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.

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.

Optimization of mHealth behavioral interventions for patients with chronic lymphocytic leukemia: the HEALTH4CLL study.

PURPOSE: This pilot study of a diet and physical activity intervention (HEALTH4CLL) was conducted to reduce fatigue and improve physical function (PF) in patients with chronic lymphocytic leukemia (CLL). METHODS: The HEALTH4CLL study used a randomized factorial design based on the multiphase optimization strategy (MOST). Patients received diet, exercise, and body weight management instructional materials plus a Fitbit and were randomized to undergo one of 16 combinations of 4 evidence-based mHealth intervention strategies over 16 weeks. Patients' fatigue, PF, health-related quality of life, behavior changes, and program satisfaction and retention were assessed. Paired t-tests were used to examine changes in outcomes from baseline to follow-up among patients. Factorial analysis of variance examined effective intervention components and their combinations regarding improvement in fatigue and PF scores. RESULTS: Among 31 patients, we observed significant improvements in fatigue (+ 11.8; t = 4.08, p = 0.001) and PF (+ 2.6; t = 2.75, p = 0.01) scores. The combination of resistance and aerobic exercise with daily self-monitoring was associated with improved fatigue scores (ß = 3.857, SE = 1.617, p = 0.027). Analysis of the individual components of the MOST design demonstrated greater improvement in the PF score with resistance plus aerobic exercise than with aerobic exercise alone (ß = 2.257, SE = 1.071, p = 0.048). CONCLUSIONS: Combined aerobic and resistance exercise and daily self-monitoring improved PF and reduced fatigue in patients with CLL. IMPLICATIONS FOR CANCER SURVIVORS: This pilot study supported the feasibility of a low-touch mHealth intervention for survivors of CLL and provided preliminary evidence that exercising, particularly resistance exercise, can improve their symptoms and quality of life.