SOURCE METADATA
Project: CHORUS
Source ID: cohort_2_webinar
Source type: tutorial
Source URL: https://www.aim-ahead.net/media/jnzdnid3/bridge2ai-for-clinical-care-informational-webinar-cohort-2.pdf
Raw file: data/raw/CHORUS/bridge2ai-for-clinical-care-informational-webinar-cohort-2_row9.pdf
--------------------------------------------------------------------------------
AIM-AHEAD Bridge2AI for Clinical Care
Training Program

Cohort 2

Informational Webinar

September 09, 2025, 2:00pm Central

JK


AIM-AHEAD Consortium

AIM-AHEAD  is  a  nationwide  network  of  institutions  and  organizations
designed  to  build  AI  talent  among  researchers  and  clinicians,  support
multidisciplinary  research  projects  that  harness  AI/ML  to  improve  the
health of Americans, and enhance the AI capabilities and infrastructure
of  communities  or  hospitals  that  otherwise  would  not  have  had  the
resources or the capacity to beneﬁt from the advancement of AI/ML.

JK


The AIM-AHEAD Coordinating Center

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

Infrastructure Core

Address research priorities and
needs to form a comprehensive
basis for AI/ML

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

JK


NIH Leadership Team

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

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

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

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

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

JK


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

JK


Bridge2AI CHoRUS Leadership Team

Eric Rosenthal
Massachusetts General
Hospital

Azra Bihorac
University of Florida

Xiaoqian Jiang
UTHealth Houston

Yulia Strekalova
University of Florida

Parisa Rashidi
University of Florida

Manlik Kwong
Tufts University

TS


Program Partnership

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

Combined Expertise: AIM-AHEAD’s strength in trainee engagement and Bridge2AI’s AI data
and curriculum deliver a comprehensive training experience.

Partnership

Focus on Training: The partnership equips trainees with practical skills to apply AI/ML
methods to clinical care challenges using real-world data.

Goal: Build a skilled workforce prepared to advance AI/ML applications in health research.

TS


Program Purpose

The  AIM-AHEAD  Bridge2AI  for  Clinical  Care  Training  Program
expands access to Bridge2AI CHoRUS data through engagement, AI
training, and mentorship. It equips trainees to apply AI/ML methods
innovative  research  at  the
to  big  data  analysis  and  conduct
intersection  of  healthcare  challenges  and  AI/ML  using  multi-modal
datasets from a broad range of cohort participants.

TS


Bridge2AI for Clinical Care Dataset

Multicenter

Multimodal &
High-Resolution

ER


CHoRUS Dataset

• Retrospective data collection

• Controlled access

• As of August 2025, covers 14 different

hospitals with over 45K unique admissions
• OMOP and telemetry in enclave except:

• Clinical notes – stored locally except tokens

•

Imaging – currently 1000 images available
with de-id in process for larger cohort

• 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)

OMOP

Controlled

Yes

Yes (OMOP schema)

OHNLP

Controlled

Planned

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)

Imaging (from PACS)

DICOM

Waveform telemetry
(bedside monitors,
gateway/middleware)

WFDB

Controlled

Controlled

Planned

Yes

Waveform EEG (hospital
database)

EDF+ and
Persyst

Controlled

Planned

Yes (open source EDF+ and
Persyst schema)

ER


Bridge2AI for Clinical Care Dataset

ER


CHoRUS Dataset

ER


CHoRUS Dataset

ER


CHoRUS Dataset

ER


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

ML for Clinical Applications

Clinical Deep Learning

Ethics of Clinical AI

Virtual meetings
with AI experts

Guided coding exercises

Notebook review

Hands-on brainstorming

Open Q&A

Community team building

ER


Accessing the Data

Registration Process

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

Once access is granted and compute is provisioned, an email will be sent

1

2

Licensing Agreement

All participants must sign a licensing agreement included in the registration form before gaining
access to the dataset

*This requirement is not a barrier to acceptance into the program. Program administrators will assist with this access if
needed.

ER


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 and
learning sessions

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

ER


Curriculum Overview

Examples of Learning Sessions Provided:

Host

Lecturer(s)

Delivery

Approach

Topic and Description

MGH

Morteza
Zabihi

Live Online

Didactic

Machine Learning Basics - Intro to ML
methods for AI

Hosts: MGB, UF, UTH, Tufts

Delivery: Live Online,
Recorded, Asynchronous

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

Jared
Houghtaling

Live Online

Didactic

Working with EHR Data for Research

MGH

Siril Singa

Workshop

Collaborative

Data Schemas in Clinical Cloud

ER


Curriculum Overview

AI-LEARN Curriculum for Bridge2AI Broad 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.

Certiﬁcation: Available upon
completion.

AI/ML Essentials for
Healthcare

No coding;
healthcare workers

Intro to AI/ML, ethics, patient engagement

Advanced
Decision-Making
Models

Focus on model
selection for
healthcare

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

MS


Curriculum Overview

AI-LEARN Curriculum for Bridge2AI Broad Learning Communities

Courses to leverage & sync DSTC_MHRI Workshops

Titles

Key Topics

Format

Format: Sync workshops and
panel discussions hosted by
MHRI team

Navigating IRB, Data Compliance,
and Quality Assurance in AI/ML
Healthcare Research

- IRB protocol drafting
- HIPAA/GDPR compliance for OMOP/FHIR data
- QA audit frameworks

Hyperparameter Tuning, Model
Selection, and Deployment for
Healthcare AI

- Compare optimization techniques (e.g.,
Bayesian)
- Model interpretability vs. performance
- Docker deployment demo

Hands-on IRB drafting,
MedStar Program managers/
IRB Chair Q&A, Compliance
checklist toolkit

Live session with real-world
use cases

Bridging the Gap: Clinicians
and Data Scientists on
Methodological Challenges

- Align AI projects with clinical priorities
- Address EHR data limitations

Panel (MedStar clinicians, data
scientists), breakout consultations,
live Q&A

MS


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  in  collaborative  research  on  the  Bridge2AI  Clinical  Care  Cloud  platform,  applying
program insights and engaging with BRIDGE Center expertise for responsible AI/ML.

Objective 4

Prepare a compelling poster presentation for the AIM-AHEAD and Bridge2AI Annual Meetings,
submit  an  abstract  for  a  health  informatics  conference,  and/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
concerns such as privacy in the responsible application of AI/ML. They will be equipped to engage in collaborative
research on the Bridge2AI platform and will connect with a community of professionals dedicated to advancing the
use of AI/ML in biomedical research.

Outcome

MS


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 2026) and the
Bridge2AI Annual Meeting* (May 2026) and present a works-in-progress poster.

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

TS


Program Benefits

Stipend

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

Travel allowances to attend both the
AIM-AHEAD Annual Meeting and the
Bridge2AI Conference in 2026

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/
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.
● Generative AI and speciﬁc use cases.
● Creating practical use cases during Bridge2AI topics.

TS


AIM-AHEAD Mentorship Process

Each  trainee  will  be  matched  with  a  mentor  who  will  provide  mentoring
and  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

TS


Applicant Eligibility

Citizenship
and Tax
Requirements

✓ Must be a U.S. Citizen, Permanent Resident, or Non-Citizen U.S. National
✓ Permanent Residency must be established by Sept. 26, 2025
✓ Temporary visa holders (F1, J1, H1, etc.) are not eligible
✓ Accepted candidates must be able to submit a W-9 form

✓ Current/former AIM-AHEAD program participants (awardees, fellows, trainees,

Participation
Restrictions

mentors, advisors, coaches) are ineligible

✓ Applicants may apply to more than one AIM-AHEAD training program but can only be

selected for one

Special Cases

✓ AIM-AHEAD Coordinating Center personnel and Federal employees may participate,

but will not receive stipend or travel allowance

Note: Please refer to the full CFA on AIM-AHEAD.net for all eligibility requirements.

TS


Education & Experience

Education
Requirements

✓ Minimum: Bachelor’s degree in physical sciences, life sciences, math, statistics, data

science, engineering, health sciences, or public health

✓ Eligible applicants include: post-baccalaureate and graduate students, postdocs, medical
students/residents, allied health trainees, early-career investigators, and early-career
professionals in non-academic institutions

Required Skills

✓ Prior programming experience
✓ Basic understanding of statistics
✓ Working command of English

Recommended
Skills

✓ Coursework in probability and statistics or higher-level math
✓ Coding experience in R or Python
✓ Experience with data manipulation and management through coursework or research

Note: Please refer to the full CFA on AIM-AHEAD.net for all eligibility requirements.

TS


Eligibility Requirements

Eligible Organizations

Higher education institutions

Local, state, and tribal
governments

Nonprofits
(with or without 501(c)(3) status)

For-profit businesses and
organizations

Other U.S.-based organizations

(e.g., school districts, housing
authorities, faith-based,
community-based, and regional
organizations)

Email Requirement

In order to gain access to
the dataset, you will need
to have a “.edu” email
address.*

*This requirement is not a barrier
to acceptance into the program.

Program administrators will
assist with this access if needed.

Note: Please refer to the full CFA on AIM-AHEAD.net for all eligibility requirements.

TS



Application Requirements

Submission Deadline: September 26, 2025 by 11:59 PM EST

Required Application Elements

●
●

●
●
●

Proﬁle Information (basic applicant details in InfoReady portal)
Letters of Support & Recommendation (two letters)

○
○

Supervisor letter conﬁrming protected time
At least one faculty recommendation (additional letters optional)

Academic Transcript (undergraduate and, if applicable, graduate)
Biographical Sketch (NIH biosketch or CV, max 5 pages)
Statement of Rationale (≤2 pages) describing goals, need for training, relevant experience, and long-term plans

Important Note

●
●

Applicants may apply to more than one program but can only be selected for one
Applicants will rank program preferences in the application

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

TS


Application Process

Applications must be submitted between September 02, 2025 and September 26, 2025 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 30 trainees will be selected

TS


Key Program Dates

CFA Release Date

September 02, 2025

Application Deadline

September 26, 2025 by 11:59 PM EST

Notice of Award

November 10, 2025

Program Start Date

November 17, 2025

Bridge2AI Conference 2026

May 2026

AIM-AHEAD Annual Meeting 2026

July 2026

Program End Date

July 31,  2026

TS


Questions?

Please see the FAQ
document  linked
above and in the chat

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

TS
