Assessing the accuracy of responses by the language model ChatGPT to questions regarding bariatric surgery
Open accessAbstract
PURPOSE: ChatGPT is a large language model trained on a large dataset covering a broad range of topics, including the medical literature. We aim to examine its accuracy and reproducibility in answering patient questions regarding bariatric surgery. MATERIALS AND METHODS: Questions were gathered from nationally regarded professional societies and health institutions as well as Facebook support groups. Board-certified bariatric surgeons graded the accuracy and reproducibility of responses. The grading scale included the following: (1) comprehensive, (2) correct but inadequate, (3) some correct and some incorrect, and (4) completely incorrect. Reproducibility was determined by asking the model each question twice and examining difference in grading category between the two responses. RESULTS: In total, 151 questions related to bariatric surgery were included. The model provided "comprehensive" responses to 131/151 (86.8%) of questions. When examined by category, the model provided "comprehensive" responses to 93.8% of questions related to "efficacy, eligibility and procedure options"; 93.3% related to "preoperative preparation"; 85.3% related to "recovery, risks, and complications"; 88.2% related to "lifestyle changes"; and 66.7% related to "other". The model provided reproducible answers to 137 (90.7%) of questions. CONCLUSION: The large language model ChatGPT often provided accurate and reproducible responses to common questions related to bariatric surgery. ChatGPT may serve as a helpful adjunct information resource for patients regarding bariatric surgery in addition to standard of care provided by licensed healthcare professionals. We encourage future studies to examine how to leverage this disruptive technology to improve patient outcomes and quality of life.
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The accuracy and completeness scoring system was based on recommendations from LeapFrog, The Joint Commission, the American College of Surgeons, and relevant literature.
“To evaluate accuracy and completeness, we developed a scoring system based on recommendations from LeapFrog (VTech Group), The Joint Commission, the American College of Surgeons, and relevant available literature (Multimedia Appendix 2) [21-24].”
From Transforming Informed Consent Generation Using Large Language Models: Mixed Methods Study (2025) · cites this paper as [23]
The Methods > Question Grading section describes a scale that distinguishes accurate, comprehensive answers from correct but incomplete ones. It does not say the scale was based on recommendations from LeapFrog, The Joint Commission, or the American College of Surgeons, or on the cited literature. No page number is available in our copy. (LLM, conf=0.84)
Experimental — accuracy not yet measuredThe findings of mixed performance align with similar reports of mixed performance across different tasks and clinical text datasets within cancer type-specific domains.
“These findings align with similar reports of mixed performance across different tasks and clinical text datasets within cancer type-specific domains [45-47]”
From Large Language Model Applications for Health Information Extraction in Oncology: Scoping Review (2025) · cites this paper as [46]
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- Authors
- Samaan, J. S., Yeo, Y. H., Rajeev, N., Hawley, L., Abel, S., Ng, W. H., Srinivasan, N., Park, J., Burch, M., Watson, R., Liran, O., & Samakar, K.
- Affiliations
- Cedars-Sinai Medical Center (US)
- University of Southern California (US)
- University of Bristol (GB)
- Corresponding author
- Samaan, Jamil S.
- ORCID
- Venue
- Obesity Surgery
- Published
- 2023-04-27
- Volume
- 33
- Issue
- 6
- Pages
- 1790-1796
- ISSN
- 0960-8923 (ISSN-L)
- 1708-0428
- PMC
- PMC10234918
- OpenAlex
- W4367175507
- Cited by
- 282
- References
- 25
- Citations by year
- 2026: 39 · 2025: 87 · 2024: 115 · 2023: 41
- FWCI
- 11.23
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Obesity Surgery (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Cedars-Sinai Medical Center
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Surgery › Bariatric Surgery and Outcomes
- Keywords
- ChatGPT, large language models
- MeSH terms
- Humans, Language, Obesity, Morbid, Quality of Life, Reproducibility of Results, Bariatric Surgery
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