The field of gastroenterology (GI) is constantly evolving. It is essential to pinpoint the most pressing and important research questions. To evaluate the potential of chatGPT for identifying research priorities in GI and provide a starting point for further investigation. We queried chatGPT on four key topics in GI: inflammatory bowel disease, microbiome, Artificial Intelligence in GI, and advanced endoscopy in GI. A panel of experienced gastroenterologists separately reviewed and rated the generated research questions on a scale of 1–5, with 5 being the most important and relevant to current research in GI. chatGPT generated relevant and clear research questions. Yet, the questions were not considered original by the panel of gastroenterologists. On average, the questions were rated 3.6 ± 1.4, with inter-rater reliability ranging from 0.80 to 0.98 (p < 0.001). The mean grades for relevance, clarity, specificity, and originality were 4.9 ± 0.1, 4.6 ± 0.4, 3.1 ± 0.2, 1.5 ± 0.4, respectively. Our study suggests that Large Language Models (LLMs) may be a useful tool for identifying research priorities in the field of GI, but more work is needed to improve the novelty of the generated research questions.

Background and aims: Patients frequently have concerns about their disease and find it challenging to obtain accurate Information. OpenAI’s ChatGPT chatbot (ChatGPT) is a new large language model developed to provide answers to a wide range of questions in various fields. Our aim is to evaluate the performance of ChatGPT in answering patients’ questions regarding gastrointestinal health. Methods: To evaluate the performance of ChatGPT in answering patients’ questions, we used a representative sample of 110 real-life questions. The answers provided by ChatGPT were rated in consensus by three experienced gastroenterologists. The accuracy, clarity, and efficacy of the answers provided by ChatGPT were assessed. Results: ChatGPT was able to provide accurate and clear answers to patients’ questions in some cases, but not in others. For questions about treatments, the average accuracy, clarity, and efficacy scores (1 to 5) were 3.9 ± 0.8, 3.9 ± 0.9, and 3.3 ± 0.9, respectively. For symptoms questions, the average accuracy, clarity, and efficacy scores were 3.4 ± 0.8, 3.7 ± 0.7, and 3.2 ± 0.7, respectively. For diagnostic test questions, the average accuracy, clarity, and efficacy scores were 3.7 ± 1.7, 3.7 ± 1.8, and 3.5 ± 1.7, respectively. Conclusions: While ChatGPT has potential as a source of information, further development is needed. The quality of information is contingent upon the quality of the online information provided. These findings may be useful for healthcare providers and patients alike in understanding the capabilities and limitations of ChatGPT.

Background and Aims: Inflammatory bowel disease (IBD) patients suffer from significant morbidity and diminished life quality. The plant cannabis is beneficial in various gastrointestinal diseases, stimulating appetite and causing weight gain. Our aims were to assess whether treatment with inhaled cannabis improves quality of life, disease activity and promotes weight gain in these patients. Methods: Patients with long-standing IBD who were prescribed cannabis treatment were included. Two quality of life questionnaires and disease activity indexes were performed, and patient’s body weight was measured before cannabis initiation and after 3 months’ treatment. Results: Thirteen patients were included. After 3 months’ treatment, patients reported improvement in general health perception (p = 0.001), social functioning (p = 0.0002), ability to work (p = 0.0005), physical pain (p = 0.004) and depression (p = 0.007). A schematic scale of health perception showed an improved score from 4.1 ± 1.43 to 7 ± 1.42 (p = 0.0002). Patients had a weight gain of 4.3 ± 2 kg during treatment (range 2–8; p = 0.0002) and an average rise in BMI of 1.4 ± 0.61 (range 0.8–2.7; p = 0.002). The average Harvey-Bradshaw index was reduced from 11.36 ± 3.17 to 5.72 ± 2.68 (p = 0.001). Conclusions: Three months’ treatment with inhaled cannabis improves quality of life measurements, disease activity index, and causes weight gain and rise in BMI in long-standing IBD patients.

Background: Artificial intelligence, particularly chatbot systems, is becoming an instrumental tool in health care, aiding clinical decision-making and patient engagement. Objective: This study aims to analyze the performance of ChatGPT-3.5 and ChatGPT-4 in addressing complex clinical and ethical dilemmas, and to illustrate their potential role in health care decision-making while comparing seniors’ and residents’ ratings, and specific question types. Methods: A total of 4 specialized physicians formulated 176 real-world clinical questions. A total of 8 senior physicians and residents assessed responses from GPT-3.5 and GPT-4 on a 1-5 scale across 5 categories: accuracy, relevance, clarity, utility, and comprehensiveness. Evaluations were conducted within internal medicine, emergency medicine, and ethics. Comparisons were made globally, between seniors and residents, and across classifications. Results: Both GPT models received high mean scores (4.4, SD 0.8 for GPT-4 and 4.1, SD 1.0 for GPT-3.5). GPT-4 outperformed GPT-3.5 across all rating dimensions, with seniors consistently rating responses higher than residents for both models. Specifically, seniors rated GPT-4 as more beneficial and complete (mean 4.6 vs 4.0 and 4.6 vs 4.1, respectively; P<.001), and GPT-3.5 similarly (mean 4.1 vs 3.7 and 3.9 vs 3.5, respectively; P<.001). Ethical queries received the highest ratings for both models, with mean scores reflecting consistency across accuracy and completeness criteria. Distinctions among question types were significant, particularly for the GPT-4 mean scores in completeness across emergency, internal, and ethical questions (4.2, SD 1.0; 4.3, SD 0.8; and 4.5, SD 0.7, respectively; P<.001), and for GPT-3.5’s accuracy, beneficial, and completeness dimensions. Conclusions: ChatGPT’s potential to assist physicians with medical issues is promising, with prospects to enhance diagnostics, treatments, and ethics. While integration into clinical workflows may be valuable, it must complement, not replace, human expertise. Continued research is essential to ensure safe and effective implementation in clinical environments.

Background: The integration of artificial intelligence (AI) into healthcare has opened new avenues for enhancing patient care and clinical research. In gastroenterology, the potential of AI tools, specifically large language models like ChatGPT, is being explored to understand their utility and effectiveness. Objectives: The primary goal of this systematic review is to assess the various applications, ascertain the benefits, and identify the limitations of utilizing ChatGPT within the realm of gastroenterology. Design: Through a systematic approach, this review aggregates findings from multiple studies to evaluate the impact of ChatGPT on the field. Data sources and methods: The review was based on a detailed literature search of the PubMed database, targeting research that delves into the use of ChatGPT for gastroenterological purposes. It incorporated six selected studies, which were meticulously evaluated for quality using the Joanna Briggs Institute critical appraisal instruments. The data were then synthesized narratively to encapsulate the roles, advantages, and drawbacks of ChatGPT in gastroenterology. Results: The investigation unearthed various roles of ChatGPT, including its use in patient education, diagnostic self-assessment, disease management, and the formulation of research queries. Notable benefits were its capability to provide pertinent recommendations, enhance communication between patients and physicians, and prompt valuable research inquiries. Nonetheless, it encountered obstacles in decoding intricate medical questions, yielded inconsistent responses at times, and exhibited limitations in generating novel content. The review also considered ethical implications. Conclusion: ChatGPT has demonstrated significant potential in the field of gastroenterology, especially in facilitating patient–physician interactions and managing diseases. Despite these advancements, the review underscores the necessity for ongoing refinement, customization, and ethical regulation of AI tools. These findings serve to enrich the dialog concerning AI’s role in healthcare, with a specific focus on ChatGPT’s application in gastroenterology.

Lidocaine is a commonly used local anaesthetic agent which has also been found to possess anti-inflammatory activity in several disorders. However, the mechanism of this effect has been little explored. The aim of this study was to investigate the effect of lidocaine on stimulated human T cells. The effect of lidocaine on Jurkat T cells was examined by enzyme-linked immunosorbent assay (ELISA) to determine secretion of interleukin (IL)-2, and by the [3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide] viability assay. Tumour necrosis factor (TNF)-α and IL-2 mRNA expression was determined by reverse transcription–polymerase chain reaction. In addition, the effect of lidocaine on the proliferation of freshly isolated peripheral blood (PB) CD3⁺ T cells was examined by carboxyfluorescein succinimidyl ester dilution. Apoptosis induction and cytokine production and secretion were determined by annexin V/PI assay, intracellular immunostaining and ELISA respectively. The results showed that lidocaine exerts a dose-dependent inhibition of IL-2 and TNF-α secretion by Jurkat T cells at the protein and mRNA levels. Moreover, lidocaine reduced nuclear factor-κB (NF-κB) signalling in clinically relevant concentrations. Similarly, proliferation of anti-CD3 stimulated PB T cells was abrogated significantly by lidocaine, and the percentage of interferon-γ- and TNF-α-producing T cells was diminished after culture with this agent. In both experimental systems, lidocaine's effect was not mediated by cytotoxic mechanism, as no significant apoptosis or necrosis was demonstrated following co-culture of T cells with this drug. In conclusion, lidocaine's anti-inflammatory effect may be mediated by a drug-induced abrogation of T cell proliferation and cytokine secretion independent of cell death. These effects are mediated at least partly by inhibition of NF-κB signalling.