Improving radiology report error detection using a multipass large language model: framework development and validation.

Radiology report errors are uncommon, making automated detection prone to false alarms. This retrospective study evaluates a multi-step LLM approach designed to review radiology reports more efficiently. The findings illustrate that the method can reduce false alarms while maintaining the ability to identify potential errors, suggesting that AI tools may have a role in supporting quality assurance processes, rather than replacing radiologist review.