Role for Artificial Intelligence in the Detection of Immune-Related Adverse Events

Abstract

In the article that accompanies this editorial, Sun and colleagues utilized large language models (LLMs) to detect immune-related adverse events (irAEs) from electronic health records and demonstrated that LLMs had higher sensitivity than ICD codes alone (94.7% vs 68.7% respectively) and similar specificity, requiring a fraction of the time compared to manual adjudication. This research represents a significant step in enhancing the efficiency of our efforts to better understand irAEs by leveraging data in the electronic medical records from patients treated with immune checkpoint inhibitors over the last 15 years to better predict these toxicities, with the goal of minimizing or mitigating them entirely.