Ardila Carlos M, González-Arroyave Daniel, Ramírez-Arbeláez Jaime
Department of Basic Sciences, Biomedical Stomatology Research Group, Faculty of Dentistry, Universidad de Antioquia, Medellín 050010, Antioquia, Colombia.
Department of Periodontics, Saveetha Dental College, and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Saveetha 600077, India.
World J Gastroenterol. 2025 May 28;31(20):105285. doi: 10.3748/wjg.v31.i20.105285.
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.
本文评估了大语言模型（LLMs）作为管理炎症性肠病的患者教育工具的变革潜力。讨论强调了它们提供细致入微和个性化信息的能力，解决了传统教育材料中的局限性。关键考虑因素包括进行特定领域的微调以提高准确性的必要性、采用超越可读性的强大评估指标，以及将大语言模型与临床决策支持系统集成以改善实时患者教育。还探讨了伦理和可及性挑战，如算法偏见、数据隐私和数字素养。建议强调跨学科合作以优化大语言模型集成的重要性，确保公平获取并改善患者治疗效果。通过推进大语言模型技术，医疗保健可以为患者提供准确且个性化的信息，增强患者参与度和疾病管理能力。
