimensional data generated by biomedical research and electronic health records (EHRs). These data include genetic, molecular, cellular, circuit-level, imaging, physiological, behavioral, linguistic, clinical and epidemiological data. New data science and AI approaches can fully leverage these data to effectively model the complex biobehavioral and environmental factors and processes that contribute to risk and resilience trajectories, and to integrate and improve prevention and treatment. However, significant challenges limit widespread adoption and integration of AI in biomedical and behavioral research and clinical practice. These include the heterogeneity and complexity of biomedical data that are often fragmented and lack of uniform standards. Development and implementation of robust, reproducible and scalable AI approaches are needed. 

This topic promotes development, rigorous validation and application of innovative AI approaches capable of modeling complex structures and processes that cannot be addressed with traditional statistical or computational models; and to develop standards, workflows, protocols and other resources to improve widespread application of these approaches to biomedical and behavioral data. These new approaches and practices will accelerate biomedical discovery, enhance understanding of risk factors and disease mechanisms, improve detection, prevention and treatment, and integrate services to deliver more effective healthcare. Areas of interest include, but are not limited to:

  • Approaches for integrating heterogeneous, multimodal, biomedical and behavioral data with improved robustness.
  • Interpretable and explainable AI for mechanistic insight, enhancing transparency and trust for broader adoption.
  • AI bridging multiple scales, including methods integrating multimodal longitudinal data to model risk, protection and disease trajectories.
  • Reproducible, scalable, generalizable AI that performs reliably across institutions, healthcare systems and settings, populations and experimental platforms.
  • AI tools that scale across and can integrate prevention and healthcare services, optimize clinical trials and/or develop precision interventions.
  • Development efforts to make large-scale biomedical/biobehavioral data AI ready.
  • Approaches to integrate disparate data systems (e.g., EHRs, surveillance, treatment, community services) to enable coordinated, longitudinal prevention and treatment care.
  • AI-driven clinical decision tools to improve integrated management of chronic disorders and their comorbidities.
  • Systems to improve engagement with patients, providers and community stakeholders in the development and implementation of AI tools for routine use in clinical and public health practice.
This topic is being issued as part of the Make America Healthy Again initiative, which is expanding NIH and agency research into specific areas.

It aligns with:

  • Mental Health and Addiction Research : The NIH, working with HHS, will strengthen existing research by directing funding for research on mental health and addiction, with a special focus on screentime use in children and adolescents.
  • Artificial Intelligence : HHS, NIH, and the Office of Science and Technology Policy will develop an evidenced-based and AI-driven approach to harnessing the data and technology available to transform research and clinical trials on pediatric cancer. This can be a model for future research in other critical areas.

Participating ICOs

National Institute on Drug Abuse (NIDA)

NIDA supports research using AI and large multiplex datasets to predict clinical outcomes and model complex interactions among risk factors across levels relevant to substance use (SU), SU disorders (SUDs) and HIV. These approaches can provide fundamental knowledge, e.g. genetic pathways or neural systems and insights into biobehavioral, social, and environmental factors, SUD risk and morbidity across SU/SUD trajectories, and improve prevention and recovery. NIDA supports research on substances including cannabinoids, psychomotor stimulants (e.g., cocaine, methamphetamine), opioids, nicotine, and emerging synthesized substances. NIDA supports research using AI to coordinate care and improve treatment engagement for people with HIV and/or SUD. Research on alcohol use is supported only in the context of misuse of other substances of interest to NIDA. NIDA also supports research on pathological effects of ultra-processed foods that may contribute to SUD risk, morbidity and comorbidities.

IC may dedicate funds available to support applications in this Topic area depending upon the availability of funds, the number of meritorious applications, and competing ICO priorities.
IC may give special consideration to support meritorious applications in this topic area.
ICO Scientific Contact:
Jessica Mollick
[email protected]

National Center for Complementary and Integrative Health (NCCIH)

NCCIH supports applications that develop or apply data science, computational modeling, and generative AI tools to advance basic, mechanistic, translational, and clinical research on complementary and integrative health approaches.

Complementary health approaches include natural products (e.g., diets, supplements, herbs, prebiotics, probiotics) and/or mind and body interventions (e.g., meditation, hypnosis, virtual reality, music, relaxation, acupuncture, massage, chiropractic manipulation, light-based therapies, related devices, yoga, tai chi, dance, some art therapies). Integrative health coordinates conventional and complementary approaches. 

Outcomes of interest include whole person health, restoration, emotional well-being, resilience, pain, sleep, chronic disease prevention, and symptom management. NCCIH also supports development and validation of tools to enhance implementation and access through health information technology.

IC may give special consideration to support meritorious applications in this topic area.
ICO Scientific Contact:
Emrin Horgusluoglu, PhD
[email protected]

National Cancer Institute (NCI)

NCI is interested in research ideas that are focused on the development and application of AI technologies to address technical challenges and research/clinical questions relevant to human cancer.  These include, but not limited to, AI development to facilitate cancer biology research, AI development for cancer prevention, AI development to enhance cancer diagnosis and treatment, and AI development in aid of cancer control and population studies.  Additionally, NCI encourages collaborations between cancer researchers and AI experts to jointly develop novel AI approaches and apply them in cancer research.  NCI will consider supporting these activities through a variety of funding mechanisms including, but not limited to, R01, P01, R21, U01, U24, U54, and administrative supplement to existing awards. Use of administrative supplements is very limited, only for supporting activities due to unforeseen circumstances and not for starting new lines of research. 

IC may give special consideration to support meritorious applications in this topic area.
ICO Scientific Contact:
Jiayin Li, MD, PhD
[email protected]

National Eye Institute (NEI)

The National Eye Institute (NEI) advances research on chronic recurrent eye diseases characterized by episodic flare-ups and remission periods. NEI seeks research leveraging data science and generative artificial intelligence (GenAI) to advance mechanistic understanding of chronic recurrent eye disorders, identify individual risk factors and biomarkers, and drive precision diagnostics that can predict symptom episodes and optimize the timing of treatment. This research should catalyze innovative therapeutic interventions, including personalized treatment strategies that adapt to individual disease patterns and environmental factors affecting chronic eye conditions. NEI encourages research that integrates complex, multi-dimensional datasets from real-world clinical sources—including genetic, molecular, imaging, physiological, and behavioral data—while safeguarding individual privacy and enhancing transparency in AI model development. 

ICO Scientific Contact:
James Gao, Ph.D.
[email protected]

National Institute on Aging (NIA)

NIA supports research on chronic recurrent disorders in aging, including Alzheimer’s disease (AD), integrating multimodal data in model systems and humans—genetics, proteomics, metabolomics, single-cell and spatial omics, imaging, and longitudinal behavioral, clinical, real world (e.g., electronic health records, administrative claims), and physiological measures—to model dynamic risk and resilience processes, define preclinical and real-world disease trajectories, and identify modifiable intervention targets across the lifespan. Of interest are AI-driven approaches to infer causal pathways and organ function changes affecting overall health; link molecular, cellular, and circuit alterations to cognitive and functional outcomes; identify mechanistic biomarkers and therapeutic targets; and reveal how aging biology and life-course exposures confer protection in exceptional health span and shape vulnerability in AD and age-associated conditions.

ICO Scientific Contact:
Amanda DiBattista, Ph.D.
[email protected]

National Institute on Alcohol Abuse and Alcoholism (NIAAA)

The National Institute on Alcohol Abuse and Alcoholism (NIAAA) is interested in research aimed at developing artificial intelligence and machine learning models that facilitate effective analysis of large databases. NIAAA is also interested in supporting studies that will integrate knowledge bases and tools into data ecosystem and use real life environmental, social, and health data to develop strategies for the prevention and treatment of alcohol use disorder (AUD)/SUD across the lifespan.

ICO Scientific Contact:
Elizabeth Powell
[email protected]

National Institute of Biomedical Imaging and Bioengineering (NIBIB)

The National Institute of Biomedical Imaging and Bioengineering (NIBIB) supports advanced computational methods and data science tools that leverage different biomedical data sources, including imaging, wearable sensors, electronic health records, to enable prediction, diagnosis, and personalized intervention strategies. NIBIB is interested in approaches that integrate multimodal data streams to build predictive models, clinical decision support, treatment optimization, early intervention, and AI-assisted data driven technology design. NIBIB aims to accelerate the translation of data science and computational innovations into practical tools that enhance clinical decision-making and improve outcomes for individuals living with chronic recurrent conditions.

ICO Scientific Contact:
Rui Pereira De Sa, PHD
[email protected]

National Institute on Deafness and Other Communication Disorders (NIDCD)

NIDCD supports research applying data science and artificial intelligence methods to advance understanding, prevention, and treatment of chronic disorders across the mission areas of hearing, balance, taste, smell, voice, speech, and language. GenAI-driven approaches may help address the complex nature of disorders in these areas, which often involve interactions among genetic, environmental, and neurobiological factors.  Applications that leverage GenAI to maximize the scientific value of available data resources or identify critical gaps where additional data would yield breakthrough insights into disease mechanisms, risk prediction, and treatment optimization are of particular interest.

ICO Scientific Contact:
Roger Miller, Ph.D.
[email protected]

National Institute of Dental and Craniofacial Research (NIDCR)

In the context of this topic, the National Institute of Dental and Craniofacial Research encourages investigator-initiated research that aligns with the objectives of the topic and advances research relevant to dental, oral, and craniofacial health and disease.

IC may give special consideration to support meritorious applications in this topic area.
ICO Scientific Contact:
NIDCR Division of Extramural Research
[email protected]

National Institute of Mental Health (NIMH)

Generative AI (GenAI) may transform mental health research by extracting complex patterns from diverse datasets (e.g., genetic, imaging, clinical information). This capability can yield transdiagnostic insights, enabling more precise predictions of illness trajectories and treatment responses. The technology can also optimize clinical workflows and develop scalable, personalized digital therapeutics and decision-support tools for early intervention and prevention. NIMH supports research that uses GenAI to uncover the biobehavioral mechanisms of mental illness. To ensure the safe and effective translation of these advancements, NIMH requires the rigorous validation of robust, interpretable, and causally-informed models. The goal is to create precision diagnostics and interventions that improve outcomes across the quintuple aim of health: population health, patient experience, cost, clinician well-being, and health for all in real-world settings.

ICO Scientific Contact:
Michele Ferrante, Ph.D.
[email protected]

National Institute of Nursing Research (NINR)

NINR supports research to advance the application of data science and genAI approaches to prevent chronic conditions and optimize outcomes among individuals with chronic conditions. Areas of interest include those articulated in the Topic Description. Applications for research that is informed by nursing expertise and engage relevant communities such as patients, families, and healthcare providers, are encouraged.

IC may give special consideration to support meritorious applications in this topic area.
ICO Scientific Contact:
Mary Bowen
[email protected]

National Library of Medicine (NLM)

NLM supports research to develop, evaluate, and sustain AI and data science tools and methods which provide utility that generalizes across human diseases and disorders.

ICO Scientific Contact:
Clayton Bingham, PhD
[email protected]

Office of Data Science Strategy (ODSS)

ODSS is interested in stimulating research opportunities to develop new AI technologies that enable data translation to knowledge, including AI tools for data cleaning, harmonization, integration, and metadata collection; developing tools to help researchers create and prepare FAIR and AI-ready data, including ontologies, schema, and data quality measures; facilitating FAIR algorithms with sufficient documentation and metadata and enhance ethical frameworks; leveraging new technologies and methods for foundational multimodal models  to accelerate biomedical and behavioral research; and enhancing partnerships across communities to develop new methods in AI while maintaining robust, responsible, and transparent practices.

IC may give special consideration to support meritorious applications in this topic area.
This office does not award grants. Applications must be relevant to the objectives of at least one of the participating Institutes or Centers listed in this topic.
ICO Scientific Contact:
Mohd Anwar
[email protected]

Christine Cutillo
[email protected]

Office of Research on Women's Health (ORWH)

The NIH Office of Research on Women's Health (ORWH) is interested in research focusing on:

  • AI integrating genomic, immune, hormonal, and behavioral data to model how hormones, reproduction, and female‑based immune pathways contribute to relapse, symptom fluctuation, and treatment response in chronic conditions in females. 
  • Data science to define how sex‑based biology intersects with contextual and environmental factors to determine risk, severity, and recovery from chronic disorders, including SUDs, in women across the life course

The Office of Autoimmune Disease Research in ORWH (OADR-ORWH) is interested in research focusing on:

  • Multimodal data-driven modeling and computational tools focused on advancing chronic autoimmune disease research.
This office does not award grants. Applications must be relevant to the objectives of at least one of the participating Institutes or Centers listed in this topic.
ICO Scientific Contact:
Elena Gorodetsky, M.D., Ph.D.
[email protected]

Victoria Shanmugam, MBBS, MRCP, FACR, CCD
[email protected]

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