AIMC Topic: Humans

Clear Filters Showing 32041 to 32050 of 95995 articles

Artificial intelligence assisted endoscopic ultrasound for detection of pancreatic space-occupying lesion: a systematic review and meta-analysis.

International journal of surgery (London, England)
BACKGROUND: Diagnosing pancreatic lesions, including chronic pancreatitis, autoimmune pancreatitis, and pancreatic cancer, poses a challenge and, as a result, is time-consuming. To tackle this issue, artificial intelligence (AI) has been increasingly...

The use of POTTER (Predictive Optimal Trees in Emergency Surgery Risk) calculator to predict mortality and complications in patients submitted to Emergency Surgery.

Revista do Colegio Brasileiro de Cirurgioes
INTRODUCTION: the ability of the care team to reliably predict postoperative risk is essential for improvements in surgical decision-making, patient and family counseling, and resource allocation in hospitals. The Artificial Intelligence (AI)-powered...

Population preferences for AI system features across eight different decision-making contexts.

PloS one
Artificial intelligence systems based on deep learning architectures are being investigated as decision-support systems for human decision-makers across a wide range of decision-making contexts. It is known from the literature on AI in medicine that ...

Curricula for teaching end-users to kinesthetically program collaborative robots.

PloS one
Non-expert users can now program robots using various end-user robot programming methods, which have widened the use of robots and lowered barriers preventing robot use by laypeople. Kinesthetic teaching is a common form of end-user robot programming...

Machine learning approach reveals microbiome, metabolome, and lipidome profiles in type 1 diabetes.

Journal of advanced research
INTRODUCTION: Type 1 diabetes (T1D) is a complex disorder influenced by genetic and environmental factors. The gut microbiome, the serum metabolome, and the serum lipidome have been identified as key environmental factors contributing to the pathophy...

Deep learning-based automatic segmentation of bone graft material after maxillary sinus augmentation.

Clinical oral implants research
OBJECTIVES: To investigate the accuracy and reliability of deep learning in automatic graft material segmentation after maxillary sinus augmentation (SA) from cone-beam computed tomography (CBCT) images.