AIMC Topic: Humans

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Application value of an artificial intelligence-based diagnosis and recognition system in gastroscopy training for graduate students in gastroenterology: a preliminary study.

Wiener medizinische Wochenschrift (1946)
OBJECTIVE: This study aimed to discuss the application value of an artificial intelligence-based diagnosis and recognition system (AIDRS) in the teaching activities for Bachelor of Medicine and Bachelor of Surgery (MBBS) in China. The learning perfor...

VenomPred 2.0: A Novel Platform for an Extended and Human Interpretable Toxicological Profiling of Small Molecules.

Journal of chemical information and modeling
The application of artificial intelligence and machine learning (ML) methods is becoming increasingly popular in computational toxicology and drug design; it is considered as a promising solution for assessing the safety profile of compounds, particu...

A deep learning approach to personality assessment: Generalizing across items and expanding the reach of survey-based research.

Journal of personality and social psychology
Traditional methods of personality assessment, and survey-based research in general, cannot make inferences about new items that have not been surveyed previously. This limits the amount of information that can be obtained from a given survey. In thi...

Deep Learning to Optimize Magnetic Resonance Imaging Prediction of Motor Outcomes After Hypoxic-Ischemic Encephalopathy.

Pediatric neurology
BACKGROUND: Magnetic resonance imaging (MRI) is the gold standard for outcome prediction after hypoxic-ischemic encephalopathy (HIE). Published scoring systems contain duplicative or conflicting elements.

Effectiveness of an artificial intelligence-based training and monitoring system in prevention of nosocomial infections: A pilot study of hospital-based data.

Drug discoveries & therapeutics
This work describes a novel artificial intelligence-based training and monitoring system (AITMS) that was used to control and prevent nosocomial infections (NIs) by improving the skills of donning/removing personal protective equipment (PPE). The AIT...

Perioperative, renal function and oncological outcomes of robot-assisted radical nephroureterectomy for patients with upper tract urothelial carcinoma.

World journal of urology
PURPOSE: To report perioperative, renal function and oncological outcomes of robot-assisted radical nephroureterectomy (RNU) for patients with upper tract urothelial carcinoma (UTUC).

A deep learning approach based on multi-omics data integration to construct a risk stratification prediction model for skin cutaneous melanoma.

Journal of cancer research and clinical oncology
PURPOSE: Skin cutaneous melanoma (SKCM) is a highly aggressive melanocytic carcinoma whose high heterogeneity and complex etiology make its prognosis difficult to predict. This study aimed to construct a risk subtype typing model for SKCM.

Automated tabulation of clinical trial results: A joint entity and relation extraction approach with transformer-based language representations.

Artificial intelligence in medicine
Evidence-based medicine, the practice in which healthcare professionals refer to the best available evidence when making decisions, forms the foundation of modern healthcare. However, it relies on labour-intensive systematic reviews, where domain spe...

Ethical considerations for the use of artificial intelligence in medical decision-making capacity assessments.

Psychiatry research
The rapid advancement of artificial intelligence (AI) and machine learning are providing new tools to clinicians. AI tools have the potential to process vast amounts of data in a short amount of time, providing new insights and changing how we approa...

Treatment response to spironolactone in patients with heart failure with preserved ejection fraction: a machine learning-based analysis of two randomized controlled trials.

EBioMedicine
BACKGROUND: Whether there is a subset of patients with heart failure with preserved ejection fraction (HFpEF) that benefit from spironolactone therapy is unclear. We applied a machine learning approach to identify responders and non-responders to spi...