AIMC Topic: Humans

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MRI super-resolution reconstruction using efficient diffusion probabilistic model with residual shifting.

Physics in medicine and biology
Magnetic resonance imaging (MRI) is essential in clinical and research contexts, providing exceptional soft-tissue contrast. However, prolonged acquisition times often lead to patient discomfort and motion artifacts. Diffusion-based deep learning sup...

Engineered multi-domain lipid nanoparticles for targeted delivery.

Chemical Society reviews
Engineered lipid nanoparticles (LNPs) represent a breakthrough in targeted drug delivery, enabling precise spatiotemporal control essential to treat complex diseases such as cancer and genetic disorders. However, the complexity of the delivery proces...

The Use of Artificial Intelligence in Residency Application Evaluation-A Scoping Review.

Journal of graduate medical education
Several residency programs have begun investigating artificial intelligence (AI) methods to facilitate application screening processes. However, no unifying guidelines for these methods exist. We sought to perform a scoping review of AI model devel...

The utility of an artificial intelligence model based on decision tree and evolution algorithm to evaluate steatotic liver disease in a primary care setting.

Brazilian journal of medical and biological research = Revista brasileira de pesquisas medicas e biologicas
Many ways of classifying steatotic liver disease (SLD) with metabolic conditions have been proposed. Thus, SLD-related variables were verified using a decision tree. We tested if the suggested components of the actual classification (metabolic dysfun...

Predicting Coronary Heart Disease Using Data Mining and Machine Learning Solutions.

Anais da Academia Brasileira de Ciencias
This research focuses on predicting cardiovascular disease using machine learning classification strategies. The study presents a unique approach by integrating multiple machine learning techniques, leveraging the strengths of Random Forest and Gradi...

Foster noisy label learning by exploiting noise-induced distortion in foreground localization.

Neural networks : the official journal of the International Neural Network Society
Large-scale, well-annotated datasets are crucial for training deep neural networks. However, the prevalence of noisy-labeled samples can cause irreversible impairment to the generalization of models. Existing approaches have attempted to mitigate the...

Analysis of food safety based on machine learning: A comprehensive review and future prospects.

Food chemistry
Food safety challenges escalate with global population growth and complex supply chains. Traditional analytical methods, though precise, face limitations in speed and adaptability. Machine learning (ML) offers data-driven solutions, excelling in cont...

Shall we call for a doctor? How to build trust toward AI in healthcare: Insights from a Polish cross-sectional preference study.

Health policy (Amsterdam, Netherlands)
OBJECTIVES: This research aimed to investigate key success factors for the adoption of AI-driven health technologies, particularly in healthcare ecosystems of low digital literacy, such as Poland.

High-Sensitivity Detection of C-Peptide Biomarker for Diabetes by Solid-State Nanopore Using Machine Learning Identification.

The journal of physical chemistry letters
Accurate and early detection of C-peptide, a stable biomarker indicative of diabetes, is crucial for disease diagnosis, treatment, and prevention. This study explores a novel detection methodology using solid-state nanopore technology coupled with ma...