AIMC Topic: Humans

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Towards a pragmatist dealing with algorithmic bias in medical machine learning.

Medicine, health care, and philosophy
Machine Learning (ML) is on the rise in medicine, promising improved diagnostic, therapeutic and prognostic clinical tools. While these technological innovations are bound to transform health care, they also bring new ethical concerns to the forefron...

Tumor attention networks: Better feature selection, better tumor segmentation.

Neural networks : the official journal of the International Neural Network Society
Compared with the traditional analysis of computed tomography scans, automatic liver tumor segmentation can supply precise tumor volumes and reduce the inter-observer variability in estimating the tumor size and the tumor burden, which could further ...

Handcrafted MRI radiomics and machine learning: Classification of indeterminate solid adrenal lesions.

Magnetic resonance imaging
PURPOSE: To assess a radiomic machine learning (ML) model in classifying solid adrenal lesions (ALs) without fat signal drop on chemical shift (CS) as benign or malignant.

Current review and next steps for artificial intelligence in multiple sclerosis risk research.

Computers in biology and medicine
In the last few decades, the prevalence of multiple sclerosis (MS), a chronic inflammatory disease of the nervous system, has increased, particularly in Northern European countries, the United States, and United Kingdom. The promise of artificial int...

Development status of telesurgery robotic system.

Chinese journal of traumatology = Zhonghua chuang shang za zhi
As an emerging field, telesurgery robotic system is changing the traditional medical mode and can delivery remote surgical treatment anywhere in the world. Advances in telesurgery robotic technology achieve the remote control beyond the current limit...

Expanding TNM for lung cancer through machine learning.

Thoracic cancer
BACKGROUND: Expanding the tumor, lymph node, metastasis (TNM) staging system by accommodating new prognostic and predictive factors for cancer will improve patient stratification and survival prediction. Here, we introduce machine learning for incorp...