Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
Various artificial intelligence-based applications have emerged as transformative tools across numerous domains. Among these, ChatGPT has earned global recognition with its capacity for dynamic user interaction and holds significant potential in the medical sector. However, the subject-specific accuracy of ChatGPT remains a matter of debate. This study assesses the capabilities and knowledge of different artificial intelligence chatbots (ChatGPT, Google Gemini, and Claude) in the domain of hand surgery. Each chatbot conducted a full written EBHS exam. The test results were analyzed according to the EBHS-guidelines, focused on the total scores and the ratio of correct to incorrect responses for each artificial intelligence model. Findings revealed that three out of the four chatbots achieved passing scores on the exam. Notably, ChatGPT-4o1 demonstrated significantly superior performance. This study highlights the subject-specific expertise of different artificial intelligence programs within the specialized field of hand surgery while also underscoring their variability and limitations.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.hansur.2025.102197 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Predicting In-Hospital Cardiac Arrest Using Machine Learning Models: Protocol for a Scoping Review.
JMIR Res Protoc
September 2025
University of Nevada, Las Vegas, Las Vegas, NV, United States.
Background: In-hospital cardiac arrest (IHCA) remains a public health conundrum with high morbidity and mortality rates. While early identification of high-risk patients could enable preventive interventions and improve survival, evidence on the effectiveness of current prediction methods remains inconclusive. Limited research exists on patients' prearrest pathophysiological status and predictive and prognostic factors of IHCA, highlighting the need for a comprehensive synthesis of predictive methodologies.
View Article and Find Full Text PDFExploring Online Health Information-Seeking Behavior Among Young Adults: Scoping Review.
J Med Internet Res
September 2025
Department of Community Medicine, Faculty of Health, UiT The Arctic University of Norway, Tromsø, Norway.
Background: The ability to access and evaluate online health information is essential for young adults to manage their physical and mental well-being. With the growing integration of the internet, mobile technology, and social media, young adults (aged 18-30 years) are increasingly turning to digital platforms for health-related content. Despite this trend, there remains a lack of systematic insights into their specific behaviors, preferences, and needs when seeking health information online.
View Article and Find Full Text PDFThe impact of artificial intelligence on biomarker discovery.
Emerg Top Life Sci
September 2025
Hurdle.bio / Chronomics Ltd., London, UK.
Artificial intelligence (AI) is transforming many fields, including healthcare and medicine. In biomarker discovery, AI algorithms have had a profound impact, thanks to their ability to derive insights from complex high-dimensional datasets and integrate multi-modal datatypes (such as omics, electronic health records, imaging or sensor and wearable data). However, despite the proliferation of AI-powered biomarkers, significant hurdles still remain in translating them to the clinic and driving adoption, including lack of population diversity, difficulties accessing harmonised data, costly and time-consuming clinical studies, evolving AI regulatory frameworks and absence of scalable diagnostic infrastructure.
View Article and Find Full Text PDFEvaluating Posterior Vitreous Detachment Annotation Consistency on OCT Scans in Patients with Disease of the Vitreomacular Interface.
Retina
September 2025
Department of Ophthalmology, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 15, CH-3010.
Purpose: To evaluate inter-grader variability in posterior vitreous detachment (PVD) classification in patients with epiretinal membrane (ERM) and macular hole (MH) on spectral-domain optical coherence tomography (SD-OCT) and identify challenges in defining a reliable ground truth for artificial intelligence (AI)-based tools.
Methods: A total of 437 horizontal SD-OCT B-scans were retrospectively selected and independently annotated by six experienced ophthalmologists adopting four categories: 'full PVD', 'partial PVD', 'no PVD', and 'ungradable'. Inter-grader agreement was assessed using pairwise Cohen's kappa scores.
[Ai's use in health care and informed consent].
Cuad Bioet
September 2025
Universidad de A Coruña. Facultad de Derecho, Campus de Elviña, s/n, 15071, A Coruña. 981 167000 ext. 1640
The implications of the use of artificial intelligence (AI) in many areas of human existence compels us to reflect on its ethical relevance. This paper addresses the signification of its use in healthcare for patient informed consent. To this end, it first proposes an understanding of AI, as well as the basis for informed consent.
View Article and Find Full Text PDF