AI for healthcare access and equity

Uses income, distance to clinic, insurance status, and age as predictors.

Trains a simple Random Forest model to classify whether someone faces healthcare access barriers.

Flags patients who may need equity-focused interventions (like mobile clinics, subsidies, or telehealth).

This is just a toy example, but in real-world healthcare AI:

You’d use larger, diverse datasets to avoid bias.

Include social determinants of health (education, housing, language access).

Ensure fairness metrics are applied so the model doesn’t reinforce inequities.

Would you like me to extend this into a fairness-aware pipeline (e.g., checking if predictions are biased across income groups or genders)? That would show how AI can actively promote equity, not just predict barriers.

Built With

• github
• phtyhon