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Gong EJ, Bang CS. Evaluating the role of large language models in inflammatory bowel disease patient information. World J Gastroenterol 2024; 30(29): 3538-3540 [PMID: 39156498 DOI: 10.3748/wjg.v30.i29.3538]
World J Gastroenterol. Aug 7, 2024; 30(29): 3538-3540 Published online Aug 7, 2024. doi: 10.3748/wjg.v30.i29.3538
Evaluating the role of large language models in inflammatory bowel disease patient information
Eun Jeong Gong, Chang Seok Bang
Eun Jeong Gong, Chang Seok Bang, Department of Internal Medicine, Hallym University College of Medicine, Chuncheon 24253, Gangwon-do, South Korea
Author contributions: Gong EJ and Bang CS contributed to conceptualization, methodology, investigation and wrote the original draft; Bang CS reviewed and edited the draft, and contributed to supervision; All authors have read and agreed to the published version of the manuscript.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
Corresponding author: Chang Seok Bang, MD, PhD, Associate Professor, Doctor, Department of Internal Medicine, Hallym University College of Medicine, Sakju-ro 77, Chuncheon 24253, Gangwon-do, South Korea. csbang@hallym.ac.kr
Received: May 27, 2024 Revised: July 15, 2024 Accepted: July 22, 2024 Published online: August 7, 2024 Processing time: 62 Days and 24 Hours
Abstract
This letter evaluates the article by Gravina et al on ChatGPT’s potential in providing medical information for inflammatory bowel disease patients. While promising, it highlights the need for advanced techniques like reasoning + action and retrieval-augmented generation to improve accuracy and reliability. Emphasizing that simple question and answer testing is insufficient, it calls for more nuanced evaluation methods to truly gauge large language models’ capabilities in clinical applications.
Core Tip: This commentary evaluates the article by Gravina et al on ChatGPT’s potential in providing medical information for inflammatory bowel disease patients. While promising, it highlights the need for advanced techniques like reasoning + action and retrieval-augmented generation to improve accuracy, emphasizing that simple question-and-answer testing is insufficient for evaluating large language models’ true capabilities.
