2025/08/28 by Ubaid Tanzim, Imaad Ali Khan, Matthew Abikenari +6 · 1 voice · 1 citation
Health Professions · Medicine · #Health Literacy and Information Accessibility #Mobile Health and mHealth Applications #Telemedicine and Telehealth Implementation
paper · pdf · doi:10.70389/pjs.100095
openalex publication_date 2025/08/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/22
Limited health literacy represents a critical barrier to effective health care, contributing to poor disease management, increased hospitalization rates, and persistent health disparities, particularly among patients with chronic conditions. This comprehensive review synthesizes current evidence on artificial intelligence (AI)-driven interventions designed to enhance health literacy, examining their theoretical foundations, implementation strategies, and potential impact on patient outcomes. We analyzed AI applications, including large language model (LLM) chatbots, personalized health information systems, and multimodal educational tools, while evaluating theoretical frameworks guiding their development and implementation within health care settings. AI technologies demonstrate significant promise in advancing health literacy through simplified medical communication, personalized content delivery, and accessible round-the-clock health guidance. LLMs effectively debunk health misinformation and provide contextual explanations, while machine learning algorithms enable personalization of educational content to individual patient needs. However, challenges persist regarding accuracy, bias mitigation, privacy protection, and equitable access. AI-supported approaches represent a transformative opportunity to address persistent health literacy barriers. Successful implementation requires careful attention to ethical considerations, human oversight, and integration with existing health care workflows to ensure both effectiveness and safety. Future research priorities include randomized controlled trials, longitudinal outcome studies, and systematic bias auditing to establish evidence-based best practices.