AIMC Topic: Humans

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Deep learning for topical trend discovery in online discourse about Pre-Exposure Prophylaxis (PrEP).

AIDS and behavior
Pre-Exposure Prophylaxis (PrEP) interventions are increasingly prevalent on social media. These data can be mined for insights about PrEP that may not be as apparent in surveys including personal musings about PrEP and barriers/facilitators to PrEP u...

Implementation of Clinical Artificial Intelligence in Radiology: Who Decides and How?

Radiology
As the role of artificial intelligence (AI) in clinical practice evolves, governance structures oversee the implementation, maintenance, and monitoring of clinical AI algorithms to enhance quality, manage resources, and ensure patient safety. In this...

Deep learning-guided weighted averaging for signal dropout compensation in DWI of the liver.

Magnetic resonance in medicine
PURPOSE: To develop an algorithm for the retrospective correction of signal dropout artifacts in abdominal DWI resulting from cardiac motion.

AsthmaKGxE: An asthma-environment interaction knowledge graph leveraging public databases and scientific literature.

Computers in biology and medicine
MOTIVATION: Asthma is a complex heterogeneous disease resulting from intricate interactions between genetic and non-genetic factors related to environmental and psychosocial aspects. Discovery of such interactions can provide insights into the pathop...

Osteoporosis screening support system from panoramic radiographs using deep learning by convolutional neural network.

Dento maxillo facial radiology
OBJECTIVES: This study was performed to develop computer-aided screening systems that could predict osteoporosis. The systems were constructed using panoramic radiographs of women aged ≥ 50 years through three types of deep convolutional neural netwo...

A neural network solves, explains, and generates university math problems by program synthesis and few-shot learning at human level.

Proceedings of the National Academy of Sciences of the United States of America
We demonstrate that a neural network pretrained on text and fine-tuned on code solves mathematics course problems, explains solutions, and generates questions at a human level. We automatically synthesize programs using few-shot learning and OpenAI's...

Using Explainable Artificial Intelligence to Discover Interactions in an Ecological Model for Obesity.

International journal of environmental research and public health
Ecological theories suggest that environmental, social, and individual factors interact to cause obesity. Yet, many analytic techniques, such as multilevel modeling, require manual specification of interacting factors, making them inept in their abil...

Application of the sliding window method and Mask-RCNN method to nuclear recognition in oral cytology.

Diagnostic pathology
BACKGROUND: We aimed to develop an artificial intelligence (AI)-assisted oral cytology method, similar to cervical cytology. We focused on the detection of cell nuclei because the ratio of cell nuclei to cytoplasm increases with increasing cell malig...

Cynomolgus monkey's choroid reference database derived from hybrid deep learning optical coherence tomography segmentation.

Scientific reports
Cynomolgus monkeys exhibit human-like features, such as a fovea, so they are often used in non-clinical research. Nevertheless, little is known about the natural variation of the choroidal thickness in relation to origin and sex. A combination of dee...

Predicting the Next-Day Perceived and Physiological Stress of Pregnant Women by Using Machine Learning and Explainability: Algorithm Development and Validation.

JMIR mHealth and uHealth
BACKGROUND: Cognitive behavioral therapy-based interventions are effective in reducing prenatal stress, which can have severe adverse health effects on mothers and newborns if unaddressed. Predicting next-day physiological or perceived stress can hel...