Abstract In recent years, with the rapid development of artificial intelligence technology, chatbots have demonstrated significant potential in the medical field, particularly in medical laboratories. This study systematically analyzes the advantages and challenges of chatbots in this field and delves into their potential applications in disease diagnosis. However, the reliability and scientific nature of chatbots are influenced by various factors, including data quality, model bias, privacy protection, and user feedback requirements. To ensure the accuracy and reliability of output content, it is essential to not only rely on legal frameworks such as the EU AI Act for necessary protection but also to employ two assessment tools, METRICS and CLEAR. These tools are designed to comprehensively evaluate the quality of AI-generated health information, thereby providing a solid theoretical foundation and support for clinical practice.
Niu et al. (2025) studied this question.