AIM-AHEAD Bridge2AI for Clinical Care Training Program

The central goal of the AIM-AHEAD Bridge2AI for Clinical Care Training Program is to expand Bridge2AI AI/ML for Clinical Care data access through engagement, training, and mentorship, including the use of AI/ML in big data analysis for trainees from a broad range of communities. This effort will allow AIM-AHEAD trainees to conduct novel data-driven research at the intersection of AI/ML and healthcare with a multi-modal array of data elements from an extensive cohort.

AIM-AHEAD Bridge2AI for Clinical Care Trainees at the AIM-AHEAD Annual Meeting
AIM-AHEAD Bridge2AI for Clinical Care Trainees at the AIM-AHEAD Annual Meeting

Program Goals

Apply AI/ML to Clinical Care

Develop advanced expertise in using AI/ML methods to address clinical and healthcare challenges

Develop and Implement AI/ML Use Cases

Design use cases using data and contribute to collaborative research on the Bridge2AI AI/ML for Clinical Care platform

Disseminate Research and Insights

Present findings through posters, conference abstracts, or peer-reviewed publications while contributing feedback to improve the collaborative platform

AIM-AHEAD Bridge2AI for Clinical Care Training Program

Program Trainees

To accomplish this objective, a cohort of up to 30 professionals committed to applying AI/ML to benefit American communities, will complete an intensive 8-month program in advanced statistical
and computational analysis developed by the Bridge2AI team.

Trainees will utilize the resources of the AIM-AHEAD Bridge2AI AI/ML for Clinical Care Network and AIM-AHEAD’s Data Science Training Core.

Cohort 1

Regional Distribution of Awardees by Hub for Cohort 1
Regional Distribution of Awardees by Hub for Cohort 1

Cohort 2

Regional Distribution of Awardees by Hub for Cohort 2
Regional Distribution of Awardees by Hub for Cohort 2
AIM-AHEAD Bridge2AI for Clinical Care Training Program

Program Impact

Upon completing the program, trainees will:

  • Gain expertise in applying AI/ML principles to clinical care, equipping them to conduct the in-depth analysis of large datasets essential for cutting-edge biomedical research.

  • Develop and present practical use cases within Bridge2AI Data Topics and actively engage in collaborative research on the Bridge2AI AI/ML for Clinical Care Collaborative Cloud platform, gaining insights on ethics and privacy preservation.

  • Develop a compelling poster for the AIM-AHEAD Annual Meeting and Bridge2AI Annual Meeting

    An important secondary outcome of these activities is early trainee feedback on the Bridge2AI platform, allowing for immediate improvements to optimize its utility and integration with other Common Fund resources.

Geographical Distribution of Trainees by Institution

AIM-AHEAD Bridge2AI for Clinical Care Training Program

Cohort 1 Outcomes

The following infographic provides an overview of trainee outcomes from Cohort 1 of the AIM-AHEAD Bridge2AI for Clinical Care Training Program. These findings highlight trainee engagement, scholarly achievements, career advancement, and knowledge gains, offering a snapshot of participant experiences and the program's impact throughout the training period. Note: Metric totals may vary based on the number of trainees who responded to individual evaluation questions. Response counts are noted where applicable.

AIM-AHEAD Bridge2AI for Clinical Care Training Program

Cohort 2 Outcomes

The following infographic provides an overview of trainee outcomes from Cohort 2 of the AIM-AHEAD Bridge2AI for Clinical Care Training Program. These findings highlight trainee engagement, scholarly achievements, career advancement, and trainee background, offering a snapshot of participant experiences and the program's impact throughout the training period. Note: Metric totals may vary based on the number of trainees who responded to individual evaluation questions. Response counts are noted where applicable.

Program Inquiries Have a question? Reach out to us by creating a Helpdesk ticket. Create a Helpdesk Ticket
Program Directors Toufeeq Syed, PhD, MS; Eric S Rosenthal, MD; Nawar Shara, PhD; Riyz Basha, PhD; Robert Mallet, PhD; Azra Bihorac, PhD; Xiaoqian Jiang, PhD; Manlik Kwong
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