ArticleJAMIA open2026
irAE.AI: AI-powered exploration of real-world immune-related adverse events.
Article in JAMIA open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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: We curated an oncology-specific FAERS dataset of ICI-treated cases (2012Q4-2025Q3), standardized tumor, drug, and adverse event fields, deduplicated reports, and generated a flat-file resource ( 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.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.