Advancing large language models as patient education tools for inflammatory bowel disease

Abstract

This article evaluates the transformative potential of large language models (LLMs) as patient education tools for managing inflammatory bowel disease. The discussion highlights their ability to deliver nuanced and personalized information, addressing limitations in traditional educational materials. Key considerations include the necessity for domain-specific fine-tuning to enhance accuracy, the adoption of robust evaluation metrics beyond readability, and the integration of LLMs with clinical decision support systems to improve real-time patient education. Ethical and accessibility challenges, such as algorithmic bias, data privacy, and digital literacy, are also examined. Recommendations emphasize the importance of interdisciplinary collaboration to optimize LLM integration, ensuring equitable access and improved patient outcomes. By advancing LLM technology, healthcare can empower patients with accurate and personalized information, enhancing engagement and disease management.

Keywords: Patient education; Inflammatory bowel disease; Large language models; Clinical decision support systems; Health technology ethics; Digital health tools

Core Tip: Large language models offer a groundbreaking approach to patient education for inflammatory bowel disease by providing accurate, personalized, and nuanced information. This article emphasizes the need for domain-specific fine-tuning of large language models, robust evaluation metrics, and their integration into clinical workflows. Ethical concerns, such as algorithmic bias and patient data privacy, and accessibility barriers, including digital literacy gaps, are critical to address. Interdisciplinary collaboration is essential for optimizing these tools to enhance patient engagement and improve health outcomes.