ARTIFICIAL INTELLIGENCE AND DIGITAL HEALTH EQUITY IN PREVENTIVE HEALTHCARE: DEVELOPING AN EXPLAINABLE FRAMEWORK TO IDENTIFY UNDERSERVED POPULATIONS THROUGH INTEGRATION OF HEALTHCARE ACCESSIBILITY, SOCIOECONOMIC CONDITIONS, DIGITAL LITERACY, ENVIRONMENTAL

Abstract

The fast arrival of intelligence (AI) and machine learning (ML) in preventive health gives new ways to spot disease early and give the right help. These tools can also make health gaps bigger when they learn from old data that is unfair. In this review we show an idea that helps find people who are left out by looking at five big areas: how easy it is to get care how well people are doing money‑wise how good people are at using technology what the environment looks like and how strong the local health is. Looking at all the studies, we see that just the usual medical risk marks do not give fair results. We need to add the Digital Determinants of Health (DDOH) small environmental dangers and the big gaps that keep people down to make predictions. The review also looks closely at the challenge of making models that work well. Are easy to understand. We check open‑box models after‑the‑fact tools such as SHAP and LIME and counter‑story explanations while keeping in mind the "Table 2 Fallacy" that mistakes chance links for causes. To fix these method and moral problems we suggest an Integrated Explainable Equity Framework. It brings together kinds of data safely keeps fairness all the way through the model’s life and uses causal XAI rules to check how the model acts. The framework puts people in charge lets communities help design it and uses low‑energy computers so that big health AI does not hurt the planet. We finish by saying that real digital health fairness needs to move from fixing bias after the fact, to guarding fairness from the start. That way explainable AI helps many experts decide together not act on who gets care.