Eliminating the AI digital divide by building local capacity.

Area of Science:

• Health Informatics
• Artificial Intelligence in Healthcare
• Health Equity

Background:

• Health delivery organizations (HDOs) are increasingly adopting artificial intelligence (AI) tools for clinical and operational improvements.
• Significant disparities exist in AI adoption capabilities between academic medical centers and lower-resource settings.
• This resource gap hinders equitable access to AI benefits, impacting patient care and operational efficiency.

Purpose of the Study:

• To identify the growing divide in AI product lifecycle management capabilities among HDOs.
• To propose a scalable solution for equitable AI adoption across diverse healthcare settings.
• To advocate for targeted public investment to support nationwide AI capacity building.

Main Methods:

• Analysis of AI adoption trends and resource disparities in HDOs.
• Examination of historical technological adoption patterns (EHR, telehealth) and government responses.
• Proposal and evaluation of a hub-and-spoke network model for technical, regulatory, and legal support.

Main Results:

• A significant gap exists in the resources and capabilities required for AI product lifecycle management.
• Previous government interventions, like centers of excellence, successfully addressed adoption disparities.
• A hub-and-spoke network model shows promise in providing essential support services to HDOs.

Conclusions:

• Widespread and meaningful adoption of AI in healthcare requires addressing resource and capability gaps.
• Hub-and-spoke networks, supported by public investment, can facilitate equitable AI implementation.
• Coordination among all health AI ecosystem stakeholders is crucial for safe and effective AI deployment.