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Leveraging two-dimensional pre-trained vision transformers for three-dimensional model generation via masked autoencoders.
Sci Rep
January 2025
Department of Electrical Power, Adama Science and Technology University, Adama, 1888, Ethiopia.
Although the Transformer architecture has established itself as the industry standard for jobs involving natural language processing, it still has few uses in computer vision. In vision, attention is used in conjunction with convolutional networks or to replace individual convolutional network elements while preserving the overall network design. Differences between the two domains, such as significant variations in the scale of visual things and the higher granularity of pixels in images compared to words in the text, make it difficult to transfer Transformer from language to vision.
View Article and Find Full Text PDFHigh definition transcranial direct current stimulation as an intervention for cognitive deficits in Alzheimer's dementia: A randomized controlled trial.
J Prev Alzheimers Dis
February 2025
Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, USA; Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX, USA; School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX, USA.
Background: Recent disease-modifying treatments for Alzheimer's disease show promise to slow cognitive decline, but show no efficacy towards reducing symptoms already manifested.
Objectives: To investigate the efficacy of a novel noninvasive brain stimulation technique in modulating cognitive functioning in Alzheimer's dementia (AD).
Design: Pilot, randomized, double-blind, parallel, sham-controlled study SETTING: Clinical research site at UT Southwestern Medical Center PARTICIPANTS: Twenty-five participants with clinical diagnoses of AD were enrolled from cognition specialty clinics.
Assessing the performance of Microsoft Copilot, GPT-4 and Google Gemini in ophthalmology.
Can J Ophthalmol
January 2025
Faculty of Medicine, University of Montreal, Montreal, QB, Canada; Department of Ophthalmology, Centre Hospitalier de l'Université de Montréal, Montreal, QB, Canada. Electronic address:
Objective: To evaluate the performance of large language models (LLMs), specifically Microsoft Copilot, GPT-4 (GPT-4o and GPT-4o mini), and Google Gemini (Gemini and Gemini Advanced), in answering ophthalmological questions and assessing the impact of prompting techniques on their accuracy.
Design: Prospective qualitative study.
Participants: Microsoft Copilot, GPT-4 (GPT-4o and GPT-4o mini), and Google Gemini (Gemini and Gemini Advanced).
ARCH: Large-scale knowledge graph via aggregated narrative codified health records analysis.
J Biomed Inform
January 2025
Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, 02115, MA, USA; VA Boston Healthcare System, 150 S Huntington Ave, Boston, 02130, MA, USA. Electronic address:
Objective: Electronic health record (EHR) systems contain a wealth of clinical data stored as both codified data and free-text narrative notes (NLP). The complexity of EHR presents challenges in feature representation, information extraction, and uncertainty quantification. To address these challenges, we proposed an efficient Aggregated naRrative Codified Health (ARCH) records analysis to generate a large-scale knowledge graph (KG) for a comprehensive set of EHR codified and narrative features.
View Article and Find Full Text PDFPerformance of large language models in rheumatology board-like questions: accuracy, quality, and safety.
Lancet Rheumatol
January 2025
Division of Rheumatology, Mayo Clinic, Rochester, MN 55905, USA. Electronic address:
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