Objective The integration of Large Language Models (LLMs) into cancer research has progressed rapidly, but a comprehensive understanding of global trends, key contributors, and emerging research areas remains lacking. This gap hinders a comprehensive understanding of the development landscape for LLM applications in clinical oncology. Methods A bibliometric analysis was conducted using publications retrieved from the Web of Science Core Collection on March 15, 2026. Eligible studies were limited to English-language articles and reviews published till 2025. Records unrelated to LLMs or cancer, duplicates, retracted publications, and those missing complete metadata were excluded. A total of 896 publications were analyzed using VOSviewer, CiteSpace, and R. ClinicalTrials.gov was searched with the same term, obtaining 29 eligible trials. Results Publication output increased sharply from 2022 to 2025. The USA and China dominated global output, with Germany demonstrating disproportionate citation efficiency relative to volume, and Heidelberg University and Harvard University leading institutionally. Research hotspots converged on LLM benchmarking, domain-specific fine-tuning, multi-omics integration, and perioperative applications. Among 29 registered trials, application areas spanned patient communication, shared decision-making, and care equity outcomes, reflecting a transition from proof-of-concept toward randomized evaluation. Conclusions LLM-driven oncology research has expanded rapidly but remains geographically and institutionally concentrated, with prospective multicenter validation still scarce. Research is transitioning from foundational benchmarking toward fine-tuning, multimodal integration, and clinical deployment. Strengthening cross-institutional collaboration, diversifying trial populations, and developing standardized safety evaluation frameworks are essential for translating bibliometric growth into meaningful advances in cancer diagnosis, treatment, and patient outcomes.
Tian et al. (Sun,) studied this question.