Large language models (LLMs) play a key role in advancing intelligent healthcare. While LLMs are increasingly applied in medical fields such as disease screening, diagnostic assistance, and health management, there are no evidence-based guidelines for assessing their effectiveness in healthcare. Now, researchers have developed a consensus that provides a systematic and evidence-based evaluation framework to assess effectiveness of LLMs in medical applications. The framework includes scientific evaluation metrics and procedures, providing guidance for model evaluators.

A study by The University of Osaka reveals that people who have had COVID-19 are more likely to wear masks. This is driven not by fear, but by an increased awareness of being a potential "silent carrier." The finding suggests that public health messages based on patients' real experiences, highlighting the risk of asymptomatic spread, could be more effective in encouraging preventive behaviors in the general population for future pandemics.