AIMC Topic: Cross-Sectional Studies
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The clinical significance of an AI-based assumption model for neurocognitive diseases using a novel dual-task system.
Scientific reports
Apr 22, 2025
Dual-task composed of gait or stepping tasks combined with cognitive tasks has been well-established as valuable tools for detecting neurocognitive disorders such as mild cognitive impairment and early-stage Alzheimer's disease. We previously develop...
Knowledge and use, perceptions of benefits and limitations of artificial intelligence chatbots among Italian physiotherapy students: a cross-sectional national study.
BMC medical education
Apr 18, 2025
BACKGROUND: Artificial Intelligence (AI) Chatbots (e.g., ChatGPT, Microsoft Bing, and Google Bard) can emulate human interaction and may support physiotherapy education. Despite growing interest, physiotherapy students' perspectives remain unexplored...
Exploring the influence of artificial intelligence integration on personalized learning: a cross-sectional study of undergraduate medical students in the United Kingdom.
BMC medical education
Apr 17, 2025
BACKGROUND: With the integration of Artificial Intelligence (AI) into educational systems, its potential to revolutionize learning, particularly in content personalization and assessment support, is significant. Personalized learning, supported by AI...
Acoustic and Natural Language Markers for Bipolar Disorder: A Pilot, mHealth Cross-Sectional Study.
JMIR formative research
Apr 16, 2025
BACKGROUND: Monitoring symptoms of bipolar disorder (BD) is a challenge faced by mental health services. Speech patterns are crucial in assessing the current experiences, emotions, and thought patterns of people with BD. Natural language processing (...
Heavy metal biomarkers and their impact on hearing loss risk: a machine learning framework analysis.
Frontiers in public health
Apr 16, 2025
BACKGROUND: Exposure to heavy metals has been implicated in adverse auditory health outcomes, yet the precise relationships between heavy metal biomarkers and hearing status remain underexplored. This study leverages a machine learning framework to i...
Comparative evaluation of artificial intelligence models GPT-4 and GPT-3.5 in clinical decision-making in sports surgery and physiotherapy: a cross-sectional study.
BMC medical informatics and decision making
Apr 14, 2025
BACKGROUND: The integration of artificial intelligence (AI) in healthcare has rapidly expanded, particularly in clinical decision-making. Large language models (LLMs) such as GPT-4 and GPT-3.5 have shown potential in various medical applications, inc...
The role of patient outcomes in shaping moral responsibility in AI-supported decision making.
Radiography (London, England : 1995)
Apr 13, 2025
INTRODUCTION: Integrating decision support mechanisms utilising artificial intelligence (AI) into medical radiation practice introduces unique challenges to accountability for patient care outcomes. AI systems, often seen as "black boxes," can obscur...
Principles for enhancing trust in artificial intelligence systems among medical imaging professionals in Ghana: A nationwide cross-sectional study.
Radiography (London, England : 1995)
Apr 13, 2025
INTRODUCTION: To realise the full potential of artificial intelligence (AI) systems in medical imaging, it is crucial to address challenges, such as cyberterrorism to foster trust and acceptance. This study aimed to determine the principles that enha...
Machine learning models for improving the diagnosing efficiency of skeletal class I and III in German orthodontic patients.
Scientific reports
Apr 13, 2025
The precise and efficient diagnosis of an individual's skeletal class is necessary in orthodontics to ensure correct and stable treatment planning. However, it is difficult to efficiently determine the true skeletal class due to several correlations ...
Neuroimaging-derived biological brain age and its associations with glial reactivity and synaptic dysfunction cerebrospinal fluid biomarkers.
Molecular psychiatry
Apr 12, 2025
Magnetic resonance Imaging (MRI)-derived brain-age prediction is a promising biomarker of biological brain aging. Accelerated brain aging has been found in Alzheimer's disease (AD) and other neurodegenerative diseases. However, no previous studies ha...
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