Background: In view of the growing complexity of managing anticoagulation for patients undergoing gastrointestinal (GI) procedures, this study evaluated ChatGPT-4's ability to provide accurate medical guidance, comparing it with its prior artificial intelligence (AI) models (ChatGPT-3.5) and the retrieval-augmented generation (RAG)-supported model (ChatGPT4-RAG).
Methods: Thirty-six anticoagulation-related questions, based on professional guidelines, were answered by ChatGPT-4. Nine gastroenterologists assessed these responses for accuracy and relevance. ChatGPT-4's performance was also compared to that of ChatGPT-3.5 and ChatGPT4-RAG. Additionally, a survey was conducted to understand gastroenterologists' perceptions of ChatGPT-4.
Results: ChatGPT-4's responses showed significantly better accuracy and coherence compared to ChatGPT-3.5, with 30.5% of responses fully accurate and 47.2% generally accurate. ChatGPT4-RAG demonstrated a higher ability to integrate current information, achieving 75% full accuracy. Notably, for diagnostic and therapeutic esophagogastroduodenoscopy, 51.8% of responses were fully accurate; for endoscopic retrograde cholangiopancreatography with and without stent placement, 42.8% were fully accurate; and for diagnostic and therapeutic colonoscopy, 50% were fully accurate.
Conclusions: ChatGPT4-RAG significantly advances anticoagulation management in endoscopic procedures, offering reliable and precise medical guidance. However, medicolegal considerations mean that a 75% full accuracy rate remains inadequate for independent clinical decision-making. AI may be more appropriately utilized to support and confirm clinicians' decisions, rather than replace them. Further evaluation is essential to maintain patient confidentiality and the integrity of the physician-patient relationship.
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http://dx.doi.org/10.20524/aog.2024.0907 | DOI Listing |
Comput Biol Med
January 2025
Department of Artificial Intelligence, Faculty of Artificial Intelligence, Egyptian Russian University, 11829, Badr City, Egypt. Electronic address:
Weakly-supervised learning (WSL) methods have gained significant attention in medical image segmentation, but they often face challenges in accurately delineating boundaries due to overfitting to weak annotations such as bounding boxes. This issue is particularly pronounced in thyroid ultrasound images, where low contrast and noisy backgrounds hinder precise segmentation. In this paper, we propose a novel weakly-supervised segmentation framework that addresses these challenges.
View Article and Find Full Text PDFBiosens Bioelectron
January 2025
School of Clinical Medicine, Discipline of Women's Health, Faculty of Medicine, University of New South Wales, Royal Hospital for Women, Sydney, Australia; Department of Maternal-Fetal Medicine, Royal Hospital for Women, Sydney, Australia. Electronic address:
Diabetes and cardiovascular disease are interlinked chronic conditions that necessitate continuous and precise monitoring of physiological and environmental parameters to prevent complications. Non-invasive monitoring technologies have garnered significant interest due to their potential to alleviate the current burden of diabetes and cardiovascular disease management. However, these technologies face limitations in accuracy and reliability due to interferences from physiological and environmental factors.
View Article and Find Full Text PDFBackground: This study aimed to evaluate the efficacy of third-generation sequencing (TGS) and a thalassemia (Thal) gene diagnostic kit in identifying Thal gene mutations.
Methods: Blood samples (n = 119) with positive hematology screening results were tested using polymerase chain reaction (PCR)-based methods and TGS on the PacBio-Sequel-II-platform, respectively.
Results: Out of the 119 cases, 106 cases showed fully consistent results between the two methods, with TGS identified HBA1/2 and HBB gene mutations in 82 individuals.
J Cheminform
January 2025
Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, UK.
Current strategies centred on either merging or linking initial hits from fragment-based drug design (FBDD) crystallographic screens generally do not fully leaverage 3D structural information. We show that an algorithmic approach (Fragmenstein) that 'stitches' the ligand atoms from this structural information together can provide more accurate and reliable predictions for protein-ligand complex conformation than general methods such as pharmacophore-constrained docking. This approach works under the assumption of conserved binding: when a larger molecule is designed containing the initial fragment hit, the common substructure between the two will adopt the same binding mode.
View Article and Find Full Text PDFBMC Psychiatry
January 2025
Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institutet, & Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden.
Depression is one of the most common psychiatric conditions. Given its high prevalence and disease burden, accurate diagnostic procedures and valid instruments are warranted to identify those in need of treatment. The Patient Health Questionnaire-9 (PHQ-9) is one of the most widely used self-report measures of depression, and its validity and reliability has been evaluated in several languages.
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