Work Force

Enhancing Efficiency with an AI-Augmented Clinician in Neurology.

Integrating artificial intelligence (AI) technologies into neurology promises increased patient access, engagement, and quality of care, as well as improved quality of work life for clinicians. While most studies have focused on comparing AI models to expert performance, we argue for a more practical approach: demonstrating how AI can augment clinical practice. This article presents a framework fo...

A deep learning algorithm that aids visualization of femoral neck fractures and improves physician training.

PURPOSE: Missed fractures are the most common radiologic error in clinical practice, and erroneous classification could lead to inappropriate treatment and unfavorable prognosis. Here, we developed a fully automated deep learning model to detect and classify femoral neck fractures using plain radiographs, and evaluated its utility for diagnostic assistance and physician training.

Advancing Neurocritical Care with Artificial Intelligence and Machine Learning: The Promise, Practicalities, and Pitfalls ahead.

Expansion of artificial intelligence (AI) in the field of medicine is changing the paradigm of clinical practice at a rapid pace. Incorporation of AI ...

Federated learning-based prediction of depression among adolescents across multiple districts in China.

Depression in adolescents is a serious mental health condition that can affect their emotional and social well-being. Detailed understanding of depres...

Enhanced cancer classification and critical feature visualization using Raman spectroscopy and convolutional neural networks.

Cell misuse and cross-contamination pose a significant threat to the accuracy of cell research outcomes, often leading to the wasteful expenditure of ...

Effectiveness of data-augmentation on deep learning in evaluating rapid on-site cytopathology at endoscopic ultrasound-guided fine needle aspiration.

Rapid on-site cytopathology evaluation (ROSE) has been considered an effective method to increase the diagnostic ability of endoscopic ultrasound-guid...

Generalizability assessment of AI models across hospitals in a low-middle and high income country.

The integration of artificial intelligence (AI) into healthcare systems within low-middle income countries (LMICs) has emerged as a central focus for ...

CardioGuard: AI-driven ECG authentication hybrid neural network for predictive health monitoring in telehealth systems.

The increasing integration of telehealth systems underscores the importance of robust and secure methods for patient data management. Traditional auth...

Semi-supervised recognition for artificial intelligence assisted pathology image diagnosis.

The analysis and interpretation of cytopathological images are crucial in modern medical diagnostics. However, manually locating and identifying relev...

Investigating Older Adults' Use of a Socially Assistive Robot via Time Series Clustering and User Profiling: Descriptive Analysis Study.

BACKGROUND: The aging population and the shortage of geriatric care workers are major global concerns. Socially assistive robots (SARs) have the poten...

Teaching Motor Skills Without a Motor: A Semi-Passive Robot to Facilitate Learning.

Semi-passive rehabilitation robots resist and steer a patient's motion using only controllable passive force elements (e.g., controllable brakes). Con...

Improving Human Activity Recognition With Wearable Sensors Through BEE: Leveraging Early Exit and Gradient Boosting.

Early-exiting has recently provided an ideal solution for accelerating activity inference by attaching internal classifiers to deep neural networks. I...

Editorial Commentary: The Scope of Medical Research Concerning ChatGPT Remains Limited by Lack of Originality.

There is no shortage of literature surrounding ChatGPT and whether this large language model can provide accurate and clinically relevant information ...

Influence of next-generation artificial intelligence on headache research, diagnosis and treatment: the junior editorial board members' vision - part 1.

Artificial intelligence (AI) is revolutionizing the field of biomedical research and treatment, leveraging machine learning (ML) and advanced algorith...

Leveraging AI for the diagnosis and treatment of autism spectrum disorder: Current trends and future prospects.

The integration of artificial intelligence (AI) into the diagnosis and treatment of autism spectrum disorder (ASD) represents a promising frontier in ...

6G in medical robotics: development of network allocation strategies for a telerobotic examination system.

PURPOSE: Healthcare systems around the world are increasingly facing severe challenges due to problems such as staff shortage, changing demographics a...

Artificial Intelligence in the Training of Radiology Residents: a Multicenter Randomized Controlled Trial.

The aim of the present study was to compare the effectiveness of AI-assisted training and conventional human training in clinical practice. This was a...

Application and innovation of artificial intelligence models in wastewater treatment.

At present, as the problem of water shortage and pollution is growing serious, it is particularly important to understand the recycling and treatment ...

Improving viral annotation with artificial intelligence.

Viruses of bacteria, "phages," are fundamental, poorly understood components of microbial community structure and function. Additionally, their depend...

Machine learning-assisted optimization of food-grade spirulina cultivation in seawater-based media: From laboratory to large-scale production.

The shortage of food and freshwater sources threatens human health and environmental sustainability. Spirulina grown in seawater-based media as a heal...