Artificial Intelligence–Assisted Error Detection in Complex Clinical Documentation: Leveraging Large Language Models to Enhance Patient Safety in Oncology

Abstract

Frontier LLMs exhibit superior error-detection capabilities and speed compared with both local models and human specialists, who are inherently time-constrained. Although synthetic data provide a controlled testbed, real-world evaluation across diverse errors and documentation styles remains critical. Advanced LLMs can serve as powerful assistants for clinical documentation reviews, substantially reducing the risk of oversight and clinician workload. Integrating LLM-driven error flagging into electronic health record workflows offers a promising strategy for enhancing documentation accuracy, treatment quality, and patient safety in oncology.

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