The integration of large language models (LLMs) into medicine is set to revolutionize clinical practice, medical research, and patient education. These models demonstrate significant potential in tasks ranging from diagnostic support to summarizing complex medical literature. However, the rapid advancement of LLMs necessitates the urgent development and validation of robust methodologies to ensure their safety, accuracy, and equitable real-world impact. While many studies focus on performance outcomes, a detailed exposition of the methods used for rigorous testing and implementation is often lacking.

This Method Collection will highlight transparent and reproducible methods for the comprehensive evaluation and application of LLMs in healthcare. We invite submissions that present innovative techniques for assessing the performance and impact of these models.

Methodological topics of interest include, but are not limited to:

1. Accuracy and Reliability Testing: Frameworks for benchmarking LLM accuracy across medical specialties and sub-topics; methods for detecting and mitigating hallucinations; and approaches for evaluating the consistency and calibration of model confidence.
2. Clinical Workflow and Usability Studies: Methods for assessing LLM integration into clinical workflows, including user-centered design and usability testing; and methodologies to measure their effects on clinician efficiency and cognitive load.
3. Real-World Impact Assessment: Applications of epidemiological and health services research methods to evaluate the real-world effectiveness and safety of LLM-based interventions, as well as approaches for safety testing in high-stakes clinical scenarios.