Research Areas

Alzheimer’s Disease and Related Dementia

RFP

The Johns Hopkins Artificial Intelligence and Technology Collaboratory (JH AITC) is seeking proposals that facilitate the rapid development and implementation of novel artificial intelligence or technology solutions that improve the health and well-being of older persons with Alzheimer’s disease and related disorders (ADRD).

The JH AITC welcomes applications from investigators with engineering, clinical, nursing, medicine, rehabilitation, therapy, social work, nutrition, public health, and business backgrounds and proposes funding approximately $1,000,000 for pilot projects in 2025. Proposed budgets can range from $10,000–200,000 over one to two years. Applicants from underrepresented racial and ethnic groups, women, members of minoritized communities, and individuals with disabilities are encouraged to apply.

Acceptable proposals must demonstrate some viable pathway toward product development and must prioritize the use of engineering- or AI-based approaches such as robotics, machine learning, big data analytics, image and biometric scanning, speech and natural language processing, integrated platforms, mobile/smart devices and applications, or nanobiotechnologies. Emergent technologies, as well as the adaptation of existing technologies or AI approaches to address a novel ADRD-related problem, are encouraged. Proposals focused on rural health are also encouraged. Proposals are expected to describe plans for pre-competitive data sharing in a consortium data commons and the National Institute on Aging archive.

Areas of programmatic interest for funding in this area, along with examples of potential projects, are listed in the tab to the left.

Areas of Interest

Patient Care and Engagement

  • Use of integrated platforms to enhance communication among older adults, their advocates, and their health care providers
  • Use of sensing technology/smart devices within home, clinic, hospital, or long-term care facility environments to detect, triage, and alert to changes in physical, behavioral, and cognitive status
  • Validation and assessment of methods for assessing, monitoring, and alleviating behavioral or neuropsychiatric symptoms in dementia (e.g., wandering, distress, paranoia)
  • AI-based precision dementia care planning using electronic health record (EHR) data analytics (i.e., polypharmacy management)

System Management and Administration

  • Predictive analytics for future health care expenditures and population risk for managed care systems
  • Design of healthcare provider alerts and decision support to mitigate safety risks
  • Redesign of teamwork, information transfer, care coordination, or clinical workflows across healthcare settings (e.g. hospital, clinic, home, or long-term care)
  • Population health monitoring which includes detection of health disparities within subgroups, expenditure and risk monitoring

Diagnostics and Assessment

  • Classification of dementia by type
  • Risk prediction tools to identify cognitive impairment or probable dementia among cognitively normal persons or non-diagnosed cases
  • Prediction of older adults’ prognosis or likelihood of surgical success, risk and resilience
  • Large multi-modal foundational models for diagnosis and assessment

Family Caregiver and Workforce Support

  • Technology to enhance caregiver wellness, provide emotional support, or facilitate financial planning
  • Virtual, augmented, mixed reality or integrated platforms for healthcare provider or family caregiver education and skill training
  • Technology to enable clinical skills and diagnostic capabilities in resource-limited areas
  • Platforms to support caregiver expectation management, role assignment, task management and assistance with direct caregiving activities and ADLs (activities of daily living)

Biology

  • Patient subtyping from molecular data
  • Temporal AI/ML models of phenotype and molecular changes during aging
  • Analyzing interventions for healthy aging and candidate drug targets for AD/ADRD
  • Deep phenotyping
  • Multi-modal data analysis
  • AI/ML for data types such as genomics, imaging, metabolomics, EHR

Please note: These pilot awards are not meant to fund projects related to geriatrics and age-related conditions. See the RFP for healthy aging, below.

Resources

A broad variety of resources are potentially available to facilitate development and completion of pilot projects, including:

  • Clinical research personnel to assist with the development and implementation of a clinical study
  • A registry of older adults from which to recruit
  • Older adult stakeholders from urban and rural areas
  • A network of potential research subjects from these areas
  • Technology development and feasibility testing
  • Database, data collection, and analytical expertise
  • Market and business model validation
  • Business networking opportunities

Useful Links

Core Leaders

Quincy Samus, PhD

Quincy Samus, PhD

Director, ADRD Pilot
Pilot Core A Core Leaders
Najim Dehak, PhD

Najim Dehak, PhD

Co-Director, ADRD Pilot
Pilot Core A Core Leaders Engineering Resources
Esther Oh, MD, PhD

Esther Oh, MD, PhD

Co-Director, ADRD Pilot
Pilot Core A Core Leaders

The goal of the Alzheimer’s Disease and Related Dementia Pilot Core is to use a multidisciplinary, collaborative approach to identify, develop, refine, and disseminate promising technologies that have a high potential to improve the health and well-being of older Americans living with ADRD and/or their caregivers, with an emphasis on technologies that can mitigate current disparities in the access and delivery of dementia care. Core leadership’s duties include:

  • Developing an annual call for and funding pilot studies focused on the application of AI and related technologies to improve the health and well-being of older adults with ADRD and/or their caregivers
  • Ensuring pilot projects are well-designed, timely, and rigorous by providing intellectual leadership, oversight, and access to JH AITC resources
  • Assisting in the further development and translation of completed pilot projects into products that will benefit older adults with ADRD and/or their caregivers
  • Expanding the expertise and network of funded investigators focused on advancing dementia-related AI technologies

Healthy Aging

RFP

The JH AITC is seeking proposals that facilitate the rapid development and implementation of novel AI or technology solutions that improve the health and well-being of older persons.

The JH AITC welcomes applications from investigators with engineering, medicine, rehabilitation, therapy, social work, nutrition, nursing, public health, or business backgrounds and proposes funding approximately $475,000 for pilot projects in 2025. Proposed budgets can range from $10,000–200,000 over one to two years.  Applicants from underrepresented racial and ethnic groups, women, members of minoritized communities, and individuals with disabilities are encouraged to apply.

Acceptable proposals must demonstrate some viable pathway toward product development and must prioritize the use of engineering- or AI-based approaches such as robotics, machine learning, big data analytics, image and biometric scanning, speech and natural language processing, integrated platforms, mobile/smart devices and applications, or nanobiotechnologies. Emergent technologies, as well as the adaptation of existing technologies or AI approaches to address aging-related health problems, are encouraged. Proposals focused on rural health are also encouraged. Proposals are expected to describe plans for pre-competitive data sharing in a consortium data commons and the National Institute on Aging archive.

Areas of programmatic interest for funding in this area, along with examples of potential projects, are listed in the tab to the left.

Areas of Interest

Patient Care and Engagement

  • Personalized approaches to treatment of conditions relevant to older persons
  • Promotion of successful aging, well-being, and resilience to (e.g., prevent frailty and muscle loss progression, engagement of older adults in physical activity, optimization of nutrition, management of physical disabilities, social engagement/prevention of loneliness)
  • Patient or caregiver access to health care or information through digital technologies (e.g., telemedicine)
  • Promotion of physical safety at home including fall prevention and detection
  • Precision medicine in the prevention and treatment of common health conditions or geriatric syndromes
  • Addresses or incorporates social determinants of health in care delivery/health systems

System Management and Administration

  • Predictive analytics for future healthcare expenditures and population risk for managed care systems
  • Design of health care provider alerts and decision support to mitigate safety risks
  • Redesign of teamwork, information transfer, care coordination, or clinical workflows across healthcare settings (e.g., hospital, clinical, home, or long-term care)
  • Population health monitoring which includes detection of health disparities within subgroups, expenditure and risk monitoring

Diagnostics and Assessment

  • Geroscience diagnostic approaches or assessments of risk prior to procedure or surgery
  • Identifying and addressing medication problems (e.g., polypharmacy medication safety, drug interactions)
  • Early detection of signs of frailty (e.g., weakness, exhaustion, weight and muscle loss) or co-existing health conditions
  • Large multi-modal foundational models for diagnosis and assessment

Family Caregiver and Workforce Support

  • Technology to enhance caregiver wellness, provide emotional support, or facilitate financial support
  • Virtual, augmented, mixed reality or integrated platforms for healthcare provider or family caregiver education and skill training
  • Technology to enable clinical skills and diagnostic capabilities in resource-limited areas
  • Platforms to support caregiver expectation management, role assignment, task management, and assistance with direct caregiving activities and ADLs (activities of daily living)

Biology

  • AI -driven biological clocks
  • Cellular mechanisms including telomere attrition, mitochondrial dysfunction, and epigenetic alterations
  • Multi-modal data analysis
  • AI/ML for data types such as genomics, imaging, metabolomics
  • Machine learning analyses of aging hallmarks

Please note: These pilot awards are not meant to fund projects related to Alzheimer’s disease or other dementias. See the RFP for ADRD, above.

Resources

A broad variety of resources are potentially available to facilitate development and completion of pilot projects, including:

  • Clinical research personnel to assist with the development and implementation of a clinical study
  • A registry of older adults from which to recruit
  • Older adult stakeholders from urban and rural areas
  • A network of potential research subjects from these areas
  • Technology development and feasibility testing
  • Database, data collection, and analytical expertise
  • Market and business model validation
  • Business networking opportunities

Useful Links

Core Leaders

Jeremy D. Walston, MD

Jeremy D. Walston, MD

Co-Principal Investigator, JH AITC
Administrative Core Pilot Core B Access to Underserved Populations of Older Adults JHU University-Wide Resources
Mounya Elhilali, PhD

Mounya Elhilali, PhD

Co-Director, Healthy Aging Pilot (Geriatrics)
Pilot Core B Core Leaders Engineering Resources

The goal of the Healthy Aging Pilot Core is to utilize a multidisciplinary collaborative approach to identify, develop, refine, and disseminate promising technologies that have high potential to improve the health and well-being of older Americans and/or their caregivers, with an emphasis on technologies that can mitigate current disparities in the access and delivery of health care in rural and urban areas across the US. Core leadership’s duties include:

  • Identifying and funding innovative AI- and machine-learning-supported technologies that promise to improve the health and well-being of older adults through an annual pilot award process
  • Ensuring pilot projects are well-designed, timely, and rigorous by providing intellectual leadership, oversight, and access to JH AITC resources
  • Assisting in the further development and translation of completed pilot projects into products that will benefit older adults and/or their caregivers
  • Expanding the expertise and network of funded investigators focused on AI technologies relevant to healthy aging

If you’re interested in being listed as a researcher for potential collaboration opportunities with industry partners and academics, please take a moment to complete this survey: JH AITC – Future Research Collaboration Permission Form

Researchers Interested in Collaborating

Researcher   Expertise
Roy Adams Computer Scientist, Johns Hopkins University Causal inference for observational data (especially electronic health records); clinical decision support tools
Ehsan Adeli Researcher, Stanford University Artificial intelligence and computer vision; precision health care; computational psychiatry
Dennis Anderson Research Scientist, Beth Israel Deaconess Medical Center Human movement; spine biomechanics; musculoskeletal modeling; aging research
John Batsis M.D., UNC Chapel Hill Obesity; physical function
Ankur Butala M.D., Johns Hopkins Medicine Movement disorders; deep brain stimulation; physiological biomarker development
Yannick Cohen Engineer, Brightway Health Customer discovery; product development
Nicholas Constant Engineer, WellAware Research Digital health; wearables
Joseph DeMattos Adjunct Faculty, UMBC Research and public policy
Linda Denney Physical Therapist/Asst. Prof., Tufts Univ. Movement analysis
Sydney Dy M.D., Johns Hopkins Medical Health services research
John Fitch Engineer, Sovrinti Inc. Systems and sensors; radio frequency communications; data analytics; machine learning and artificial intelligence; Internet of Things; instrumentation; program and project management
Kimia Ghobadi Engineer, Johns Hopkins University Optimization and decision-making
Kelly Gleason Asst. Prof., Johns Hopkins School of Nursing Patient portals; patient-clinician communication
Chien-Ming Huang Engineer, Johns Hopkins University Human-robot interaction
Ula Hwang M.D., NYU Langone Health Geriatric and dementia emergency care
Vijaya B. Kolachalama Prof., Boston University Machine learning
Laura Korthauer Clinical Neuropsychologist, Rhode Island Hospital Neuropsychological assessment; cognition; Alzheimer’s disease and related dementias
F. Vankee Lin Researcher, Stanford University Aging; mental health; precision medicine; intervention
Laureano Moro-Velazquez Engineer, Johns Hopkins University Machine learning to diagnose neurological diseases
Chintan Pandya Researcher, Johns Hopkins University Health systems; electronic health records; clinical decision support; prediction modeling
Lise Pape Engineer, Walk with Path Design; product development; clinical product evaluation
Daniel Peterson Engineer, Arizona State University Balance and fall prevention
Rachel Sava Researcher, McLean Hospital Technology; geriatrics
Michael C. Schubert Physical Therapist, Johns Hopkins University Physical therapy
Neal K. Shah CEO, CareYaya Health Technologies Artificial intelligence; technology innovation; business
Shifali Singh M.D., Harvard Medical School Cognition; mood; psychopathology; digital health; speech
Tracy Vannorsdall Neuropsychologist, Johns Hopkins University Cognitive/brain dysfunction; measurement and tracking of cognition and neuropsychiatric symptoms
Gene Wang Founder, Care Daily Artificial intelligence-powered caregivers

If you’re interested in being listed as a researcher for potential collaboration opportunities with industry partners and academics, please take a moment to complete this survey: JH AITC – Future Research Collaboration Permission Form

 

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