Artificial Intelligence in Patient Education: A review

Patient education is a crucial component of clinical disease prevention and treatment, directly impacting patient treatment adherence, self-management capabilities, and clinical outcomes. Traditional patient education faces challenges such as the need for highly readable materials, untimely updates, lack of personalization, and limited time for healthcare professionals. The emergence of Artificial Intelligence (AI) offers new possibilities for revolutionizing patient education models. Some studies have already observed the application effects of AI in this field. This article reviews recent literature on this progress. Research finds that large language models (LLMs) like ChatGPT and Google Bard demonstrate significant advantages in generating patient education materials. These materials show high accuracy in disseminating basic knowledge about common diseases and providing 24/7 instant information services. However, AI applications still face some challenges. Furthermore, the applicability of AI across different linguistic environments, cultural backgrounds, and special populations requires further validation. Future study should focus on developing specialized LLMs for medicine, establishing effective human-computer collaborative review mechanisms, thus to formulate industry standards and regulatory frameworks, and validate the long-term impact of LLMs on clinical outcomes through prospective studies.