Artificial intelligence in immune checkpoint inhibitor research: A bibliometric analysis of the landscape

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Not reported; bibliometric study provides descriptive counts and co-occurrence frequencies only

• Primary Outcome: Dramatic rise in AI-related ICI publications from 2015 to 2026, with China and the USA identified as the leading contributing nations; key AI application domains identified include treatment response prediction, dosing optimisation, immune-related adverse event management, digital pathology, and tumour microenvironment characterisation

• Nnt Or Sensitivity: Not applicable; no clinical outcome metrics reported. Bibliometric outputs include publication volume trends, citation network centrality, and keyword co-occurrence frequencies — specific numerical values not available from the abstract

• Confidence Interval: Not reported; not applicable to bibliometric methodology as employed

Clinical Application

The study's findings are directly applicable to research strategy and priority-setting rather than immediate clinical practice. Identification of AI application domains (response prediction, adverse event management) can guide clinicians toward emerging evidence bases to monitor. The bibliometric methodology itself is not a clinical tool. Australia has a well-developed ICI prescribing landscape with multiple agents (pembrolizumab, nivolumab, atezolizumab, durvalumab, ipilimumab) listed on the Pharmaceutical Benefits Scheme (PBS) across numerous indications including melanoma, NSCLC, urothelial carcinoma, and others. The TGA has approved several AI-assisted diagnostic tools in oncology pathology. RACGP and Cancer Australia guidelines increasingly reference biomarker-guided ICI selection. This bibliometric study signals that AI tools for ICI response prediction and immune-related adverse event management are a rapidly growing research priority globally — Australian oncology centres and the Australian Institute of Health and Welfare should monitor this space for translatable clinical tools. The dominance of Chinese and US research output suggests Australian researchers may find collaborative opportunities or gaps to address in this field. Oncologists, haematologists, clinical researchers, and health informaticians working in cancer immunotherapy; research funding bodies and academic institutions seeking to identify priority areas for AI-ICI investment

Abstract

This bibliometric analysis examines the transformative role of artificial intelligence (AI) in immune checkpoint inhibitor (ICI) research. Using VOSviewer, CiteSpace, and Bibliometrix, we analyzed 1,938 publications from the Web of Science Core Collection (2015-2026), revealing a dramatic rise in AI-related ICI studies, led by China and the USA. 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 digital pathology and tumor microenvironment characterization. This comprehensive overview provides valuable insights into research trends and emerging frontiers for AI-driven innovations in cancer immunotherapy.

References

1. 1.Kang, J., Tang, R., Li, D., Ma, L., & Zhang, Z. (2026). Artificial intelligence in immune checkpoint inhibitor research: A bibliometric analysis of the landscape. Human Vaccines & Immunotherapeutics. https://doi.org/10.1080/21645515.2026.2707688