AIMC Topic: Humans

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Enhanced fracture detection on radiographs with AI assistance for clinicians: a systematic review and meta-analysis.

Annals of medicine
BACKGROUND: Emergency radiographic interpretation for fractures is prone to missed or misdiagnoses. Artificial intelligence (AI) is expected to become a powerful tool to assist clinicians in fracture detection.

Stress detection using the phase controlled Bi-channel adaptive features from the brain EEG signals.

Computers in biology and medicine
This work proposes a stress classification system from the electroencephalogram (EEG) signals collected from the stress subjects. The scheme extracts the phase-controlled Bi-channel adaptive features using a pair of EEG signals. The proposed adaptive...

What Drives Microplastic Exposure in Human Blood and Feces? Machine Learning Reveals Potential Key Influencing Factors.

Environmental science & technology
Microplastics are pervasive environmental pollutants, making human exposure unavoidable. Although previous studies have detected microplastics in human blood and feces, these investigations were limited by small sample sizes and key contributors to m...

Path-Based Graph Neural Network for Drug Synergy Prediction and Interpretation.

Journal of chemical information and modeling
Combination therapy is a method of treating complex diseases by using multiple drugs, which has the advantages of good efficacy and few toxic side effects. It has been widely used in clinical research. The significant increase in the number of drug c...

Artificial intelligence guidance in ethically challenging clinical scenarios in child and adolescent psychiatry: a qualitative study in the context of Turkiye.

BMC medical ethics
BACKGROUND: Ethical decision-making in child and adolescent psychiatry (CAP) is inherently complex, shaped by developmental vulnerability, evolving autonomy, and competing responsibilities to patients, families, and the legal system. Clinicians often...

How perceived stress and social support shape non-communicable disease risks beyond traditional factors: a machine learning perspective.

BMC public health
BACKGROUND: Psychosocial factors such as perceived stress and social support have been increasingly recognized as significant contributors to non-communicable diseases (NCDs). However, their predictive value in comparison to traditional risk factors ...

Multimodal deep learning ensemble framework for skin cancer detection.

Scientific reports
Skin cancer is the abnormal growth of skin cells, most often developing on skin exposed to the sun. It is among the most fatal forms of cancer, making its early detection and therapy crucial. In addition to conventional techniques, deep learning meth...

Determinants and outcomes of advanced practice nurses' leadership behaviours: an AI-aided mixed-methods systematic review protocol.

Systematic reviews
BACKGROUND: Advanced practice nurses play a vital role in healthcare innovation, delivering high-quality care and improving patient outcomes. Leadership is a core competency of advanced practice nurses, empowering them to drive systemic improvements ...

Artificial intelligence in the workplace: a living systematic review protocol on worker safety, health, and well-being implications.

Systematic reviews
BACKGROUND: Advancements in artificial intelligence (AI) are transforming employment and working conditions in ways that shape the safety, health, and well-being of workers. We describe a protocol for a living systematic review (LSR) that will examin...

Evaluation of visual patient predictive for enhancing level 3 situation awareness: protocol for a multicentre randomised computer-based simulation and diagnostic accuracy study (true positive rate, precision, average lead time).

BMJ open
INTRODUCTION: Visual Patient Predictive (VPP) is an AI-based extension of the Visual Patient Avatar (VPA) that integrates deep learning models to predict upcoming vital sign deviations and display them as dashed visual elements. By explicitly showing...