Examining the Accuracy and Reproducibility of Responses to Nutrition Questions Related to Inflammatory Bowel Disease by Generative Pre-trained Transformer-4

Abstract

Background: Generative pre-trained transformer-4 (GPT-4) is a large language model (LLM) trained on a vast corpus of data, including the medical literature. Nutrition plays an important role in managing inflammatory bowel disease (IBD), with an unmet need for nutrition-related patient education resources. This study examines the accuracy, comprehensiveness, and reproducibility of responses by GPT-4 to patient nutrition questions related to IBD. Methods: Questions were obtained from adult IBD clinic visits, Facebook, and Reddit. Two IBD-focused registered dieticians independently graded the accuracy and reproducibility of GPT-4's responses while a third senior IBD-focused registered dietitian arbitrated. Each question was inputted twice into the model. Results: 88 questions were selected. The model correctly responded to 73/88 questions (83.0%), with 61 (69.0%) graded as comprehensive. 15/88 (17%) responses were graded as mixed with correct and incorrect/outdated data. The model comprehensively responded to 10 (62.5%) questions related to "Nutrition and diet needs for surgery," 12 (92.3%) "Tube feeding and parenteral nutrition," 11 (64.7%) "General diet questions," 10 (50%) "Diet for reducing symptoms/inflammation," and 18 (81.8%) "Micronutrients/supplementation needs." The model provided reproducible responses to 81/88 (92.0%) questions. Conclusions: GPT-4 comprehensively answered most questions, demonstrating the promising potential of LLMs as supplementary tools for IBD patients seeking nutrition-related information. However, 17% of responses contained incorrect information, highlighting the need for continuous refinement prior to incorporation into clinical practice. Future studies should emphasize leveraging LLMs to enhance patient outcomes and promoting patient and healthcare professional proficiency in using LLMs to maximize their efficacy.

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