Review highlights the need for standardized metrics in evaluating large language models for medical applications, suggesting new avenues for future research.
Key Points
Robust evaluation of large language models is crucial for ensuring their safety and reliability in medical settings, and addressing current challenges is essential.
Current evaluations often lack standardized performance metrics and sufficient use of real patient data, affecting their effectiveness and applicability.
A proposed taxonomy categorizes medical applications of large language models, facilitating better understanding and guiding future research directions.
Existing natural language processing evaluations do not adequately assess the quality of text produced by large language models, indicating further evaluation needs.