Administrative Core
ABSTRACT The Administrative Core (AC) will serve as the organizational hub of the Johns Hopkins Artificial Intelligence and Technology Collaboratory (JHU AITC), whose purpose is to advance artificial intelligence (AI) and technology solutions for aging and Alzheimer’s disease/Alzheimer’s disease–related dementias (AD/ADRD). Building on five years of successful operations (54 pilot awards, 7 commercialized products, 4 patents or patents-pending, and $11.7 million in external funding) the AC will provide governance, leadership, infrastructure, and accountability to ensure that the Collaboratory’s goals are met. The AC is co-directed by Phillip Phan, PhD, and Peter Abadir, MD, with oversight by the Executive Committee (EC, comprising the Multiple Principal Investigators), guidance from a Scientific Advisory Board (SAB), and day-to-day management by an experienced Executive Director. Specific Aim 1 is to guide the JHU AITC’s vision through interdisciplinary leadership and governance. The AC will coordinate scientific direction, cross-Core integration, fiscal oversight, and transparent decision-making, en- suring accountability to the EC, and SAB. Specific Aim 2 is to establish data standards, resource sharing, and dissemination. Collaborating with the Data Integrity & Computational Methods Core, the AC will implement FAIR (Findable, Accessible, Interoperable, Reusable) principles, create protocols for secure data access, and maintain a public website with training resources, solicitation announcements, and contributions to the Artificial Intelli- gence Technology Coordinating Center (AITCC)’s Living Textbook. Specific Aim 3 is to administer an annual national competition to identify and support innovative AI projects. Collaborating with the Research Innovation Core, the AC will oversee a rigorous two-stage peer review and ethics evaluation, aligned with the NIST AI Risk Management and SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials–Artificial In- telligence) checklists, to ensure scientific and clinical rigor and commercial viability. Specific Aim 4 is to collabo- rate with the AITCC on stakeholder engagement in ethical AI development. The AC will ensure that projects integrate perspectives of older adults, caregivers, clinicians, and industry, emphasizing fairness, privacy, and accessibility, particularly for rural and underserved populations. Specific Aim 5 is to closely partner with the AITCC in communication, reporting, and dissemination. Activities include quarterly and annual reporting, national symposia, publications, and broad engagement with academic, clinical, industry, and community stakeholders. The AC will apply a Donabedian structure–process–outcome framework to ensure reproducibility, accountability, and continuous improvement. Evaluation includes quarterly monitoring, fiscal oversight, benchmarking, and rapid responsiveness to awardees and stakeholders. By ensuring strong integration across Cores, accountability to the NIH/NIA mission, and broad dissemination of resources, the AC will advance a robust portfolio of AI inno- vations. Expected outcomes include enhanced reproducibility, accelerated translation of research into practice, commercial viability, and access to technology-enabled solutions for older adults.
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Imported from NIH RePORTER | Grant 2P30AG073104-06.
Open sourceOverview
Grant agreement 2P30AG073104-06
Open sourceABSTRACT The Administrative Core (AC) will serve as the organizational hub of the Johns Hopkins Artificial Intelligence and Technology Collaboratory (JHU AITC), whose purpose is to advance artificial intelligence (AI) and technology solutions for aging and Alzheimer’s disease/Alzheimer’s disease–related dementias (AD/ADRD). Building on five years of successful operations (54 pilot awards, 7 commercialized products, 4 patents or patents-pending, and $11.7 million in external funding) the AC will provide governance, leadership, infrastructure, and accountability to ensure that the Collaboratory’s goals are met. The AC is co-directed by Phillip Phan, PhD, and Peter Abadir, MD, with oversight by the Executive Committee (EC, comprising the Multiple Principal Investigators), guidance from a Scientific Advisory Board (SAB), and day-to-day management by an experienced Executive Director. Specific Aim 1 is to guide the JHU AITC’s vision through interdisciplinary leadership and governance. The AC will coordinate scientific direction, cross-Core integration, fiscal oversight, and transparent decision-making, en- suring accountability to the EC, and SAB. Specific Aim 2 is to establish data standards, resource sharing, and dissemination. Collaborating with the Data Integrity & Computational Methods Core, the AC will implement FAIR (Findable, Accessible, Interoperable, Reusable) principles, create protocols for secure data access, and maintain a public website with training resources, solicitation announcements, and contributions to the Artificial Intelli- gence Technology Coordinating Center (AITCC)’s Living Textbook. Specific Aim 3 is to administer an annual national competition to identify and support innovative AI projects. Collaborating with the Research Innovation Core, the AC will oversee a rigorous two-stage peer review and ethics evaluation, aligned with the NIST AI Risk Management and SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials–Artificial In- telligence) checklists, to ensure scientific and clinical rigor and commercial viability. Specific Aim 4 is to collabo- rate with the AITCC on stakeholder engagement in ethical AI development. The AC will ensure that projects integrate perspectives of older adults, caregivers, clinicians, and industry, emphasizing fairness, privacy, and accessibility, particularly for rural and underserved populations. Specific Aim 5 is to closely partner with the AITCC in communication, reporting, and dissemination. Activities include quarterly and annual reporting, national symposia, publications, and broad engagement with academic, clinical, industry, and community stakeholders. The AC will apply a Donabedian structure–process–outcome framework to ensure reproducibility, accountability, and continuous improvement. Evaluation includes quarterly monitoring, fiscal oversight, benchmarking, and rapid responsiveness to awardees and stakeholders. By ensuring strong integration across Cores, accountability to the NIH/NIA mission, and broad dissemination of resources, the AC will advance a robust portfolio of AI inno- vations. Expected outcomes include enhanced reproducibility, accelerated translation of research into practice, commercial viability, and access to technology-enabled solutions for older adults.