The convergence of large language models and physician–patient relationship research: a bibliometric analysis

Abstract Objectives This study systematically reviewed publications on natural language processing and large language models in the field of physician–patient relationships using bibliometric methods, and analyzed publication trends, current research status, and thematic evolution. Methods Publications were selected from the PubMed database. Bibliometric research was conducted using CiteSpace, including keyword co-occurrence and clustering, timeline analysis, and keyword burst analysis. Results A total of 128 publications were included. Literature fluctuated at a low level before 2019, but increased markedly following the release of ChatGPT, peaking in 2024. Keyword co-occurrence and clustering identified seven core thematic categories: artificial intelligence, natural language processing, large language models, patient education, clinical decision-making, social media, care experience, and e-Health. Timeline analysis indicated a recent shift toward shared decision-making and emotional support. Keyword burst analysis further revealed that large language models, clinical decision support, and generative AI emerged as prominent topics after 2024. Conclusions The rise of large language models has accelerated research in the physician–patient relationship domain, shifting research hotspots toward human-centered scenarios, particularly in areas such as patient experience, clinical decision support, and e-Health.