Objective:

To explore how AI systems in healthcare can be designed to avoid perpetuating existing health and social inequities.

Approach:

Key Findings:

• AI in healthcare risks reproducing existing disparities unless equity, diversity, and inclusion are integrated throughout the AI life cycle.
• The EDAI framework provides actionable guidance at micro, meso, and macro levels for integrating equity, diversity, and inclusion in health care.
• Populations facing barriers to care are often underrepresented in datasets used to train AI systems, leading to 'invisible populations'.
• Incorporating equity-related factors can improve AI model performance, but these considerations are rarely prioritized.

Interpretation:

The underprioritization of equity in AI development is often treated as optional rather than essential for safe AI development.

Limitations:

• Existing AI systems may not adequately address social determinants of health.
• Implementation challenges include workforce readiness and inconsistent access to AI tools.

Conclusion:

The path forward for existing AI systems requires intentional strategies to ensure equitable health care delivery.

Source

Based on findings from:

Can AI in Health Care Be Truly Inclusive?

Beth Rush. Journal Of Medical Internet Research, 2026.

https://www.jmir.org/2026/1/e104527

Attribution Notice

This content is an AI-generated, fully rewritten summary based on a published scholarly article. It does not reproduce the original text and is not a substitute for the original publication. Readers are encouraged to consult the source for full context, data, and methodology.