irAE.AI: AI-powered exploration of real-world immune-related adverse events

Objectives: Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment, but their clinical benefit is limited by the onset of immune-related adverse events (irAEs). Real-world pharmacovigilance data, such as the FDA's Adverse Event Reporting System (FAERS) database, offer the scale needed to better characterize and understand these toxicities. However, extracting cohorts and normalizing complex fields requires expertise in data preprocessing, technical terminology harmonization, and programming. Materials and Methods: = 71 175 irAE cases). To broaden accessibility, we developed a large language model (LLM)-powered analytics assistant that translates natural language queries into executable Python code for cohort filtering, visualization, and statistical testing. Our system also leverages retrieval-augmented generation (RAG) to return clinical guideline-grounded responses to questions about irAEs and their management for research purposes. Results: We benchmarked the system on curated tasks (question intent classification, TableQA, statistics, plotting) using multiple LLMs and report model- and task-specific performance metrics. As a case study, the platform recovered established irAE trends including differences in irAE patterns between anti-CTLA-4 treatment and other immunotherapy regimens and tumor-specific differences in irAE profiles following anti-PD-1 treatment. Discussion: Our results show that LLM-driven analytics can reliably translate natural-language queries into reproducible analyses, though model choice affects accuracy across tasks. Conclusion: This platform provides a transparent, user-friendly approach for exploring real-world immunotherapy safety data to support hypothesis generation, candidate biomarker identification, and irAE risk assessment in immuno-oncology.