AIMC Topic: Humans

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Laboratory Data as a Potential Source of Bias in Healthcare Artificial Intelligence and Machine Learning Models.

Annals of laboratory medicine
Artificial intelligence (AI) and machine learning (ML) are anticipated to transform the practice of medicine. As one of the largest sources of digital data in healthcare, laboratory results can strongly influence AI and ML algorithms that require lar...

AI-Based solutions for current challenges in regenerative medicine.

European journal of pharmacology
The emergence of Artificial Intelligence (AI) and its usage in regenerative medicine represents a significant opportunity that holds the promise of tackling critical challenges and improving therapeutic outcomes. This article examines the ways in whi...

Robust brain MRI image classification with SIBOW-SVM.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Primary Central Nervous System tumors in the brain are among the most aggressive diseases affecting humans. Early detection and classification of brain tumor types, whether benign or malignant, glial or non-glial, is critical for cancer prevention an...

Invited Review for 20th Anniversary Special Issue of PLRev "AI for Mechanomedicine".

Physics of life reviews
Mechanomedicine is an interdisciplinary field that combines different areas including biomechanics, mechanobiology, and clinical applications like mechanodiagnosis and mechanotherapy. The emergence of artificial intelligence (AI) has revolutionized m...

Key genes and pathways in the molecular landscape of pancreatic ductal adenocarcinoma: A bioinformatics and machine learning study.

Computational biology and chemistry
Pancreatic ductal adenocarcinoma (PDAC) is recognized for its aggressive nature, dismal prognosis, and a notably low five-year survival rate, underscoring the critical need for early detection methods and more effective therapeutic approaches. This r...

Clinical evaluation of accelerated diffusion-weighted imaging of rectal cancer using a denoising neural network.

European journal of radiology
BACKGROUND: To evaluate the effectiveness of a deep learning denoising approach to accelerate diffusion-weighted imaging (DWI) and thus improve diagnostic accuracy and image quality in restaging rectal MRI following total neoadjuvant therapy (TNT).

Detection of carotid plaques on panoramic radiographs using deep learning.

Journal of dentistry
OBJECTIVES: Panoramic radiographs (PRs) can reveal an incidental finding of atherosclerosis, or carotid artery calcification (CAC), in 3-15% of examined patients. However, limited training in identification of such calcifications among dental profess...

Enabling machine learning models in alarm fatigue research: Creation of a large relevance-annotated oxygen saturation alarm data set.

Computers in biology and medicine
BACKGROUND: Too many unnecessary alarms in the intensive care unit are one of the main reasons for alarm fatigue: Medical staff is overburdened and fails to respond appropriately. This endangers both patients and staff. Currently, there are no algori...

Artificial intelligence in medical imaging education: Recommendations for undergraduate curriculum development.

Radiography (London, England : 1995)
OBJECTIVES: Artificial intelligence (AI) is rapidly being integrated into medical imaging practice, prompting calls to enhance AI education in undergraduate radiography programs. Combining evidence from literature, practitioner insights, and industry...