AI tools for heart disease prediction carry serious risks if built on biased data, and deploying them without an equity framework could deepen healthcare disparities for marginalized communities. Algorithmic bias stems from flawed training datasets, poor variable selection and lack of diverse research teams — all of which produce tools that fail the populations who need them most. Responsible AI development demands continuous bias monitoring, external validation across demographic groups and meaningful patient input.