ArticleSurgical endoscopy2024
The performance of artificial intelligence large language model-linked chatbots in surgical decision-making for gastroesophageal reflux disease.
Article in Surgical endoscopy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 3 of them syntheses that pooled it.
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Who cites it
15 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Radiomics and artificial intelligence-based prediction of tumor response in digestive system neoplasm: a systematic review and meta-analysis.Frontiers in medicine · 2026Pooled it
- Leveraging chatbots for enhanced decision-making: a comprehensive literature review.Frontiers in artificial intelligence · 2026Pooled it
- Large Language Models for Chatbot Health Advice Studies: A Systematic Review.JAMA network open · 2025Pooled it
- Multidisciplinary tumor board decisions and artificial intelligence-generated recommendations in general surgery: a retrospective observational study.Updates in surgery · 2026Article
- Performance of GPT-based large language models in hepatocellular carcinoma stratification: liver function assessment, BCLC staging, and treatment recommendations.Scientific reports · 2026Article
- Patient and clinician engagement with generative artificial intelligence (GenAI): A scoping review of implications for patient-centered communication.Patient education and counseling · 2026Article
- Article
- Large Language Models' Clinical Decision-Making on When to Perform a Kidney Biopsy: Comparative Study.Journal of medical Internet research · 2025Article
- Artificial intelligence in gastrointestinal surgery: A systematic review.World journal of gastrointestinal surgery · 2025Article
- Reporting guidelines for chatbot health advice studies: explanation and elaboration for the Chatbot Assessment Reporting Tool (CHART).BMJ (Clinical research ed.) · 2025Article
- Large language models' capabilities in responding to tuberculosis medical questions: testing ChatGPT, Gemini, and Copilot.Scientific reports · 2025Article
- Accuracy of ChatGPT-3.5, ChatGPT-4o, Copilot, Gemini, Claude, and Perplexity in advising on lumbosacral radicular pain against clinical practice guidelines: cross-sectional study.Frontiers in digital health · 2025Article
- A Performance Evaluation of Large Language Models in Keratoconus: A Comparative Study of ChatGPT-3.5, ChatGPT-4.0, Gemini, Copilot, Chatsonic, and Perplexity.Journal of clinical medicine · 2024Article
- Assessing the Accuracy of Artificial Intelligence Models in Scoliosis Classification and Suggested Therapeutic Approaches.Journal of clinical medicine · 2024Article
- Article
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Authors and funding
11 authors.
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Abstract
backgroundLarge language model (LLM)-linked chatbots may be an efficient source of clinical recommendations for healthcare providers and patients. This study evaluated the performance of LLM-linked chatbots in providing recommendations for the surgical management of gastroesophageal reflux disease (GERD).
methodsNine patient cases were created based on key questions addressed by the Society of American Gastrointestinal and Endoscopic Surgeons (SAGES) guidelines for the surgical treatment of GERD. ChatGPT-3.5, ChatGPT-4, Copilot, Google Bard, and Perplexity AI were queried on November 16th, 2023, for recommendations regarding the surgical management of GERD. Accurate chatbot performance was defined as the number of responses aligning with SAGES guideline recommendations. Outcomes were reported with counts and percentages.
resultsSurgeons were given accurate recommendations for the surgical management of GERD in an adult patient for 5/7 (71.4%) KQs by ChatGPT-4, 3/7 (42.9%) KQs by Copilot, 6/7 (85.7%) KQs by Google Bard, and 3/7 (42.9%) KQs by Perplexity according to the SAGES guidelines. Patients were given accurate recommendations for 3/5 (60.0%) KQs by ChatGPT-4, 2/5 (40.0%) KQs by Copilot, 4/5 (80.0%) KQs by Google Bard, and 1/5 (20.0%) KQs by Perplexity, respectively. In a pediatric patient, surgeons were given accurate recommendations for 2/3 (66.7%) KQs by ChatGPT-4, 3/3 (100.0%) KQs by Copilot, 3/3 (100.0%) KQs by Google Bard, and 2/3 (66.7%) KQs by Perplexity. Patients were given appropriate guidance for 2/2 (100.0%) KQs by ChatGPT-4, 2/2 (100.0%) KQs by Copilot, 1/2 (50.0%) KQs by Google Bard, and 1/2 (50.0%) KQs by Perplexity.
conclusionsGastrointestinal surgeons, gastroenterologists, and patients should recognize both the promise and pitfalls of LLM's when utilized for advice on surgical management of GERD. Additional training of LLM's using evidence-based health information is needed.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.