Background: As health care continues to evolve with technological advancements, the integration of artificial intelligence into clinical practices has shown promising potential to enhance patient care and operational efficiency. Among the forefront of these innovations are large language models (LLMs), a subset of artificial intelligence designed to understand, generate, and interact with human language at an unprecedented scale. Objective: This systematic review describes the role of LLMs in improving diagnostic accuracy, automating documentation, and advancing specialist education and patient engagement within the field of gastroenterology and gastrointestinal endoscopy. Methods: Core databases including MEDLINE through PubMed, Embase, and Cochrane Central registry were searched using keywords related to LLMs (from inception to April 2024). Studies were included if they satisfied the following criteria: (1) any type of studies that investigated the potential role of LLMs in the field of gastrointestinal endoscopy or gastroenterology, (2) studies published in English, and (3) studies in full-text format. The exclusion criteria were as follows: (1) studies that did not report the potential role of LLMs in the field of gastrointestinal endoscopy or gastroenterology, (2) case reports and review papers, (3) ineligible research objects (eg, animals or basic research), and (4) insufficient data regarding the potential role of LLMs. Risk of Bias in Non-Randomized Studies—of Interventions was used to evaluate the quality of the identified studies. Results: Overall, 21 studies on the potential role of LLMs in gastrointestinal disorders were included in the systematic review, and narrative synthesis was done because of heterogeneity in the specified aims and methodology in each included study. The overall risk of bias was low in 5 studies and moderate in 16 studies. The ability of LLMs to spread general medical information, offer advice for consultations, generate procedure reports automatically, or draw conclusions about the presumptive diagnosis of complex medical illnesses was demonstrated by the systematic review. Despite promising benefits, such as increased efficiency and improved patient outcomes, challenges related to data privacy, accuracy, and interdisciplinary collaboration remain. Conclusions: We highlight the importance of navigating these challenges to fully leverage LLMs in transforming gastrointestinal endoscopy practices. Trial Registration: PROSPERO 581772; https://www.crd.york.ac.uk/prospero/

Background: The large language model (LLM) has the potential to be applied to clinical practice. However, there has been scarce study on this in the field of gastroenterology. Aim: This study explores the potential clinical utility of two LLMs in the field of gastroenterology: a customized GPT model and a conventional GPT-4o, an advanced LLM capable of retrieval-augmented generation (RAG). Method: We established a customized GPT with the BM25 algorithm using Open AI’s GPT-4o model, which allows it to produce responses in the context of specific documents including textbooks of internal medicine (in English) and gastroenterology (in Korean). Also, we prepared a conventional ChatGPT 4o (accessed on 16 October 2024) access. The benchmark (written in Korean) consisted of 15 clinical questions developed by four clinical experts, representing typical questions for medical students. The two LLMs, a gastroenterology fellow, and an expert gastroenterologist were tested to assess their performance. Results: While the customized LLM correctly answered 8 out of 15 questions, the fellow answered 10 correctly. When the standardized Korean medical terms were replaced with English terminology, the LLM’s performance improved, answering two additional knowledge-based questions correctly, matching the fellow’s score. However, judgment-based questions remained a challenge for the model. Even with the implementation of ‘Chain of Thought’ prompt engineering, the customized GPT did not achieve improved reasoning. Conventional GPT-4o achieved the highest score among the AI models (14/15). Although both models performed slightly below the expert gastroenterologist’s level (15/15), they show promising potential for clinical applications (scores comparable with or higher than that of the gastroenterology fellow). Conclusions: LLMs could be utilized to assist with specialized tasks such as patient counseling. However, RAG capabilities by enabling real-time retrieval of external data not included in the training dataset, appear essential for managing complex, specialized content, and clinician oversight will remain crucial to ensure safe and effective use in clinical practice.

To address the challenges associated with fuel consumption in vehicles with low fuel efficiency, several factors must be recognized. Identifying the key factors of fuel efficiency prediction is crucial for making accurate decisions. Therefore, we propose a comprehensive framework that uses machine learning to predict fuel efficiency by integrating various vehicle information. The proposed method comprises a predictive model and analysis framework utilizing key vehicle attributes, such as fuel type, engine displacement, and vehicle grade, to enhance prediction accuracy. We conducted a comparative study using six machine-learning models. To evaluate the machine learning model, MSE (Mean Square Error), RMSE (Root Mean Square Error), MAE (Mean Absolute Error), and R-squared (\(R^2\) Score) were used. We experimented with SHAP(Shapley Additive Explanations), LIME (Local Interpretable Model-agnostic Explanations), and odds ratio analysis to evaluate the impact of various factors on fuel efficiency. We confirmed that the proposed method can predict fuel efficiency. Extra Trees Regressor and Random Forest Regressor demonstrated high prediction accuracy, particularly excelling in capturing nonlinear relationships. We also underscore the importance of identifying markers to support decision-making, offering critical insights into the key factors impacting fuel efficiency predictions.

Seoung-Ho Choi and Sung Hoon Jung. IJFIS 2019;19:40-7. https://doi.org/10.5391/IJFIS.2019.19.1.40

Jump rope exercise requires a fast tempo and breathing, which often leads to the problem of users forgetting their jump count during the workout. To address this issue, we propose a jump rope exercise assistance program that recognizes the user’s jump rope motions and analyzes the impact of joint coordinates on these motions. The proposed solution extracts frame-by-frame joint coordinate data from jump rope performance videos. It then utilizes artificial intelligence models to recognize jump rope motions and measure the jump count through motion recognition. We employed five machine learning models and two deep learning models to validate the jump rope motion recognition and count measurement. We analyzed the joint coordinates significantly influencing each jump rope motion using SHAP. Furthermore, we used Odds Ratios to analyze the jump rope motion occurrence probability based on joint coordinate values. Through these methods, we confirmed that the proposed solution effectively performs jump rope motion recognition and joint coordinate impact analysis for jump rope motions.