Artificial Intelligence in Immune Checkpoint Inhibitor Research: A Bibliometric Analysis of the Landscape

This bibliometric analysis examines the transformative role of artificial intelligence (AI) in immune checkpoint inhibitor (ICI) research. Analyzing 597 publications from the Web of Science Core Collection (2016–2025), we reveal a dramatic rise in AI-related ICI studies, led by the USA and China. Key findings demonstrate AI's integration in predicting treatment response, optimizing dosing strategies, and managing immune-related adverse events. Through keyword co-occurrence and citation analyses, we identify critical AI applications including novel gene target discovery, drug structure design, and multimodal data fusion for personalized immunotherapy. While highlighting AI's potential to bridge preclinical research and clinical practice, we emphasize the need for interpretable models, robust validation, and ethical frameworks to ensure equitable clinical translation. This comprehensive overview provides valuable insights into research trends and future directions for AI-driven innovations in cancer immunotherapy.