Clinical artificial intelligence: teaching a large language model to generate recommendations that align with guidelines for the surgical management of GERD.

BACKGROUND: Large Language Models (LLMs) provide clinical guidance with inconsistent accuracy due to limitations with their training dataset. LLMs are "teachable" through customization. We compared the ability of the generic ChatGPT-4 model and a customized version of ChatGPT-4 to provide recommendations for the surgical management of gastroesophageal reflux disease (GERD) to both surgeons and patients. METHODS: Sixty patient cases were developed using eligibility criteria from the Society of American Gastrointestinal and Endoscopic Surgeons (SAGES) & United European Gastroenterology (UEG)-European Association of Endoscopic. Surgery (EAES) guidelines for the surgical management of GERD. Standardized prompts were engineered for physicians as the end-user, with separate layperson prompts for patients. A customized GPT was developed to generate recommendations based on guidelines, called the GERD Tool for Surgery (GTS). Both the GTS and generic ChatGPT-4 were queried July 21st, 2024. Model performance was evaluated by comparing responses to SAGES & UEG-EAES guideline recommendations. Outcome data was presented using descriptive statistics including counts and percentages. RESULTS: The GTS provided accurate recommendations for the surgical management of GERD for 60/60 (100.0%) surgeon inquiries and 40/40 (100.0%) patient inquiries based on guideline recommendations. The Generic ChatGPT-4 model generated accurate guidance for 40/60 (66.7%) surgeon inquiries and 19/40 (47.5%) patient inquiries. The GTS produced recommendations based on the 2021 SAGES & UEG-EAES guidelines on the surgical management of GERD, while the generic ChatGPT-4 model generated guidance without citing evidence to support its recommendations. CONCLUSION: ChatGPT-4 can be customized to overcome limitations with its training dataset to provide recommendations for the surgical management of GERD with reliable accuracy and consistency. The training of LLM models can be used to help integrate this efficient technology into the creation of robust and accurate information for both surgeons and patients. Prospective data is needed to assess its effectiveness in a pragmatic clinical environment.

American society for metabolic and bariatric surgery: intra-operative care pathway for minimally invasive Roux-en-Y gastric bypass.

BACKGROUND: Clinical care pathways help guide and provide structure to clinicians and providers to improve healthcare delivery and quality. The Quality Improvement and Patient Safety Committee (QIPS) of the American Society for Metabolic and Bariatric Surgery (ASMBS) has previously published care pathways for the performance of laparoscopic sleeve gastrectomy (LSG) and pre-operative care of patients undergoing Roux-en-Y gastric bypass (RYGB). OBJECTIVE: This current RYGB care pathway was created to address intraoperative care, defined as care occurring on the day of surgery from the preoperative holding area, through the operating room, and into the postanesthesia care unit (PACU). METHODS: PubMed queries were performed from January 2001 to December 2019 and reviewed according to Level of Evidence regarding specific key questions developed by the committee. RESULTS: Evidence-based recommendations are made for care of patients undergoing RYGB including the pre-operative holding area, intra-operative management and performance of RYGB, and concurrent procedures. CONCLUSIONS: This document may provide guidance based on recent evidence to bariatric surgeons and providers for the intra-operative care for minimally invasive RYGB.

The performance of artificial intelligence large language model-linked chatbots in surgical decision-making for gastroesophageal reflux disease.

BACKGROUND: Large 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). METHODS: Nine 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. RESULTS: Surgeons 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. CONCLUSIONS: Gastrointestinal 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.

ASMBS statement on metabolic and bariatric surgery for beneficiaries of Centers for Medicare and Medicaid Services with a review of the literature.

This position statement is issued by the American Society for Metabolic and Bariatric. Surgery in response to inquiries made to the Society by patients, physicians, Society members, hospitals, health insurance payors, the media, and others regarding the access and outcomes of metabolic and bariatric surgery for beneficiaries of Centers for Medicare and Medicaid Services. This position statement is based on current clinical knowledge, expert opinion, and published peer-reviewed scientific evidence available at this time. The statement is not intended to be and should not be construed as stating or establishing a local, regional, or national standard of care. This statement will be revised in the future as additional evidence becomes available.