May ChatGPT be a tool producing medical information for common inflammatory bowel disease patients’ questions? An evidence-controlled analysis

Utilizing large language models for gastroenterology research: a conceptual framework

Large language models (LLMs) transform healthcare by assisting clinicians with decision-making, research, and patient management. In gastroenterology, LLMs have shown potential in clinical decision support, data extraction, and patient education. However, challenges such as bias, hallucinations, integration with clinical workflows, and regulatory compliance must be addressed for safe and effective implementation. This manuscript presents a structured framework for integrating LLMs into gastroenterology, using Hepatitis C treatment as a real-world application. The framework outlines key steps to ensure accuracy, safety, and clinical relevance while mitigating risks associated with artificial intelligence (AI)-driven healthcare tools. The framework includes defining clinical goals, assembling a multidisciplinary team, data collection and preparation, model selection, fine-tuning, calibration, hallucination mitigation, user interface development, integration with electronic health records, real-world validation, and continuous improvement. Retrieval-augmented generation and fine-tuning approaches are evaluated for optimizing model adaptability. Bias detection, reinforcement learning from human feedback, and structured prompt engineering are incorporated to enhance reliability. Ethical and regulatory considerations, including the Health Insurance Portability and Accountability Act, General Data Protection Regulation, and AI-specific guidelines (DECIDE-AI, SPIRIT-AI, CONSORT-AI), are addressed to ensure responsible AI deployment. LLMs have the potential to enhance decision-making, research efficiency, and patient care in gastroenterology, but responsible deployment requires bias mitigation, transparency, and ongoing validation. Future research should focus on multi-institutional validation and AI-assisted clinical trials to establish LLMs as reliable tools in gastroenterology.

Application of Internet Hospitals in the Disease Management of Patients With Ulcerative Colitis: Retrospective Study

BACKGROUND

Ulcerative colitis (UC) is a chronic disease characterized by frequent relapses, requiring long-term management and consuming substantial medical and social resources. Effective management of UC remains challenging due to the need for sustainable remission strategies, continuity of care, and access to medical services. Intelligent diagnosis refers to the use of artificial intelligence-driven algorithms to analyze patient-reported symptoms, generate diagnostic probabilities, and provide treatment recommendations through interactive tools. This approach could potentially function as a method for UC management.

OBJECTIVE

This study aimed to analyze the diagnosis and treatment data of UC from both physical hospitals and internet hospitals, highlighting the potential benefits of the intelligent diagnosis and treatment service model offered by internet hospitals.

METHODS

We collected data on the visits of patients with UC to the Department of Gastroenterology at Tianjin Medical University General Hospital. A total of 852 patients with UC were included between July 1, 2020, and June 31, 2023. Statistical methods, including chi-square tests for categorical variables, t tests for continuous variables, and rank-sum tests for visit numbers, were used to evaluate the medical preferences and expenses of patients with UC.

RESULTS

We found that internet hospitals and physical hospitals presented different medical service models due to the different distribution of medical needs and patient groups. Patients who chose internet hospitals focused on disease consultation and prescription medication (3295/3528, 93.40%). Patients' medical preferences gradually shifted to web-based services provided by internet hospitals. Over time, 58.57% (270/461) of patients chose either web-based services or a combination of web-based and offline services for UC diagnosis and treatment. The number of visits in the combination of web-based and offline service modes was the highest (mean 13.83, SD 11.07), and younger patients were inclined to visit internet hospitals (49.66%>34.71%). In addition, compared with physical hospitals, there was no difference in testing fees and examination fees for patients with UC in internet hospitals, but medicine fees were lower.

CONCLUSIONS

The intelligent diagnosis and treatment model provided by internet hospitals demonstrates the potential benefits in managing UC, including feasibility, accessibility, convenience, and economics.

Assessing the Validity, Safety, and Utility of ChatGPT's Responses for Patients with Frozen Shoulder

This study evaluates the potential of ChatGPT as a tool for providing information to patients with frozen shoulder, focusing on its validity, utility, and safety. Five experienced physicians selected fourteen key questions on musculoskeletal disorders after discussion and verified their adequacy by consulting one hundred and twenty frozen shoulder patients for additional or alternative inquiries. These questions were input into ChatGPT version 4.0, and its responses were assessed by the physicians using a 5-point Likert scale, with scores ranging from 1 (least favorable) to 5 (most favorable) in terms of validity, safety, and utility. The findings showed that for validity, 85.7% of the responses scored 5, and 14.3% scored 4. For safety, 92.9% received a score of 5, while one response received a 4. Utility ratings also demonstrated high scores, with 85.7% of responses rated 5 and 14.3% rated 4. These results indicate that ChatGPT provides generally valid, safe, and useful information for patients with frozen shoulder. However, users should be aware of potential gaps or inaccuracies, and continued updates are necessary to ensure reliable and accurate guidance. It should not be considered a substitute for professional medical advice, diagnosis, or treatment, highlighting the need for continued updates to ensure reliable and accurate guidance.

An Access-Focused Patient-Centric Value Assessment Framework for Medication Formulary Decision-Making in Immune-Mediated Inflammatory Diseases

The healthcare system in the United States (US) is complex and often fragmented across national and regional health plans which exhibit substantial variability in benefit design and formulary policies for accessing medications. We propose an access-focused value assessment framework for formulary decision-making for medications to manage immune-mediated inflammatory diseases (IMIDs), where patients are at the center of this framework. Formulary decision-making for IMID medications can be a challenging, even daunting, task with continuously evolving and enhanced treat-to-target goals. Given the complexity of the US healthcare system, patients and their caregivers need assurance from formulary decision-makers that rapid, predictable, and sustained access to both well-established treatments and innovative therapies will be a priority, with a particular emphasis on continuity of effective care. This access-focused patient-centric (APAC) value assessment approach encompasses three "value components"-higher therapeutic goals, better health-related quality of life, and improved work productivity-the monetization of which can be derived using data from clinical trials when real-world data are yet to become available. Measures and assessment approaches are outlined to serve as a pragmatic tool for decision-makers in the US to ensure timely delivery and sustained access of clinically indicated therapies aimed to improve patient outcomes, enhance equity, and increase efficiency.

Inter-Rater Disagreements in Applying the Montreal Classification for Crohn's Disease: The Five-Nations Survey Study

BACKGROUND

The Montreal classification has been widely used in Crohn's disease since 2005 to categorize patients by the age of onset (A), disease location (L), behavior (B), and upper gastrointestinal tract and perianal involvement. With evolving management paradigms in Crohn's disease, we aimed to assess the performance of gastroenterologists in applying the Montreal classification.

METHODS

An online survey was conducted among participants at an international educational conference on inflammatory bowel diseases. Participants classified 20 theoretical Crohn's disease cases using the Montreal classification. Agreement rates with the inflammatory bowel diseases board (three expert gastroenterologists whose consensus rating was considered the gold standard) were calculated for gastroenterologist specialists and fellows/specialists with ≤ 2 years of clinical experience. A majority vote < 75% among participants was considered a notable disagreement. The same cases were classified using three large language models (LLMs), ChatGPT-4, Claude-3, and Gemini-1.5, and assessed for agreement with the board and gastroenterologists. Fleiss Kappa was used to assess within-group agreement.

RESULTS

Thirty-eight participants from five countries completed the survey. In defining the Montreal classification as a whole, specialists (21/38 [55%]) had a higher agreement rate with the board compared to fellows/young specialists (17/38 [45%]) (58% vs. 49%, p = 0.012) and to LLMs (58% vs. 18%, p < 0.001). Disease behavior classification was the most challenging, with 76% agreement among specialists and fellows/young specialists and 48% among LLMs compared to the inflammatory bowel diseases board. Regarding disease behavior, within-group agreement was moderate (specialists: k = 0.522, fellows/young specialists: k = 0.532, LLMs: k = 0.577; p < 0.001 for all). Notable points of disagreement included: defining disease behavior concerning obstructive symptoms, assessing disease extent via video capsule endoscopy, and evaluating treatment-related reversibility of the disease phenotype.

CONCLUSIONS

There is significant inter-rater disagreement in applying the Montreal classification, particularly for disease behavior in Crohn's disease. Improved education or revisions to phenotype criteria may be needed to enhance consensus on the Montreal classification.

Comparing the performance of ChatGPT and ERNIE Bot in answering questions regarding liver cancer interventional radiology in Chinese and English contexts: A comparative study

Introduction

This study aims to critically assess the appropriateness and limitations of two prominent large language models (LLMs), enhanced representation through knowledge integration (ERNIE Bot) and chat generative pre-trained transformer (ChatGPT), in answering questions about liver cancer interventional radiology. Through a comparative analysis, the performance of these models will be evaluated based on their responses to questions about transarterial chemoembolization and hepatic arterial infusion chemotherapy in both English and Chinese contexts.

Methods

A total of 38 questions were developed to cover a range of topics related to transarterial chemoembolization (TACE) and hepatic arterial infusion chemotherapy (HAIC), including foundational knowledge, patient education, and treatment and care. The responses generated by ERNIE Bot and ChatGPT were rigorously evaluated by 10 professionals in liver cancer interventional radiology. The final score was determined by one seasoned clinical expert. Each response was rated on a five-point Likert scale, facilitating a quantitative analysis of the accuracy and comprehensiveness of the information provided by each language model.

Results

ERNIE Bot is superior to ChatGPT in the Chinese context (ERNIE Bot: 5, 89.47%; 4, 10.53%; 3, 0%; 2, 0%; 1, 0% vs ChatGPT: 5, 57.89%; 4, 5.27%; 3, 34.21%; 2, 2.63%; 1, 0%; P = 0.001). However, ChatGPT outperformed ERNIE Bot in the English context (ERNIE Bot: 5, 73.68%; 4, 2.63%; 3, 13.16; 2, 10.53%；1, 0% vs ChatGPT: 5, 92.11%; 4, 2.63%; 3, 5.26%; 2, 0%; 1, 0%; P = 0.026).

Conclusions

This study preliminarily demonstrated that ERNIE Bot and ChatGPT effectively address questions related to liver cancer interventional radiology. However, their performance varied by language: ChatGPT excelled in English contexts, while ERNIE Bot performed better in Chinese. We found that choosing the appropriate LLMs is beneficial for patients in obtaining more accurate treatment information. Both models require manual review to ensure accuracy and reliability in practical use.

The global research of artificial intelligence on inflammatory bowel disease: A bibliometric analysis

Aims

This study aimed to evaluate the related research on artificial intelligence (AI) in inflammatory bowel disease (IBD) through bibliometrics analysis and identified the research basis, current hotspots, and future development.

Methods

The related literature was acquired from the Web of Science Core Collection (WoSCC) on 31 December 2024. Co-occurrence and cooperation relationship analysis of (cited) authors, institutions, countries, cited journals, references, and keywords in the literature were carried out through CiteSpace 6.1.R6 software and the Online Analysis platform of Literature Metrology. Meanwhile, relevant knowledge maps were drawn, and keywords clustering analysis was performed.

Results

According to WoSCC, 1919 authors, 790 research institutions, 184 journals, and 49 countries/regions published 176 AI-related papers in IBD during 1999-2024. The number of papers published has increased significantly since 2019, reaching a maximum by 2023. The United States had the highest number of publications and the closest collaboration with other countries. The clustering analysis showed that the earliest studies focused on "psychometric value" and then moved to "deep learning model," "intestinal ultrasound," and "new diagnostic strategies."

Conclusion

This study is the first bibliometric analysis to summarize the current status and to visually reveal the development trends and future research hotspots of the application of AI in IBD. The application of AI in IBD is still in its infancy, and the focus of this field will shift to improving the efficiency of diagnosis and treatment through deep learning techniques, big data-based treatment, and prognosis prediction.

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

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.

Assessing the quality of ChatGPT's responses to questions related to radiofrequency ablation for varicose veins

OBJECTIVE

This study aimed to evaluate the accuracy and reproducibility of information provided by ChatGPT, in response to frequently asked questions about radiofrequency ablation (RFA) for varicose veins.

METHODS

This cross-sectional study was conducted at The Aga Khan University Hospital, Karachi, Pakistan. A set of 18 frequently asked questions regarding RFA for varicose veins were compiled from credible online sources and presented to ChatGPT twice, separately, using the new chat option. Twelve experienced vascular surgeons (with >2 years of experience and ≥20 RFA procedures performed annually) independently evaluated the accuracy of the responses using a 4-point Likert scale and assessed their reproducibility.

RESULTS

Most evaluators were males (n = 10/12 [83.3%]) with an average of 12.3 ± 6.2 years of experience as a vascular surgeon. Six evaluators (50%) were from the UK followed by three from Saudi Arabia (25.0%), two from Pakistan (16.7%), and one from the United States (8.3%). Among the 216 accuracy grades, most of the evaluators graded the responses as comprehensive (n = 87/216 [40.3%]) or accurate but insufficient (n = 70/216 [32.4%]), whereas only 17.1% (n = 37/216) were graded as a mixture of both accurate and inaccurate information and 10.8% (n = 22/216) as entirely inaccurate. Overall, 89.8% of the responses (n = 194/216) were deemed reproducible. Of the total responses, 70.4% (n = 152/216) were classified as good quality and reproducible. The remaining responses were poor quality with 19.4% reproducible (n = 42/216) and 10.2% nonreproducible (n = 22/216). There was nonsignificant inter-rater disagreement among the vascular surgeons for overall responses (Fleiss' kappa, -0.028; P = .131).

CONCLUSIONS

ChatGPT provided generally accurate and reproducible information on RFA for varicose veins; however, variability in response quality and limited inter-rater reliability highlight the need for further improvements. Although it has the potential to enhance patient education and support healthcare decision-making, improvements in its training, validation, transparency, and mechanisms to address inaccurate or incomplete information are essential.

Effectiveness of Telemedicine in Inflammatory Bowel Disease in Russia: TIGE-Rus (Telemonitoring for IBD Goodness Examination in Russia) Study Protocol of a Randomized Controlled Trial

Background: Inflammatory bowel diseases (IBD), associated with a significant burden on patients' lives, are becoming increasingly common. Patients with IBD need continuous treatment and lifelong monitoring, which could be achieved by telemonitoring. Telemonitoring has been shown to be effective in improving outcomes for patients with IBD, and can provide a more convenient and accessible way for patients to receive care. However, the certainty of evidence remains low. This article outlines the methodology of a randomized control study that aims to assess the efficacy of telemonitoring compared to face-to-face follow-up for patients with IBD in Russia, hypothesizing that the implementation of telemonitoring will lead to improvement in clinical, social, and organizational areas. Methods: The TIGE-Rus study is a randomized controlled trial. The study consists of three stages, including selection of patients and random assignment into two groups with a ratio of 1:1, follow-up care using telemonitoring or face-to-face appointments, and evaluation and comparison of follow-up efficacy in both groups. In the first stage, all patients will undergo laboratory tests and instrumental examinations, and fill out questionnaires to measure disease activity, quality of life, medication adherence, psychological well-being, and satisfaction with medical care. In the second stage, the control group will receive standard care while the telemonitoring group will have access to a web platform where they can report their clinical activity, fill out questionnaires, and have online consultations with gastroenterologists. The gastroenterologists will also make monthly phone calls to each patient in the telemonitoring group to monitor their progress. In the third stage of the study, both the telemonitoring group and the control group will be re-hospitalized after six months of monitoring. IBD activity will be evaluated through laboratory and instrumental examinations. Additionally, all the participants will complete questionnaires to assess the disease activity, medication adherence, quality of life, psychological well-being, and satisfaction with medical care in both groups. Conclusions: The trial will explore whether telemonitoring is effective in improving clinical, social, and organizational aspects in the management of patients with IBD in the setting of the Russian healthcare system.

Perception, concerns, and practice of ChatGPT among Egyptian pharmacists: a cross-sectional study in Egypt

BACKGROUND

The emergence of large language models (LLMs) like ChatGPT attracted significant attention for their potential to revolutionize pharmacy practice. While artificial intelligence (AI) offers promising benefits, its integration also presents unique challenges.

OBJECTIVES

This cross-sectional study aimed to explore the current Egyptian pharmacists' perceptions, practices, and concerns regarding ChatGPT in pharmacy practice.

METHODS

The study questionnaire was shared with pharmacists during March and April 2024. We included pharmacists licensed by the Egyptian Ministry of Health and Population. We adapted a convenient sampling technique by sending the research questionnaire via emails, student networks, social media (Facebook and WhatsApp), and student organizations. Any pharmacist interested in participating followed a link to review the study description and was asked to provide electronic consent before continuing with the study. Data were analyzed using SPSS software, employing Chi-square tests for categorical variables and Spearman's correlation for continuous variables. Statistical significance was set at p < 0.05.

RESULTS

The study sample size included 428 pharmacists from the main economic regions of Egypt. The results revealed a strong recognition (73.6%) among participants of ChatGPT's anticipated benefits within pharmacy practice. Around two-thirds of the participants (65.9%) expressed disagreement or neutrality regarding the application of ChatGPT for analyzing patients' medical inputs and providing individualized medical advice. Regarding factors affecting perception, we found that the region is the only factor that significantly contributed to the level of perception among pharmacists (P = 0.011) with Greater cairo region showing the highest perception level. We found that 73.6% of participants who have heard about ChatGPT reported high levels of concern. One-third of participants never use ChatGPT in their pharmacy work, and 20% rarely use it. Using Spearman's correlation test, there was no significant correlation between anticipated advantages, concerns and practice level (P > 0.05).

CONCLUSION

This study reveals a generally positive perception of ChatGPT's potential benefits among Egyptian pharmacists, despite existing concerns regarding accuracy, data privacy, and bias. Notably, no significant associations were found between demographic factors and pharmacists' perceptions, practices, or concerns. This underscores the need for comprehensive educational initiatives to promote informed and responsible ChatGPT utilization within pharmacy practice. Future research should explore the development and implementation of tailored training programs and guidelines to ensure the safe and effective integration of ChatGPT into pharmacy workflows for optimal patient care.

Conversational LLM Chatbot ChatGPT-4 for Colonoscopy Boston Bowel Preparation Scoring: An Artificial Intelligence-to-Head Concordance Analysis

BACKGROUND/OBJECTIVES

To date, no studies have evaluated Chat Generative Pre-Trained Transformer (ChatGPT) as a large language model chatbot in optical applications for digestive endoscopy images. This study aimed to weigh the performance of ChatGPT-4 in assessing bowel preparation (BP) quality for colonoscopy.

METHODS

ChatGPT-4 analysed 663 anonymised endoscopic images, scoring each according to the Boston BP scale (BBPS). Expert physicians scored the same images subsequently.

RESULTS

ChatGPT-4 deemed 369 frames (62.9%) to be adequately prepared (i.e., BBPS > 1) compared to 524 frames (89.3%) assessed by human assessors. The agreement was slight (κ: 0.099, p = 0.0001). The raw human BBPS score was higher at 3 (2-3) than that of ChatGPT-4 at 2 (1-3), demonstrating moderate concordance (W: 0.554, p = 0.036).

CONCLUSIONS

ChatGPT-4 demonstrates some potential in assessing BP on colonoscopy images, but further refinement is still needed.

Navigating new horizons in inflammatory bowel disease: Integrative approaches and innovations

This editorial offers an updated synthesis of the major advancements in the management and treatment of inflammatory bowel disease (IBD), as documented in the World Journal of Gastroenterology between 2023 and early 2024. This editorial explores substantial developments across key research areas, such as intestinal microecology, computational drug discovery, dual biologic therapy, telemedicine, and the integration of lifestyle changes into patient care. Furthermore, the discussion of emerging topics, including bowel preparation in colonoscopy, the impact of the coronavirus disease 2019 pandemic, and the intersection between IBD and mental health, reflects a shift toward a more holistic approach to IBD research. By integrating these diverse areas of research, this editorial seeks to promote a holistic and multidisciplinary approach to IBD treatment, combining emerging technologies, personalized medicine, and conventional therapies to improve patient outcomes.

Application of large language model combined with retrieval enhanced generation technology in digestive endoscopic nursing

Background

Although large language models (LLMs) have demonstrated powerful capabilities in general domains, they may output information in the medical field that could be incorrect, incomplete, or fabricated. They are also unable to answer personalized questions related to departments or individual patient health. Retrieval-augmented generation technology (RAG) can introduce external knowledge bases and utilize the retrieved information to generate answers or text, thereby enhancing prediction accuracy.

Method

We introduced internal departmental data and 17 commonly used gastroenterology guidelines as a knowledge base. Based on RAG, we developed the Endo-chat medical chat application, which can answer patient questions related to gastrointestinal endoscopy. We then included 200 patients undergoing gastrointestinal endoscopy, randomly divided into two groups of 100 each, for a questionnaire survey. A comparative evaluation was conducted between the traditional manual methods and Endo-chat.

Results

Compared to ChatGPT, Endo-chat can accurately and professionally answer relevant questions after matching the knowledge base. In terms of response efficiency, completeness, and patient satisfaction, Endo-chat outperformed manual methods significantly. There was no statistical difference in response accuracy between the two. Patients showed a preference for AI services and expressed support for the introduction of AI. All participating nurses in the survey believed that introducing AI could reduce nursing workload.

Conclusion

In clinical practice, Endo-chat can be used as a highly effective auxiliary tool for digestive endoscopic care.

Future Perspective: Harnessing the Power of Artificial Intelligence in the Generation of New Peptide Drugs

The expansive field of drug discovery is continually seeking innovative approaches to identify and develop novel peptide-based therapeutics. With the advent of artificial intelligence (AI), there has been a transformative shift in the generation of new peptide drugs. AI offers a range of computational tools and algorithms that enables researchers to accelerate the therapeutic peptide pipeline. This review explores the current landscape of AI applications in peptide drug discovery, highlighting its potential, challenges, and ethical considerations. Additionally, it presents case studies and future prospectives that demonstrate the impact of AI on the generation of new peptide drugs.

Potential application of ChatGPT in Helicobacter pylori disease relevant queries

Background

Advances in artificial intelligence are gradually transforming various fields, but its applicability among ordinary people is unknown. This study aims to explore the ability of a large language model to address Helicobacter pylori related questions.

Methods

We created several prompts on the basis of guidelines and the clinical concerns of patients. The capacity of ChatGPT on Helicobacter pylori queries was evaluated by experts. Ordinary people assessed the applicability.

Results

The responses to each prompt in ChatGPT-4 were good in terms of response length and repeatability. There was good agreement in each dimension (Fleiss' kappa ranged from 0.302 to 0.690, p < 0.05). The accuracy, completeness, usefulness, comprehension and satisfaction scores of the experts were generally high. Rated usefulness and comprehension among ordinary people were significantly lower than expert, while medical students gave a relatively positive evaluation.

Conclusion

ChatGPT-4 performs well in resolving Helicobacter pylori related questions. Large language models may become an excellent tool for medical students in the future, but still requires further research and validation.

Editor-in-Chief articles of choice and comments from January to June 2024

As the Editor-in-Chief of the World Journal of Gastroenterology, I carefully review all articles every week before a new issue’s online publication, including the title, clinical and research importance, originality, novelty, and ratings by the peer reviewers. Based on this review, I select the papers of choice and suggest pertinent changes (e.g., in the title or text) to the company editors responsible for publication. This process, while time-consuming, is essential for assuring the quality of publications and highlighting important articles that readers may revisit.

Author's reply: "Assessing ChatGPT and perplexity AI performance"

None.

Accuracy Evaluation of GPT-Assisted Differential Diagnosis in Emergency Department

In emergency department (ED) settings, rapid and precise diagnostic evaluations are critical to ensure better patient outcomes and efficient healthcare delivery. This study assesses the accuracy of differential diagnosis lists generated by the third-generation ChatGPT (ChatGPT-3.5) and the fourth-generation ChatGPT (ChatGPT-4) based on electronic health record notes recorded within the first 24 h of ED admission. These models process unstructured text to formulate a ranked list of potential diagnoses. The accuracy of these models was benchmarked against actual discharge diagnoses to evaluate their utility as diagnostic aids. Results indicated that both GPT-3.5 and GPT-4 reasonably accurately predicted diagnoses at the body system level, with GPT-4 slightly outperforming its predecessor. However, their performance at the more granular category level was inconsistent, often showing decreased precision. Notably, GPT-4 demonstrated improved accuracy in several critical categories that underscores its advanced capabilities in managing complex clinical scenarios.

Evaluating the role of large language models in inflammatory bowel disease patient information

This letter evaluates the article by Gravina et al on ChatGPT's potential in providing medical information for inflammatory bowel disease patients. While promising, it highlights the need for advanced techniques like reasoning + action and retrieval-augmented generation to improve accuracy and reliability. Emphasizing that simple question and answer testing is insufficient, it calls for more nuanced evaluation methods to truly gauge large language models' capabilities in clinical applications.

Role of artificial intelligence in Crohn's disease intestinal strictures and fibrosis

Crohn's disease (CD) is a chronic inflammatory disorder of the gastrointestinal tract. Intestinal fibrosis or stricture is one of the most prevalent complications in CD with a high recurrence rate. Manual examination of intestinal fibrosis or stricture by physicians may be biased or inefficient. A rapid development of artificial intelligence (AI) technique in recent years facilitates the detection of existing or possible intestinal fibrosis and stricture in CD through various modalities, including endoscopy, imaging examination, and serological biomarkers. We reviewed the articles on AI application in diagnosing intestinal fibrosis and stricture in CD during the past decade and categorized them into three aspects based on the detection methods, and found that AI helps accurate and expedient identification and prediction of intestinal fibrosis and stenosis in CD.

Charting new AI education in gastroenterology: Cross-sectional evaluation of ChatGPT and perplexity AI in medical residency exam

BACKGROUND

Conversational chatbots, fueled by large language models, spark debate over their potential in education and medical career exams. There is debate in the literature about the scientific integrity of the outputs produced by these chatbots.

AIMS

This study evaluates ChatGPT 3.5 and Perplexity AI's cross-sectional performance in responding to questions from the 2023 Italian national residency admission exam (SSM23), comparing results and chatbots' concordance with previous years SSMs.

METHODS

Gastroenterology-related SSM23 questions were input into ChatGPT 3.5 and Perplexity AI, evaluating their performance in correct responses and total scores. This process was repeated with questions from the three preceding years. Additionally, chatbot concordance was assessed using Cohen's method.

RESULTS

In SSM23, ChatGPT 3.5 outperforms Perplexity AI with 94.11% correct responses, demonstrating consistency across years. Concordance weakened in 2023 (κ=0.203, P = 0.148), but ChatGPT consistently maintains a high standard compared to Perplexity AI.

CONCLUSION

ChatGPT 3.5 and Perplexity AI exhibit promise in addressing gastroenterological queries, emphasizing potential educational roles. However, their variable performance mandates cautious use as supplementary tools alongside conventional study methods. Clear guidelines are crucial for educators to balance traditional approaches and innovative systems, enhancing educational standards.

Are Internet Information Sources Helpful for Adult Crohn's Disease Patients Regarding Nutritional Advice?

Background: Adult patients suffering from Crohn's disease (CD) are often dissatisfied with the information they receive from their physicians about nutrition and its impact on CD inflammation activity. Only a few publications are available about patients' internet research on nutrition in CD. The study aim is to elucidate the internet information sources of adult CD patients regarding nutritional advice via a questionnaire. Methods: A questionnaire with 28 (general and specific) questions for outpatients at our tertiary center with CD was created and used for an analysis of their information sources about nutrition in CD. Four CD and/or nutritional medicine experts examined the 21 most relevant websites referring to nutritional advice for CD patients. Results: One hundred and fifty CD patients reported their Internet research behavior for nutritional advice and their dietary habits. Many CD patients prefer to consult the Internet instead of asking their general practitioner (GP) for nutritional recommendations. Most of the websites providing nutritional advice for CD patients are of very poor quality and cannot be recommended. We found significant correlations between (a) nutritional habits of CD patients, (b) their information sources and several demographic or CD-related factors. There is a lack of websites which provide high-quality, good nutritional advice to CD patients. Conclusions: The majority of the examined websites did not provide sufficient information according to the CD guidelines and nutritional medicine guidelines. A higher quality level of website content (e.g., on social media or on university/center websites) provided by experienced physicians is required to secure trustworthy and reliable nutritional information in CD.