Large Language Models in Gastroenterology: Systematic Review
Author Information
Author(s): de Azevedo Cardoso Taiane, Shung Dennis, Zhang Hao, Gong Eun Jeong MD, PhD, Bang Chang Seok MD, PhD, Lee Jae Jun MD, PhD, Park Jonghyung MS, Kim Eunsil MS, Kim Subeen MS, Kimm Minjae PhD, Choi Seoung-Ho MS
Primary Institution: Hallym University College of Medicine
Hypothesis
This systematic review describes the role of LLMs in improving diagnostic accuracy, automating documentation, and advancing specialist education and patient engagement within the field of gastroenterology and gastrointestinal endoscopy.
Conclusion
The integration of large language models (LLMs) in gastroenterology has the potential to enhance diagnostic accuracy, operational efficiency, and patient care.
Supporting Evidence
- LLMs can improve diagnostic accuracy and streamline documentation in gastroenterology.
- 21 studies were included in the systematic review, highlighting the potential of LLMs.
- Challenges such as data privacy and accuracy need to be addressed for effective implementation.
Takeaway
This study looks at how smart computer programs can help doctors do their jobs better, like figuring out what's wrong with patients and making paperwork easier.
Methodology
A systematic review was performed by searching core databases for studies related to LLMs in gastroenterology, applying specific inclusion and exclusion criteria.
Potential Biases
The overall risk of bias was low in 5 studies and moderate in 16 studies.
Limitations
The primary limitation was the variability in study objectives and outcomes, which prevented a meta-analysis.
Digital Object Identifier (DOI)
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