Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11665836 | PMC |
| http://dx.doi.org/10.1148/radiol.233465 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
ChatGPT-4o is Not a Reliable Study Source for Orthopaedic Surgery Residents.
JB JS Open Access
September 2025
Department of Orthopaedic Surgery, St. Luke's University Health Network, Bethlehem, Pennsylvania.
Background: The use of artificial intelligence platforms by medical residents as an educational resource is increasing. Within orthopaedic surgery, older Chat Generative Pre-trained Transformer (ChatGPT) models performed worse than resident physicians on practice examinations and rarely answered questions with images correctly. The newer ChatGPT-4o was designed to improve these deficiencies but has not been evaluated.
View Article and Find Full Text PDFArtificial Intelligence in Cardiac Treatment Decision-Making: An Evaluation of the Performance of ChatGPT Versus the Heart Team in Coronary Revascularization.
Rev Cardiovasc Med
August 2025
Cardiovascular Surgery Department, Ankara Bilkent City Hospital, 06800 Ankara, Turkey.
Background: This study aimed to investigate the performance of two versions of ChatGPT (o1 and 4o) in making decisions about coronary revascularization and to compare the recommendations of these versions with those of a multidisciplinary Heart Team. Moreover, the study aimed to assess whether the decisions generated by ChatGPT, based on the internal knowledge base of the system and clinical guidelines, align with expert recommendations in real-world coronary artery disease management. Given the increasing prevalence and processing capabilities of large language models, such as ChatGPT, this comparison offers insights into the potential applicability of these systems in complex clinical decision-making.
View Article and Find Full Text PDFComparative performance of neurosurgery-specific, peer-reviewed versus general AI chatbots in bilingual board examinations: evaluating accuracy, consistency, and error minimization strategies.
Acta Neurochir (Wien)
September 2025
Department of Neurosurgery, Istinye University, Istanbul, Turkey.
Background: Recent studies suggest that large language models (LLMs) such as ChatGPT are useful tools for medical students or residents when preparing for examinations. These studies, especially those conducted with multiple-choice questions, emphasize that the level of knowledge and response consistency of the LLMs are generally acceptable; however, further optimization is needed in areas such as case discussion, interpretation, and language proficiency. Therefore, this study aimed to evaluate the performance of six distinct LLMs for Turkish and English neurosurgery multiple-choice questions and assess their accuracy and consistency in a specialized medical context.
View Article and Find Full Text PDFMultiple large language models versus clinical guidelines for postmenopausal osteoporosis: a comparative study of ChatGPT-3.5, ChatGPT-4.0, ChatGPT-4o, Google Gemini, Google Gemini Advanced, and Microsoft Copilot.
Arch Osteoporos
September 2025
Department of Family Medicine, Chang-Gung Memorial Hospital, Linkou Branch, Taoyuan City, Taiwan.
Unlabelled: The study assesses the performance of AI models in evaluating postmenopausal osteoporosis. We found that ChatGPT-4o produced the most appropriate responses, highlighting the potential of AI to enhance clinical decision-making and improve patient care in osteoporosis management.
Purpose: The rise of artificial intelligence (AI) offers the potential for assisting clinical decisions.
Interpreting BI-RADS-Free Breast MRI Reports Using a Large Language Model: Automated BI-RADS Classification From Narrative Reports Using ChatGPT.
Acad Radiol
September 2025
Department of Radiology, Başakşehir Çam and Sakura City Hospital, Istanbul, Turkey (E.E.).
Purpose: This study aimed to evaluate the performance of ChatGPT (GPT-4o) in interpreting free-text breast magnetic resonance imaging (MRI) reports by assigning BI-RADS categories and recommending appropriate clinical management steps in the absence of explicitly stated BI-RADS classifications.
Methods: In this retrospective, single-center study, a total of 352 documented full-text breast MRI reports of at least one identifiable breast lesion with descriptive imaging findings between January 2024 and June 2025 were included in the study. Incomplete reports due to technical limitations, reports describing only normal findings, and MRI examinations performed at external institutions were excluded from the study.