Artificial Intelligence Medical Compendium

Explore the latest research on artificial intelligence and machine learning in medicine.

Showing 70,131 to 70,140 of 234,131 articles

Constructing a deep learning-assisted smartphone application for intelligent recognition of steak doneness during cooking.

Meat science
The recognition of steak doneness during cooking poses challenges for consumers. In this study, the framework of deep learning-driven non-destructive steak doneness assessment during cooking was constructed. In addition, physicochemical properties we... read more 

DiffMLP: A diffusion-based multi-hop link prediction framework in knowledge graphs.

Neural networks : the official journal of the International Neural Network Society
Multi-hop link prediction in knowledge graphs remains a challenging task, particularly in modeling complex reasoning paths and handling uncertainty. Existing approaches often fail to capture the interactive dependencies between the reasoning context ... read more 

Diffusion for Diffusion: A versatile multiphysics fields refinement framework in pollutants transportation.

Water research
Refining coupled multiphysics fields in pollutant transport from sparse measurements is critical for environmental risk assessment and industrial pollution mitigation. However, existing numerical solvers and machine learning architectures exhibit inh... read more 

Evaluating the performance of Large language models in rheumatology for connective tissue Diseases: DeepSeek-R1, ChatGPT-4.0, Copilot, and Gemini-2.0.

International journal of medical informatics
BACKGROUND: Large language models (LLMs) demonstrate significant potential in medical information provision and may serve as valuable tools for patients seeking health information. Existing research primarily focuses on individual models or general m... read more 

Robot-assisted gait training for lower limb motor recovery in cerebral palsy: A meta-analysis of combined and standalone approaches.

Gait & posture
BACKGROUND: Cerebral palsy (CP), a leading cause of childhood motor disability, severely impacts lower limb function. Conventional gait training (CGT) faces challenges in standardization, while robot-assisted gait training (RAGT) offers biomechanical... read more 

Graph causal representation learning for out-of-distribution generalization.

Neural networks : the official journal of the International Neural Network Society
In recent years, graph neural networks (GNNs) have made remarkable achievements in graph representation learning, mainly by exploring the correlation between input graphs and labels. However, some studies have shown that GNN tends to predict the labe... read more 

Manipulating starch crystalline structure for controlled digestibility and glycemic response.

Carbohydrate polymers
Postprandial hyperglycemia is a key driver in the development of type 2 diabetes, and dietary starch is a major modulator of glycemic response. The crystalline structure of starch is a principal determinant of its digestibility and postprandial glyce... read more 

Cross-subject emotion recognition with loop adaptive adversarial transfer network.

Neural networks : the official journal of the International Neural Network Society
Emotion recognition has broad application prospects in real life. The variability across subjects in emotion-related electroencephalogram (EEG) signals is still a significant challenge for the practical use of EEG-based emotion recognition despite re... read more 

Artificial intelligence-assisted biliary stent length selection for common bile duct strictures in endoscopic retrograde cholangiopancreatography: Model development and validation.

Hepatobiliary & pancreatic diseases international : HBPD INT
BACKGROUND: Biliary stent placement during endoscopic retrograde cholangiopancreatography (ERCP) is important for drainage in common bile duct (CBD) strictures, while the stent length is associated with many stent-related complications. We aimed to d... read more 

Fuzzy symbolic convergent cross mapping: A causal coupling measure for EEG signals in disorders of consciousness patients.

Neural networks : the official journal of the International Neural Network Society
Accurate and timely diagnosis in disorders of consciousness (DOC) patients remains a core clinical challenge. Electroencephalography (EEG) shows strong potential for detecting physiological biomarkers of consciousness, and brain network analysis serv... read more