Leveraging Artificial Intelligence for Immune Checkpoint Inhibitor Safety: A Scoping Review of Current Applications

Abstract

PURPOSE: To systematically map how artificial intelligence (AI) is being applied to immune-related adverse events (irAEs) induced by immune checkpoint inhibitors (ICIs), and to identify key knowledge gaps and future directions to responsible implementation. METHODS: We conducted a scoping review in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guideline. MEDLINE (Ovid), Embase, and Scopus were searched from January 1, 2015, to August 24, 2025. Eligible studies applied at least one AI method (eg, machine learning, natural language processing) to investigate irAEs. Studies were grouped into three clinical domains: (1) risk prediction, (2) identification/detection, and (3) clinical information/decision support. Data were synthesized narratively and mapped descriptively. RESULTS: 40 studies met inclusion criteria, encompassing 45,897 ICI-treated patients. Most applied AI for risk prediction (n = 27), followed by identification/detection (n = 10) and decision support (n = 3). AI approaches showed promise in detecting irAEs from structured and unstructured data, stratifying patient-level risk, and supporting clinical decision making. However, methodological limitations were common: most studies used retrospective data and lacked external validation, limiting clinical applicability. CONCLUSION: AI shows potential to enhance ICI safety by enabling earlier detection of irAEs, personalized risk prediction, and scalable clinical support tools. To support clinical translation, future research must prioritize external and prospective validation, standardized outcome reporting, and impact evaluation (eg, effects on clinical outcomes and workflows) within robust governance frameworks.

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