Is It Possible for Artificial Intelligence in Healthcare to Achieve Genuine Inclusivity?
By
Beth Rush
June 22, 2026
Clinical Scorecard: Is It Possible for Artificial Intelligence in Healthcare to Achieve Genuine Inclusivity?
At a Glance
Category Detail
Condition Artificial Intelligence in Healthcare Key Mechanisms Integration of equity, diversity, and inclusion throughout the AI lifecycle. Target Population Populations facing barriers to accessing care, including unstable housing, migrants, individuals with disabilities, and those with low digital access. Care Setting Health and oral health care systems.
Key Highlights
AI risks reproducing existing disparities unless equity is integrated throughout its lifecycle. The EDAI framework provides guidance for integrating equity, diversity, and inclusion in healthcare. Invisible populations are often underrepresented in AI training datasets. Equity considerations are rarely prioritized in AI model development. Oral health is frequently excluded from digital health innovations. Guideline-Based Recommendations
Diagnosis
Incorporate social determinants of health in AI model development. Management
Utilize the EDAI framework to address barriers at micro, meso, and macro levels. Monitoring & Follow-up
Ensure ongoing evaluation of AI systems for equitable outcomes. Risks
AI systems may exacerbate health disparities if not designed with inclusivity in mind. Patient & Prescribing Data
Vulnerable populations with complex health and social needs.
AI-driven care coordination systems can identify hidden risk factors.
Clinical Best Practices
Engage stakeholders from diverse backgrounds in AI development. Prioritize equity as a core component of AI infrastructure. Provide adequate training and support for frontline healthcare workers using AI tools. Related Resources & Content
