AIMC Topic: Artificial Intelligence

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[Retinal Imaging as Potential Biomarkers for Dementia].

Brain and nerve = Shinkei kenkyu no shinpo
Alzheimer's disease (AD) is a leading cause of dementia, and the current diagnostic methods of AD, such as positron emission tomography imaging, have a high cost and poor accessibility. Amyloidβ accumulates in the brain long before the symptomatic on...

Automatic Screening and Identifying Myopic Maculopathy on Optical Coherence Tomography Images Using Deep Learning.

Translational vision science & technology
PURPOSE: The purpose of this study was to engineer deep learning (DL) models that can identify myopic maculopathy in patients with high myopia based on optical coherence tomography (OCT) images.

The false hope of current approaches to explainable artificial intelligence in health care.

The Lancet. Digital health
The black-box nature of current artificial intelligence (AI) has caused some to question whether AI must be explainable to be used in high-stakes scenarios such as medicine. It has been argued that explainable AI will engender trust with the health-c...

Clinical Artificial Intelligence Applications in Radiology: Chest and Abdomen.

Radiologic clinics of North America
Organ segmentation, chest radiograph classification, and lung and liver nodule detections are some of the popular artificial intelligence (AI) tasks in chest and abdominal radiology due to the wide availability of public datasets. AI algorithms have ...

Optimization of Radiology Workflow with Artificial Intelligence.

Radiologic clinics of North America
The potential of artificial intelligence (AI) in radiology goes far beyond image analysis. AI can be used to optimize all steps of the radiology workflow by supporting a variety of nondiagnostic tasks, including order entry support, patient schedulin...

Basic Artificial Intelligence Techniques: Machine Learning and Deep Learning.

Radiologic clinics of North America
Machine learning is an important tool for extracting information from medical images. Deep learning has made this more efficient by not requiring an explicit feature extraction step and in some cases detecting features that humans had not identified....