ArticleJournal of clinical oncology : official journal of the American Society of Clinical Oncology2024
Enhancing Precision in Detecting Severe Immune-Related Adverse Events: Comparative Analysis of Large Language Models and International Classification of Disease Codes in Patient Records.
Article in Journal of clinical oncology : official journal of the American Society of Clinical Oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Hierarchical prompting with reasoning large language models for immune checkpoint inhibitor-associated myocarditis evidence extraction pilot study.European heart journal. Digital health · 2026Article
- Population-Scale Precision Safety in Oncology Reveals Clinical and Genetic Determinants of Systemic Therapy Toxicity.medRxiv : the preprint server for health sciences · 2026Article
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- irAE-GPT: leveraging large language models to identify immune-related adverse events in electronic health records and clinical trial datasets.EBioMedicine · 2026Article
- An agentic AI system enhances clinical detection of immunotherapy toxicities: a multi-phase validation study.medRxiv : the preprint server for health sciences · 2026Article
- Where is the data? Delayed and chronic irAE surveillance and management after cessation of ICIs: expert insights from SITC on survivorship care and the need for long-term data.Journal for immunotherapy of cancer · 2026Article
- Large language models for bioinformatics.Quantitative biology (Beijing, China) · 2026Review
- Risk prediction models for adverse drug reactions in pediatrics: a scoping review.International journal of clinical pharmacy · 2026Article
- Development and Validation of a Large Language Model Case Identification Strategy for Eosinophilic Esophagitis.Gastro hep advances · 2026Article
- Causal AI for cancer immunotherapy: a narrative framework review of target trial emulation, treatment-effect learning and clinical translation.Frontiers in immunology · 2026Review
- Precision oncology: from large language models to multi-agent systems.Frontiers in oncology · 2026Review
- Large Language Models for Drug-Related Adverse Events in Oncology Pharmacy: Detection, Grading, and Actioning.Pharmacy (Basel, Switzerland) · 2025Review
- From digital assistants to clinical partners: revolutionizing pediatric urology through large language model-powered decision support and patient education.World journal of urology · 2025Article
- Large Language Models for Adverse Drug Events: A Clinical Perspective.Journal of clinical medicine · 2025Review
- Reliability of Large Language Model Knowledge Across Brand and Generic Cancer Drug Names.JCO clinical cancer informatics · 2025Article
- Large language model evaluation in autoimmune disease clinical questions comparing ChatGPT 4o, Claude 3.5 Sonnet and Gemini 1.5 pro.Scientific reports · 2025Article
- The baseline circulating immunophenotype characteristics associate with PD(L)-1 targeted treatment response, irae onset, and prognosis.Scientific reports · 2025Article
- Comparing Artificial Intelligence-Generated and Clinician-Created Personalized Self-Management Guidance for Patients With Knee Osteoarthritis: Blinded Observational Study.Journal of medical Internet research · 2025Observational
- The potential of large language models to advance precision oncology.EBioMedicine · 2025Review
Corrections and comments
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30 authors.
Funding
Abstract
purposeCurrent approaches to accurately identify immune-related adverse events (irAEs) in large retrospective studies are limited. Large language models (LLMs) offer a potential solution to this challenge, given their high performance in natural language comprehension tasks. Therefore, we investigated the use of an LLM to identify irAEs among hospitalized patients, comparing its performance with manual adjudication and International Classification of Disease (ICD) codes.
methodsHospital admissions of patients receiving immune checkpoint inhibitor (ICI) therapy at a single institution from February 5, 2011, to September 5, 2023, were individually reviewed and adjudicated for the presence of irAEs. ICD codes and an LLM with retrieval-augmented generation were applied to detect frequent irAEs (ICI-induced colitis, hepatitis, and pneumonitis) and the most fatal irAE (ICI-myocarditis) from electronic health records. The performance between ICD codes and LLM was compared via sensitivity and specificity with an α = .05, relative to the gold standard of manual adjudication. External validation was performed using a data set of hospital admissions from June 1, 2018, to May 31, 2019, from a second institution.
resultsOf the 7,555 admissions for patients on ICI therapy in the initial cohort, 2.0% were adjudicated to be due to ICI-colitis, 1.1% ICI-hepatitis, 0.7% ICI-pneumonitis, and 0.8% ICI-myocarditis. The LLM demonstrated higher sensitivity than ICD codes (94.7%
conclusionLLMs are a useful tool for the detection of irAEs, outperforming ICD codes in sensitivity and adjudication in efficiency.
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