Despite the promising potential of Artificial Intelligence (AI) models to enhance health equities for persons with disabilities, poorly designed applications risk exacerbating inequities. To address this, we conducted a narrative review using disability justice frameworks as an analytical lens to evaluate how AI applications operationalize disability and health equity across various domains. AI models demonstrate potential in early intervention, personalized care, equitable resource allocation, assistive technologies, and health surveillance. However, most AI models rely primarily on biomedical and functional data, often trained on biased datasets that neglect social and structural determinants of disability. As a result, these applications insufficiently capture broader dimensions of wellbeing, including capabilities, recognition, and structural justice, limiting their effectiveness in addressing health inequities. To advance health equity for persons with disabilities, AI applications must incorporate disability-inclusive datasets, participatory co-design with persons with disabilities, auditing for fairness, and real-world validation. Integrating clinical, social, and structural dimensions in a justice-oriented framework can help AI models move beyond narrow biomedical models toward more inclusive, context-sensitive, and equitable health systems.