Can AI Bridge the Health Literacy Gap? An Analysis of Requirements and Opportunities

Abstract

One of the most important factors influencing patient outcomes is health literacy (HL), which is the capacity to obtain, comprehend, and use health information. Disparities still exist despite the abundance of digital health resources because of complicated medical terminology, a lack of personalization, and a lack of multilingual support. By utilizing diverse data sources, such as electronic health records (EHRs), online health communities (like Reddit), and medical ontologies (like UMLS, SNOMED-CT), this study examines how artificial intelligence (AI) can close the HL gap. We examine cutting-edge methods like large language models (LLMs) for text simplification (e.g., grade-level adaptation in GPT-4) and natural language processing (NLP) for HL classification (e.g., linguistic profiling in the ECLIPPSE study). We draw attention to issues such as cultural biases in HL evaluation, oversimplification of medical information, and difficulties integrating data. To personalize the delivery of health information, our suggested framework integrates AIdriven methods such as automatic HL level identification, concept mapping, and semantic enrichment. This work attempts to improve accessibility while maintaining clinical accuracy by combining structured (EHRs) and unstructured (social media) data. To guarantee equitable health communication, future directions include multilingual adaptation and real-world validation.

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