Identification of Online Health Information Using Large Pretrained Language Models: Mixed Methods Study

• Published in: Journal of medical Internet research Vol. 27; no. 5; p. e70733
• Main Authors: Tan, Dongmei, Huang, Yi, Liu, Ming, Li, Ziyu, Wu, Xiaoqian, Huang, Cheng
• Format: Journal Article
• Language: English
• Published: Canada Journal of Medical Internet Research 14.05.2025; Gunther Eysenbach MD MPH, Associate Professor; JMIR Publications
• Subjects: Ability; Accuracy; Adaptability; Artificial Intelligence; Authenticity; Cancer; Chatbots; Conspiracy; Consumer Health Information; Content analysis; COVID-19 vaccines; Credibility; Cultural factors; Data collection; Datasets; Discrepancies; Evaluation; False information; Food; Health information; Health services; Healthy food; Humans; Identification; Infants; Information; Interfaces; Internet; Language; Large language models; Machine learning; Maternal and infant welfare; Medical decision making; Misconceptions; Misinformation; Natural Language Processing; Nutrition; Online health care information services; Original Paper; Pandemics; Public health; Reliability; Social networks; Text categorization; Topics; Verification; China; latent Dirichlet allocation; online health information; large pretrained language models; ChatGPT; performance evaluation; text similarity analysis; artificial intelligence; text generation; information identification

Online health information is widely available, but a substantial portion of it is inaccurate or misleading, including exaggerated, incomplete, or unverified claims. Such misinformation can significantly influence public health decisions and pose serious challenges to health care systems. With advanc...