Introduction: Large language models (LLMs), exemplified by ChatGPT and similar artificial intelligence platforms, are rapidly reshaping healthcare delivery, medical education, and scientific research. In gastroenterology, these technologies have the potential to support clinical decision-making, streamline documentation, enhance endoscopy workflows, and improve academic productivity.
Introduction:
Large language models (LLMs), exemplified by ChatGPT and similar artificial intelligence platforms, are rapidly reshaping healthcare delivery, medical education, and scientific research. In gastroenterology, these technologies have the potential to support clinical decision-making, streamline documentation, enhance endoscopy workflows, and improve academic productivity. Despite their growing visibility, real-world data on how gastroenterologists are using LLMs in daily practice have been limited.
Problem Statement:
The widespread adoption of LLMs has occurred faster than the development of formal training programs, governance frameworks, and specialty-specific guidance. Understanding current patterns of use, perceived benefits, concerns, and barriers is essential for ensuring safe and effective integration of these technologies into gastroenterology practice.
Summary:
The EuroGI-AI project provides the largest assessment to date of LLM adoption among European gastroenterologists. The survey demonstrated that more than half of respondents already use LLMs in their clinical or academic activities, with many engaging with these tools on a weekly or daily basis. Educational purposes, clinical decision support, and scientific writing emerged as the most common applications. Among academic users, literature summarization and language refinement were particularly valued, highlighting the growing role of AI in research productivity. Most respondents considered LLMs reliable and believed they improve professional efficiency and clinical outcomes. Notably, many clinicians also recognized potential applications within endoscopy practice. However, concerns regarding inaccuracies remained common and were associated with reduced trust in AI-generated outputs. Despite widespread use, formal training was uncommon, yet there was overwhelming support for structured educational programs and incorporation of AI competencies into gastroenterology curricula. Institutional restrictions, financial costs, technological complexity, and time limitations were identified as key barriers to broader adoption. Overall, the findings suggest that LLMs are rapidly becoming integrated into European gastroenterology practice. Future success will depend on specialty-specific development, structured training, robust governance frameworks, and ongoing evaluation to ensure safe, ethical, and clinically meaningful implementation of AI technologies.
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