AIMC Topic: Humans

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The impact of digital competence on pedagogical innovation among nurse educators: The moderating role of artificial intelligence readiness.

Nurse education in practice
AIM: To investigate the relationships between digital competence, AI readiness and pedagogical innovation among nurse educators, with a specific focus on the moderating role of AI readiness.

A Causality-Aware Paradigm for Evaluating Creativity of Multimodal Large Language Models.

IEEE transactions on pattern analysis and machine intelligence
Recently, numerous benchmarks have been developed to evaluate the logical reasoning abilities of large language models (LLMs). However, assessing the equally important creative capabilities of LLMs is challenging due to the subjective, diverse, and d...

Aligning, Autoencoding and Prompting Large Language Models for Novel Disease Reporting.

IEEE transactions on pattern analysis and machine intelligence
Given radiology images, automatic radiology report generation aims to produce informative text that reports diseases. It can benefit current clinical practice in diagnostic radiology. Existing methods typically rely on large-scale medical datasets an...

NeuroNasal: Advanced AI-Driven Self-Supervised Learning Approach for Enhanced Sinonasal Pathology Detection.

Sensors (Basel, Switzerland)
Sinus diseases are inflammations or infections of the sinuses that significantly impact patient quality of life. They cause nasal congestion, facial pain, headaches, thick nasal discharge, and a reduced sense of smell. However, accurately diagnosing ...

Deep Learning-Based Medical Ultrasound Image and Video Segmentation Methods: Overview, Frontiers, and Challenges.

Sensors (Basel, Switzerland)
The intricate imaging structures, artifacts, and noise present in ultrasound images and videos pose significant challenges for accurate segmentation. Deep learning has recently emerged as a prominent field, playing a crucial role in medical image pro...

A Multitask CNN for Near-Infrared Probe: Enhanced Real-Time Breast Cancer Imaging.

Sensors (Basel, Switzerland)
The early detection of breast cancer, particularly in dense breast tissues, faces significant challenges with traditional imaging techniques such as mammography. This study utilizes a Near-infrared Scan (NIRscan) probe and an advanced convolutional n...

Application of machine learning in assessing disease activity in SLE.

Lupus science & medicine
OBJECTIVE: SLE is a chronic autoimmune disease with immune complex deposition in various organs, causing inflammation. The Systemic Lupus Erythematosus Disease Activity Index 2000 assesses disease severity but is subjective. This study aimed to const...

The potential of artificial intelligence reading label system on the training of ophthalmologists in retinal diseases, a multicenter bimodal multi-disease study.

BMC medical education
OBJECTIVE: To assess the potential of artificial intelligence reading label system on the training of ophthalmologists in a multicenter bimodal multi-disease study.

Automatic identification of hard and soft tissue landmarks in cone-beam computed tomography via deep learning with diversity datasets: a methodological study.

BMC oral health
BACKGROUND: Manual landmark detection in cone beam computed tomography (CBCT) for evaluating craniofacial structures relies on medical expertise and is time-consuming. This study aimed to apply a new deep learning method to predict and locate soft an...