AIM-AHEAD 
Bridge2AI for Clinical Care
Training Program
Cohort I

Informational Webinar

November 6, 2024, 3:00pm CT/4:00pm ET

AIM-AHEAD Consortium

Introduction

The Artificial Intelligence/Machine Learning Consortium to Advance 
Health Equity and Research Diversity (AIM-AHEAD) program was 
established by the National Institutes of Health (NIH).

Purpose

The purpose of AIM-AHEAD is to enhance diversity in the field 
of artificial intelligence and machine learning (AI/ML), with 
emphasis on reducing health disparities and promoting 
health equity. 

This will be achieved by engaging in a fair, equitable, and 
transparent process of building a consortium of AI/ML partners 
to promote health equity and an inclusive and diverse 
workforce.  

The AIM-AHEAD Coordinating Center

Introduction
The A-CC consists of four cores, focused on various initiatives to achieve AIM-AHEAD’s mission. 

Leadership Core

Data Science Training Core

Lead, recruit, and 
coordinate the AIM-AHEAD 
Consortium

Assess, develop, and 
implement data science 
training curriculum

Data and Research Core

Address research priorities 
and needs to form an 
inclusive basis for AI/ML

Infrastructure Core

Assess data, computing, and 
software infrastructure to facilitate 
AI/ML and health disparities 
research

Bridge2AI Consortium

Data

Ethics

People

Diverse
FAIR
AI-ready

Accurate
Reliable
Ethically Sourced

Diverse teams
Diverse research cohorts
Training

Generate new data & best 
practices to:

● Propel modern AI/ML 

models to pioneer new 
science, 

● Advance a new culture of 
ethical considerations 
for data, and 

● Create a modernized 

workforce that is skilled 
in this new method of 
scientific data creation.

NIH Leadership Team

Samson Gebreab, Ph.D. MSc. 
Program Lead, AIM-AHEAD
Office of Data Science 
Strategy,
NIH

Dr. Emir Khatipov
Program Director, 
AIM-AHEAD
Office of Data Science 
Strategy,
NIH

Shurjo K. Sen, Ph.D.
Program Director, 
Bridge2AI
Office of Genomic Data 
Science,
NIH

Eva Lancaster, Ph.D.  
Program Director, 
AIM-AHEAD
Office of Data Science 
Strategy,
NIH

Haluk Resat Ph.D.
Program Lead, 
Bridge2AI
Office of Strategic 
Coordination
NIH

Christian Evans, PMP 
Program Specialist, 
AIM-AHEAD
Office of Data Science 
Strategy,
NIH

AIM-AHEAD  Leadership Team

Jamboor 
Vishwanatha, PhD
UNT Health Science 
Center
AIM-AHEAD PI

Toufeeq A. Syed, PhD
University of Texas Health 
Science Center, 
Houston, TX
AIM-AHEAD MPI

Nawar Shara, PhD
MedStar Health 
Research Institute
AIM-AHEAD DSTC MPI

Bridge2AI CHoRUS Leadership Team

Program Leads

Eric 
Rosenthal
MGH

Azra Bihorac
UF

Xiaoqian 
Jiang
UT Health

Yulia 
Strekalova
UF

Parisa 
Rashidi
UF

Andrew 
Williams
Tufts

Program Purpose 

Expand Data Access: Increase access to Bridge2AI AI/ML for Clinical 
Care datasets, especially for underrepresented trainees.

Engagement and Training: Provide engagement, training, and 
mentorship opportunities for trainees on AI/ML and big data analysis.

Purpose

Focus on Health Disparities: Empower trainees to conduct data-driven 
research addressing health disparities.

Multi-Modal Data Use: Equip trainees to work with diverse, multi-modal 
datasets from a broad cohort to conduct impactful AI/ML research.

Program Partnership

Strategic Partnership: AIM-AHEAD and Bridge2AI collaborate to provide 
specialized AI/ML training for clinical care, leveraging shared resources and 
expertise.

Combined Expertise: AIM-AHEAD's strength in diverse trainee recruitment and 
Bridge2AI’s AI data and curriculum drive a comprehensive training experience.

Partnership

Focus on Underrepresented Communities: Jointly committed to expanding AI/ML 
proficiency in communities historically underrepresented in biomedical research.

Goal: Develop a skilled, diverse workforce prepared to advance health equity 
through AI/ML applications in clinical care.

Bridge2AI for Clinical Care Dataset

Multicenter

Multimodal &
High-Resolution

CHoRUS Dataset

• Retrospective data collection
• Controlled access 
• As of November 2024, covers 14 

different hospitals with 23.4K unique 
admissions
• OMOP and telemetry in enclave except:

• Clinical notes – stored locally except 

•

tokens
Imaging – de-id in process at this 
point

• EEG – extraction in process at this 

point

• Datasets are being used for training 

activities and publications

Data type

Data 
standard

Demographics OMOP

OMOP

Controlled

Controlled

Access control

Metadata

Yes

Yes

Yes

Yes

OMOP

Controlled

OMOP

Controlled

Medication administration 
(dosing time-stamped upon 
each infusion change or 
dose administration)

Procedures (documentation 
by providers)

Nursing flowsheets 
(high-frequency 
documentation)

Diagnoses (documentation 
by providers)

Clinical notes (extracted 
and tokenized using OHNLP 
toolkit)

Imaging (from PACS)

DICOM

Waveform telemetry 
(bedside monitors, 
gateway/middleware)

WFDB

Controlled

Controlled

Planned

Yes

Published metadata 
schema

Yes (OMOP schema)

Yes (OMOP schema)

Yes (OMOP schema)

Yes (OMOP schema with 
extensions)

Yes (OHNLP open source 
schema)

Yes (DICOM schema)

Yes (PhysioNet schema 
extended)

OMOP

Controlled

Yes

Yes (OMOP schema)

OHNLP

Controlled

Planned

http://doi:10.1007/s12028-024-02007  

Waveform EEG (hospital 
database)

EDF+ and 
Persyst

Controlled

Planned

Yes (open source EDF+ and 
Persyst schema)

CHoRUS Dataset

CHoRUS Dataset

CHoRUS Dataset

CHoRUS Dataset

Foundational Hands-On Training

Gain experience with AI/ML in the Bridge2AI CHoRUS ecosystem
Learn fundamental skills, tools, and design patterns for applying AI to clinical problems.

Asynchronous
Jupyter Notebooks

Synchronous
Office Hours

Hybrid Workshop:
AI in Clinical Care

Python and Version Control

Structured EHR Datasets

Virtual meetings
with AI experts

Guided coding exercises

ML for Clinical Applications

Notebook review

Hands-on brainstorming

Clinical Deep Learning

Ethics of Clinical AI

Open Q&A

Community team building

Training Overview

Trainees will receive hands-on training on the Bridge2AI AI/ML for Clinical Care 
Network and leverage the data and tools to create practical use cases, putting 
their new skills to work in real-life situations and innovative data-driven 
research. Training will include:

Workshops on using Jupyter 
Notebooks

Didactics on generative AI 
and specific use cases

Ongoing mentorship and 
support using Collaborative 
Cloud platforms

Workshops on using the OHDSI 
tool stack

Instruction on creating 
practical use cases

Workshops on the 
OHDSI/OMOP common data 
model

Virtual live courses

Hands-on training on the 
Bridge2AI AI/ML for Clinical 
Care Collaborative Cloud

Program Trainee Objectives

Objective 1

Exhibit advanced expertise in AI/ML principles as they are applied to clinical care.

Objective 2

Develop and present use cases suitable to apply in Bridge2AI Data Topics.

Objective 3

Participate directly in joint research and development projects on the Bridge2AI AI/ML for 
Clinical Care Collaborative Cloud platform, utilizing the expertise and insights gained from the 
program and interfacing with the BRIDGE Center ethics expertise in AI/ML biases and privacy 
preservation.

Objective 4

Prepare a compelling poster presentation for the AIM-AHEAD Annual Meeting and the 
Bridge2AI Annual Meeting in 2025, submit an abstract for a health informatics conference, 
or develop a manuscript for a peer-reviewed journal.

 After completing the program, trainees will understand how to develop real-world use cases and how to address 
ethical concerns such as bias and privacy. They will be equipped to engage in collaborative research on the 
Bridge2AI platform, and will join a committed community of professionals dedicated to extending AI/ML benefits to 
underrepresented communities in biomedical research.

Outcome

Curriculum Overview

Examples

Hosts: MGB, UF, UTH, Tufts

Host

Lecturer(s)

Delivery

Approach

Topic and Description

Delivery: Live Online, Recorded, 
Asynchronous

MGB

Morteza 
Zabihi

Live Online

Didactic

Machine Learning Basics - Intro to ML 
methods for AI

Format: Didactic, Workshop, 
Office Hours, Self-Directed

UF

Zhenhong Hu Self-Directed

Python 
Notebook

Intro to Python & Version Control

UTH

Debora 
Simmons

Recorded

Lecture

Ethics of AI in Clinical Practice - 
Safety, risk, and legal considerations

Tufts

Andrew 
Williams

Live Online

Didactic

Working with EHR Data for Research

MGH

Aliyah Geer

Workshop

Collaborative

Data Schemas in Clinical Cloud

Curriculum Overview

AI-LEARN Curriculum for Bridge2AI diverse learning communities 

Courses to leverage & Sync DSTC_MHRI Workshops

Curriculum Offering

Target Audience / 
Purpose

Key Topics

Format: Online, self-paced 
with video lectures, case 
studies, and exercises. 

AI/ML Essentials for 
Healthcare

No coding; 
healthcare workers

Intro to AI/ML, ethics, patient engagement, 
health equity

Certification: Available upon 
completion. 

Open Data Science 
for All

Beginners to 
intermediate 
learners

Advanced 
Decision-Making 
Models

Focus on model 
selection for 
healthcare

Data science basics, supervised/unsupervised 
learning, AI trust, healthcare applications

Statistical modeling, decision trees, healthcare 
use cases

Cutting Edge AI 
Training Modules

Keeping up with AI 
advancements

Integration with DSTC_MHRI workshops, latest 
AI/ML trends

Requirements for Accessing Data

Registration

Participants will fill out a registration form with name, email, and institution

Email must be an institution email not a personal

Once access is granted you will receive an email with how to gain access

Licensing Agreement

All participants must sign a licensing agreement

Trainee Expectations

In order to successfully complete the program, selected trainees must:

Time Commitment: Be able to commit to 8 hours per week (on average) of coursework and 
synchronous class sessions 

Attendance: Attend one virtual, synchronous class session per week (day of the week and 
time TBD)

Assignments: Complete all assigned milestones and goals

Presentation of Work: Attend both the AIM-AHEAD Annual Meeting (July 2025) 
and the Bridge2AI Annual Meeting (May 2025) and present a works-in-progress 
poster. 

*These are both in-person events and a $2,000 travel allowance will be given to each trainee for travel expenses.

Program Benefits

Stipend

An $8,000 stipend upon successful 
completion of trainee milestones

A $2,000 allowance to attend the AIM-AHEAD 
Annual Meeting and the Bridge2AI Annual 
Meeting in 2025 

Support

Support and guidance 
from an experienced 
AIM-AHEAD mentor

Support from the 
AIM-AHEAD Data 
Science Training Core

Direct 1:1 guidance, virtual office 
hours, helpdesk support and 
concierge services supporting R 
and Python coding and the 
OHDSI tool stack

Training

Training on:

●

●

●

●

●

Introductory machine learning and feature engineering

The Bridge2AI AI/ML for Clinical Care Collaborative Cloud

Ethics and Policy issues in AI/ML

AI/ML for Clinical Care canonical Jupyter Notebooks

The OHDSI/OMOP common data model

AIM-AHEAD Mentorship Process

Each trainee will be matched with a mentor who will provide ongoing support 
throughout the training program. Mentors are matched with mentees using the 
Connect Platform.  Mentorship matches are made using: 

AI Algorithm

Administrative 
Matching

Mentor Pool 
Search 

Applicant Eligibility

Citizenship

Must be a U.S. Citizen, Permanent Resident, or Non-Citizen U.S. 
National

Education

Post-baccalaureate and graduate students, early-career 
investigators, or employees with a bachelor’s degree in a related field 

EducationSkills & 

Experience

To ensure success in the training program, applicants must already possess 
certain skills, knowledge and experience. These include:

Practical experience in coding/programming with R or Python

Basic understanding of statistics

Institutional Eligibility

Non-Academic Organizations 

Nonprofits with or without 501(c)(3) status, Tribally derived institutions, or For-Profit 
Businesses

Must be a domestic organization located in the United States and its territories

Higher Education Institutions

Public, Private, HSIs, HBCUs, TCUs, AANAPISI, or NAH Serving Institutions

Must be a domestic institution located in the United States and its territories

Application Requirements

Submission Deadline: November 18, 2024 by 11:59 PM EST

Profile Information: Name, organization, department, position, research area, and contact.

Letters of Support: A supervisor's letter confirming training time and contact info is required, along 
with one faculty recommendation attesting to the applicant's skills and readiness for advanced data 
analytics. 

Transcripts: Official or photocopy of undergraduate and graduate (if applicable).

NIH Biosketch or CV: Max 5 pages.

Statement of Rationale: Max 900 words—goals, research question, coding plan, relevant experience, 
and long-term objectives.

*This is just an overview. Please see the CFA for the full list of application requirements.

Application Process

Applications must be submitted between October 18, 2024 and November 18, 2024 at 11:59 PM EST 

Note: Please use Chrome, Firefox, or Edge browsers. 

1

2

3

4

Familiarize 
yourself with the 
program 
requirements 
outlined in the call 
for applications 

Gather all of the 
required 
application 
materials 

Create an 
account on 
AIM-AHEAD 
Connect  and 
register as a 
“mentee/learner”

Submit 
application for 
review using the 
InfoReady 
platform

Up to 25 trainees will be selected 

Program Timeline

Funding Cycle      2024-2025

Program Length

8 months

CFA Release Date 

October 18, 2024

Application Deadline 

November 18, 2024 by 11:59 PM EST

Notice of Award

January 6, 2025

Program Start Date

January 15, 2025

Bridge2AI Annual Meeting 2025

May 2025

AIM-AHEAD Annual Meeting 2025

July 2025

Questions?

Please see the PDF 
linked in the chat for 
more helpful links 
and resources. 

Scan the QR code 
above to access the 
AIM-AHEAD 
Bridge2AI for Clinical 
Care CFA.

