AIM-AHEAD Hallmarks of Success

Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity

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“Hallmarks of Success” are goals for the AIM-AHEAD Consortium to strive towards. These hallmarks have been aligned to AIM-AHEAD’s four North Stars, and is included in the AIM-AHEAD Coordinating Center (A-CC) evaluation plan. Cores, hubs, and consortium partners will report on how their activities contribute to which hallmark(s) to track progress.

Hallmarks NS I:

  • NS-I.a: Engage a variety of organizations/institutions, including those whose mission is to serve multiple historically underrepresented populations (e.g., ethnic minorities, rural, LGBTQ+) and/or those impacted by health disparities, to expand and support a diverse AI/ML workforce in health.
  • NS-I.b: Develop equitable partnerships with shared goals to expand diversity in AI/ML.
  • NS-I.c: Expose consortium members to AI/ML pathways in health (e.g., professional development, training, workshops).
  • NS-I.d: Create opportunities for those from historically underrepresented groups to use AI/ML.
  • NS-I.e: Develop a diverse network of people at all career levels using AI/ML to reduce health disparities.
  • NS-I.f: Develop AI/ML curriculum modules, other training materials, opportunities for K-12 students, undergraduate, graduate, and faculty training/development (including research opportunities).

Hallmarks NS II:

  • NS-II.a: Establish connections to a diverse group of community consortium members and stakeholders across all regions.
  • NS-II.b: Provide support to develop AI/ML skills and capabilities in communities.
  • NS-II.c: Collaborate with consortium members to leverage strengths, existing resources, and innovation in developing AI/ML training approaches/materials.
  • NS-II.d: Provide evidence of community voices in AI/ML training, research, and other activities.
  • NS-II.e: Foster community members/organizational knowledge and awareness working with AI/ML tools.

Hallmarks NS III:

  • NS-III.a: Provide evidence of AI/ML minority health and health disparity research projects (e.g., publications, abstracts, dissemination).
  • NS-III.b: Demonstrate use cases from AI/ML and health disparities for data sharing, training, and infrastructure.
  • NS-III.c: Provide evidence of successful pilot research projects (e.g., using clinical/biomedical data, EHR, social determinants of health, etc.)
  • NS-III.d: Improve quality, diversity, and balance of data to be representative of historically underrepresented communities for AI/ML.
  • NS-III.e: Provide access to data sources reflective of historically underrepresented communities (e.g., health data, SDoH, etc.).

Hallmarks NS IV:

  • NS-IV.a: Assess barriers to using AI/ML tools to address health disparities.
  • NS-IV.b: Assess near-, medium-, long-term, and emerging AI/ML needs to address health disparities.
  • NS-IV.c: Make AI/ML (e.g., tools, methods, uses in health, etc.) more relevant, accessible, and usable for communities.
  • NS-IV.d: Provide data sharing options options that reflect community consortium member preferences, based on social and technical needs (e.g., options for distributed/federated learning).
  • NS-IV.e: Promote sustainable AI/ML infrastructure for curating, accessing, and analyzing data (e.g., integrated platforms/services or cloud computing).
  • NS-IV.f: Demonstrate evidence of training, mentoring, and fellowship opportunities to build community capacity in AI/ML.
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