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Large Language Models in Gastroenterology: Systematic Review.

Eun Jeong Gong^1,2,3, Chang Seok Bang^1,2,3, Jae Jun Lee³

¹Department of Internal Medicine, Hallym University College of Medicine, Chuncheon, Republic of Korea.

Journal of Medical Internet Research

|December 20, 2024

Summary

This summary is machine-generated.

Large language models (LLMs) show potential in gastroenterology for improving diagnostics and automating documentation. Addressing challenges in data privacy and accuracy is key to their successful integration into endoscopy practices.

Keywords:

AI LLM accuracy artificial intelligence clinical practice clinical reasoning data privacy deep learning diagnosis diagnostic emotional support endoscopy gastroenterology large language model patient engagement systematic review

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Area of Science:

Medical Informatics
Artificial Intelligence in Medicine
Gastroenterology

Background:

Healthcare is advancing with AI integration, particularly large language models (LLMs).
LLMs offer capabilities in understanding and generating human language for clinical applications.
These models present opportunities to enhance patient care and operational efficiency in medicine.

Purpose of the Study:

To systematically review the role of LLMs in gastroenterology and gastrointestinal endoscopy.
To assess LLM applications in improving diagnostic accuracy, automating documentation, and enhancing specialist education and patient engagement.

Main Methods:

Systematic review of studies on LLMs in gastroenterology and gastrointestinal endoscopy.
Searched MEDLINE, Embase, and Cochrane Central databases (inception to April 2024).
Included English, full-text studies investigating LLM potential, excluding case reports and basic research; Risk of Bias tool used for quality assessment.

Main Results:

21 studies were included, with a narrative synthesis due to heterogeneity.
LLMs demonstrated ability in providing medical information, consultation advice, report generation, and diagnostic support.
Key challenges identified include data privacy, accuracy concerns, and the need for interdisciplinary collaboration.

Conclusions:

LLMs offer significant potential to transform gastrointestinal endoscopy practices.
Navigating challenges related to data privacy, accuracy, and collaboration is crucial for effective LLM implementation.