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Enhanced non-invasive machine learning approach for early colorectal cancer detection: Predictive modeling and validation in a Jordanian cohort.

Computers in biology and medicine
BACKGROUND: Colorectal cancer (CRC) ranks as the third most prevalent cancer worldwide, posing significant public health challenges. Late-stage detection often results in poor treatment outcomes, elevating mortality rates. The economic and psychologi...

Assessing the association of multi-environmental chemical exposures on metabolic syndrome: A machine learning approach.

Environment international
Metabolic syndrome (MetS) is a major global public health concern due to its rising prevalence and association with increased risks of cardiovascular disease and type 2 diabetes. Emerging evidence suggests that environmental chemical exposures may pl...

Existential risk narratives about AI do not distract from its immediate harms.

Proceedings of the National Academy of Sciences of the United States of America
There is broad consensus that AI presents risks, but considerable disagreement about the nature of those risks. These differing viewpoints can be understood as distinct narratives, each offering a specific interpretation of AI's potential dangers. On...

Classification of Grades of Subchondral Sclerosis from Knee Radiographic Images Using Artificial Intelligence.

Sensors (Basel, Switzerland)
Osteoarthritis (OA) is the most common joint disease, affecting over 300 million people worldwide. Subchondral sclerosis is a key indicator of OA. Currently, the diagnosis of subchondral sclerosis is primarily based on radiographic images; however, r...

Deep Learning-Driven Abbreviated Shoulder MRI Protocols: Diagnostic Accuracy in Clinical Practice.

Tomography (Ann Arbor, Mich.)
BACKGROUND: Deep learning (DL) reconstruction techniques have shown promise in reducing MRI acquisition times while maintaining image quality. However, the impact of different acceleration factors on diagnostic accuracy in shoulder MRI remains unexpl...

Molecular features and diagnostic modeling of synovium- and IPFP-derived OA macrophages in the inflammatory microenvironment via scRNA-seq and machine learning.

Journal of orthopaedic surgery and research
BACKGROUND: Osteoarthritis (OA) is the leading cause of degenerative joint disease, with total joint replacement as the only definitive cure. However, no disease-modifying therapy is currently available. Inflammation and fibrosis in the infrapatellar...

Predicting depression and unravelling its heterogeneous influences in middle-aged and older people populations: a machine learning approach.

BMC psychology
BACKGROUND: Aging has become a global trend, and depression, as an accompanying issue, poses a significant threat to the health of middle-aged and older adults. Existing studies primarily rely on statistical methods such as logistic regression for sm...

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
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...

A machine learning-based framework for predicting postpartum chronic pain: a retrospective study.

BMC medical informatics and decision making
BACKGROUND: Postpartum chronic pain is prevalent, affecting many women after delivery. Machine learning algorithms have been widely used in predicting postoperative conditions. We investigated the prevalence of and risk factors for postpartum chronic...

Automated machine learning for early prediction of systemic inflammatory response syndrome in acute pancreatitis.

BMC medical informatics and decision making
BACKGROUND: Systemic inflammatory response syndrome (SIRS) is a frequent and serious complication of acute pancreatitis (AP), often associated with increased mortality. This study aims to leverage automated machine learning (AutoML) algorithms to cre...