Educational Materials for Helicobacter pylori Infection: A Comparative Evaluation of Large Language Models Versus Human Experts
Year: 2025
Type: article
Abstract: Helicobacter pylori infects about half of the global population and is a major cause of peptic ulcer disease and gastric cancer. Improving patient education can increase screening participation, enhance treatment adherence, and help reduce gastric cancer incidence. Recently, large language models (LLMs) such as ChatGPT, Gemini, and DeepSeek-R1 have been explored as tools for producing patient education materials, yet their performance compared to expert gastroenterologists remains under evaluation. This review analyzed seven peer-reviewed studies (2024-2025) assessing LLMs’ ability to answer H. pylori-related questions or generate educational content, evaluated against physician- and patient-rated benchmarks across six domains: accuracy, completeness, readability, comprehension, safety, and user satisfaction. LLMs demonstrated high accuracy, up to 92% in some studies, comparable to or exceeding that of general gastroenterologists and approaching senior specialist levels. However, their responses were often judged incomplete, described as “correct but insufficient.” Readability exceeded the recommended sixth-grade level, though comprehension remained acceptable. Occasional inaccuracies in treatment advice raised minor safety concerns. Experts and medical trainees rated LLM outputs positively, while patients found them less clear and helpful. Overall, LLMs show strong potential to provide accurate, scalable H. pylori education. Enhancing completeness, simplifying language, and ensuring clinical safety are key for their effective integration into gastroenterology patient education.
Source: AI
DOI: 10.3390/ai6120311
Institutions: University of Sassari, Baylor College of Medicine
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $1,930
OpenAlex ID: W4416779855