Analyzing Research Trends in the Field of Artificial Intelligence and Health Literacy: A Scientometric Approach

Objective: This study aims to analyze the knowledge structure, scientific trends, and thematic evolution of research on artificial intelligence and health literacy using a scientometric approach.

Methods: This study was conducted using a bibliometric approach. Data were retrieved from the Scopus database without time restrictions. A total of 2,864 documents published between 1983 and 2025 were selected for analysis. The data were imported into Bibliometrix software.

Results: The findings indicate that this field has experienced sustained and accelerated growth with an annual growth rate of 4.95%. The scientific collaboration network shows that the United States, with the highest number of links, is at the center of the network. The co-occurrence map identified four main themes: health education and language models, clinical applications, methodology and technical assessment, and mental health. Thematic evolution analysis revealed three distinct phases: expert systems (1983-2005), digital technologies and modern infrastructures (2005-2018), and generative AI and large language models (2018-2024). In the strategic diagram, the combination of "AI-ChatGPT-health literacy" was identified as a motor theme with high centrality and impact.

Conclusion: The field of AI and health literacy has evolved from expert systems to large language models, and the integration of ChatGPT with health literacy has been identified as a strategic research front.

Keywords: Artificial Intelligence, Health Literacy, Large Language Models, Health Education

Key Message: AI in healthcare has evolved from expert systems to large language models. Advancing this emerging field at the intersection of health literacy requires a comprehensive approach to ethics, interdisciplinary collaboration, and modern infrastructure.