The 2025–2026 AIM-AHEAD Collaborative Training Programs are now underway, following the official program orientations that took place in December 2025. The AIM-AHEAD All of Us Training Program, AIM-AHEAD Bridge2AI AI-READI Training Program, and AIM-AHEAD Bridge2AI for Clinical Care Training Program held a joint orientation on December 5, 2025, featuring a general AIM-AHEAD overview followed by program-specific breakout sessions to welcome and onboard incoming trainees. The final kickoff for 2025 was the AIM-AHEAD & NCATS Training Program, which held its orientation session on December 18, 2025, welcoming the 50 trainees selected for the program’s third cohort.
The 2025–2026 AIM-AHEAD Collaborative Training Programs are now underway, following the official program orientations that took place in December 2025. The AIM-AHEAD All of Us Training Program, AIM-AHEAD Bridge2AI AI-READI Training Program, and AIM-AHEAD Bridge2AI for Clinical Care Training Program held a joint orientation on December 5, 2025, featuring a general AIM-AHEAD overview followed by program-specific breakout sessions to welcome and onboard incoming trainees. The final kickoff for 2025 was the AIM-AHEAD & NCATS Training Program, which held its orientation session on December 18, 2025, welcoming the 50 trainees selected for the program’s third cohort.
Applications for the four training programs were accepted from September 2 to November 28, 2025. During this application period, AIM-AHEAD received 1,346 program applications from 775 unique applicants across all training programs, reflecting a growing interest in developing expertise in artificial intelligence and machine learning (AI/ML) for research scientists and healthcare practitioners nationwide. Following a thorough review process, a total of 130 trainees were selected and onboarded across the four programs from a highly competitive applicant pool, as indicated by the outcome figures below:
AIM-AHEAD All of Us Training Program - Cohort 3
AIM-AHEAD Bridge2AI AI-READI Training Program - Cohort 2
AIM-AHEAD Bridge2AI for Clinical Care Training Program - Cohort 2
AIM-AHEAD & NCATS Training Program - Cohort 3
Each program will run for eight months, concluding on July 31, 2026, and will offer trainees mentorship, skills training, and opportunities to engage in collaborative and multi-disciplinary AI/ML health research projects.
The AIM-AHEAD Consortium (Data Science Training Core and Communications Hub) has partnered with the National Institutes of Health’s All of Us Research Program, and RTI to offer an eight-month training program designed to advance research in AI/ML using the All of Us data and infrastructure. The program selected a total of 25 trainees (including graduate students, postdoctoral researchers, early-career faculty, and non-academic professionals), and introduced a specialized “Genomics Track” for Cohort 3 which provides additional training in genomic data analysis. Trainees use the Researcher Workbench, a cloud-based platform, to access, extract, and analyze Registered and Controlled Tier data, perform modeling and validation in R, Python, or SAS via Jupyter Notebook, and complete a research project exploring the intersection of AI/ML and health.
Trainees also use the All of Us Dataset Builder to search and organize health information and the Cohort Builder to create, review, and annotate participant cohorts. Projects develop skills in merging and validating data, building supervised models, splitting data for training and testing, identifying anomalies, and performing model validation. Using data collected from various communities, trainees can explore research topics such as non-medical factors influencing health, interactions between family health history and lifestyle, and economic, environmental, and heritable determinants of clinically significant diseases.
Program Co-Directors:
25 Trainees from the following institutions were selected for this program:
|
AIM-AHEAD Regional Hub |
Institutions |
|
Central Hub |
University of Hawaii at Manoa |
|
North/Midwest Hub |
University of Alaska Fairbanks University of Kansas Medical Center Mayo Clinic Iowa State University University of Utah |
|
Northeast Hub |
Columbia University Medical Center Coppin State University Harvard T.H. Chan School of Public Health Harvard University University of Pennsylvania Pitt/Carnegie Mellon MD/PhD Program University of Rochester |
|
South Central Hub |
Baylor College of Medicine Louisiana Health Science Center New Orleans |
|
Southeast-Meharry Hub |
Meharry Medical College National Institute of Environmental Health Sciences Northern Illinois University Vanderbilt University Medical Center University of Virginia Virginia Commonwealth University |
|
Southeast-Morehouse Hub |
Alabama A&M University |
|
West Hub |
University of California San Francisco |
The AIM-AHEAD Bridge2AI AI-READI Training Program engages researchers and students from AIM-AHEAD to use the AI-READI Fairhub.io platform and the AI-READI dataset. The Artificial Intelligence Ready and Exploratory Atlas for Diabetes Insights (AI-READI) project seeks to create a flagship responsibly-sourced dataset to support AI/ML research that provides critical insights into type 2 diabetes mellitus (T2DM), including salutogenic pathways to return to health. The first version of the dataset was released on May 3, 2024, and will be available for program participants during the award period. The program will train up to 25 participants, including graduate students, postdocs, early-career faculty, healthcare professionals, and non-academic professionals, through hands-on use of AI-READI data and the AIM-AHEAD Connect platform.
Trainees will receive training on biomedical research concepts, human subjects research protection, rigor in research, stigma and stigmatizing research, biology and society, social responsibility in research, and the structure and domains of the AI-READI dataset, including retinal imaging, EKG, and environmental sensor data. Technical training will include R, Python, Jupyter Notebooks, model development, cohort creation, concept sets, and analysis of waveform and imaging data. Trainees will apply these skills to develop research proposals for Fairhub, and prepare abstracts, manuscripts, or poster presentations for the Bridge2AI All Hands Conference and AIM-AHEAD Annual Meeting. The program also collects early trainee feedback on the AI-READI dataset to support iterative improvements and integration with other NIH Bridge2AI resources.
Program Co-Directors:
25 Trainees from the following institutions were selected for this program:
|
AIM-AHEAD Regional Hub |
Institution |
|
Central Hub |
N/A |
|
North/Midwest Hub |
Kansas State University University of Utah |
|
Northeast Hub |
Johns Hopkins University New York University State University of New York (SUNY) Upstate Medical University Tritonis, Inc. Yale University |
|
South Central Hub |
Computational Biomedicine Lab at the University of Houston Jackson State University The University of Mississippi Texas Southern University University of Texas Health San Antonio |
|
Southeast-Meharry Hub |
Indiana University North Carolina A&T State University Northern Illinois University Vanderbilt University Medical Center |
|
Southeast-Morehouse Hub |
University of Alabama at Birmingham Savannah State University |
|
West Hub |
University of Arizona University of Arizona College of Medicine Phoenix University of California Los Angeles University of California San Diego University of California San Francisco University of Southern California Stanford University |
The AIM-AHEAD Bridge2AI for Clinical Care Training Program expands access to Bridge2AI AI/ML for Clinical Care data through engagement, training, and mentorship. The program trains up to 30 graduate students, postdocs, early-career faculty, healthcare professionals, and non-academic professionals to apply AI/ML to large, multi-modal datasets and conduct data-driven research at the intersection of AI/ML and healthcare. Trainees gain hands-on experience with high-resolution clinical, imaging, waveform, and environmental data to develop translational AI/ML pipelines and meaningful insights.
Delivered via the AIM-AHEAD Connect platform, the eight-month program combines virtual workshops, live classes, Jupyter Notebook exercises, and instruction on the OHDSI/OMOP common data model and feature engineering. Trainees create practical use cases, participate in team projects, and receive mentorship from AIM-AHEAD and Bridge2AI experts. Program outcomes include research proposals, abstracts, manuscripts, and poster presentations for the Bridge2AI and AIM-AHEAD Annual Meetings, as well as early feedback on the AI/ML for Clinical Care Collaborative Cloud to support iterative improvement. Upon completion, trainees gain advanced AI/ML skills, hands-on experience with complex healthcare datasets, and the ability to conduct translational research that benefits all communities.
Program Co-Directors:
30 Trainees from the following institutions were selected for this program:
|
AIM-AHEAD Regional Hub |
Institution |
|
Central Hub |
N/A |
|
North/Midwest Hub |
N/A |
|
Northeast Hub |
Children's National Hospital Columbia University Medical Center Dartmouth College Faith Based Genetic Research Institute Independent University of Maryland College Park Yale University |
|
South Central Hub |
Baylor College of Medicine BrainCheck Inc. Houston Methodist Research Institute Ochsner Health System Rice University University of Texas at Arlington UTHealth Houston School of Public Health |
|
Southeast-Meharry Hub |
Duke University School of Nursing University of Illinois at Chicago University of Michigan The Ohio State University Vanderbilt University School of Medicine Winston-Salem State University |
|
Southeast-Morehouse Hub |
Alabama State Department of Agriculture and Industries Medical University of South Carolina |
|
West Hub |
University of Arizona University of California Berkeley University of California Los Angeles University of California San Francisco New Mexico State University Stanford Health Care |
The central goal of this training collaboration is to increase the use of AI/ML in health research by training individuals from multidisciplinary backgrounds to build generalizable models that generate rigorous, reproducible, and meaningful insights from real-world data. The program equips participants with the skills to analyze large, complex datasets and build translational, practice-ready AI/ML pipelines that support high-value health research.
The AIM-AHEAD Consortium’s Leadership Core and Communications Hub partnered with the National Center for Advancing Translational Sciences (NCATS) at the National Institutes of Health (NIH) to develop a traineeship in advanced data analysis. This traineeship is designed to equip trainees across America with the knowledge and skills to effectively use the strengths of artificial intelligence and machine learning with large real-world health databases to improve health and health care for all communities.
Trainees complete foundational online courses, participate in live classes, and gain hands-on experience through a guided research project using the N3C Education Training Dataset, which contains over 446,000 records reflecting the complexity of real health data from hospitals, clinics, and agencies. This intensive eight-month program trains 50 professionals to apply AI/ML methods effectively, unlocking insights that can improve health outcomes for all Americans.
Program Co-Directors:
50 Trainees from the following institutions were selected for this program:
|
AIM-AHEAD Regional Hub |
Institution |
|
Central Hub |
N/A |
|
North/Midwest Hub |
Biorealm, LLC University of Colorado Anschutz Medical Campus University of Iowa University of Nebraska Medical Center University of Utah |
|
Northeast Hub |
Albert Einstein College of Medicine Boston Children's Hospital University at Buffalo Georgetown University School of Medicine Icahn School of Medicine at Mount Sinai Northeastern University Yale University |
|
South Central Hub |
Baylor College of Medicine Texas Tech University Health Sciences Center at El Paso The University of Texas at Austin University of Texas at San Antonio The University of Texas Health Science Center at Houston |
|
Southeast-Meharry Hub |
University of Arkansas for Medical Sciences Case Western Reserve University School of Medicine Cincinnati Children's Hospital Medical Center Duke University Indiana University School of Dentistry University of Michigan University of North Carolina at Chapel Hill Ohio State University Old Dominion University University of Virginia University Hospitals Cleveland Medical Center Vanderbilt University Medical Center |
|
Southeast-Morehouse Hub |
University of Alabama at Birmingham Emory University School of Medicine University of Florida Georgia State University Medical University of South Carolina University of Miami University of Miami Miller School of Medicine University of Puerto Rico, Rio Piedras Campus |
|
West Hub |
University of Arizona University of California Los Angeles University of California San Diego University of California San Francisco University of New Mexico School of Medicine |
Dr. Huang, an Assistant Professor of University of Pennsylvania Perelman School of Medicine Pathology and Laboratory Medicine, will discuss the critical role of building an integrated ecosystem for medical imaging and demonstrate how AI can assist pathologists to enhance diagnostic accuracy and efficiency through multiple AI algorithm development efforts.
Find out more about how AIM-AHEAD CLINAQ Program participants Kalyani Narra (MD) and Dhruvangi Sharma (PhD) are advancing healthcare through AI/ML-driven research.
Register now to attend the coding session two, where Dr. Gordon Gao from Johns Hopkins University will explore practical, AI-assisted approaches to research, innovation, and problem-solving.