Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.jvs.2019.03.078 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Event Reduction With Obicetrapib: A Word Of Caution.
J Am Coll Cardiol
August 2025
Division of Cardiovascular Disease, University of Alabama, Birmingham, Alabama, USA. Electronic address:
Sensitivity and Specificity of a Revised Version of the TRACK-MS Screening Battery for Early Detection of Cognitive Impairment in Patients with Multiple Sclerosis.
Biomedicines
August 2025
Department of Neurology, Faculty of Medicine, Ulm University, D-89081 Ulm, Germany.
: Cognitive impairment is one of the most common and debilitating clinical features of Multiple Sclerosis (MS). Neuropsychological assessment, however, is time-consuming and requires personal resources, so, due to limited resources in daily clinical practice, information on cognitive profiles is often lacking, despite its high prognostic relevance. Time-saving and effective tools are required to bridge this gap.
View Article and Find Full Text PDFCan DSP Mutation Carriers Safely Participate in Sport Activity?: A Word of Caution.
JACC Clin Electrophysiol
July 2025
Cardiovascular Pathology Unit, Department of Cardiac, Thoracic and Vascular Sciences and Public Health, University of Padua, Padua, Italy. Electronic address:
The role of indocyanine green in the diagnosis of biliary atresia: A word of caution.
BMJ Case Rep
August 2025
Pediatric Surgery, Children's Nebraska, Omaha, Nebraska, USA.
Indocyanine green (ICG) fluorescence imaging has emerged as a potential tool in evaluating biliary atresia, offering real-time visualisation of hepatobiliary excretion. Following intravenous administration, ICG is taken up by hepatocytes and excreted into bile, allowing assessment of biliary patency. In biliary atresia, absent or delayed fluorescence in the intestine may suggest obstruction.
View Article and Find Full Text PDFMulti-model assurance analysis showing large language models are highly vulnerable to adversarial hallucination attacks during clinical decision support.
Commun Med (Lond)
August 2025
The Windreich Department of Artificial Intelligence and Human Health, Mount Sinai Medical Center, New York, NY, USA.
Background: Large language models (LLMs) show promise in clinical contexts but can generate false facts (often referred to as "hallucinations"). One subset of these errors arises from adversarial attacks, in which fabricated details embedded in prompts lead the model to produce or elaborate on the false information. We embedded fabricated content in clinical prompts to elicit adversarial hallucination attacks in multiple large language models.
View Article and Find Full Text PDF