A new study presented at the International Association for the Study of Lung Cancer 2025 World Conference on Lung Cancer (WCLC) validates the use of Sybil, a deep learning artificial intelligence model, for predicting future […]
Artificial intelligence can detect and interpret social features between people from images and videos almost as reliably as humans, according to a new study from the University of Turku in Finland published in the journal […]
Input and expertise from radiologists can help develop better and more trustworthy artificial intelligence (AI) tools, new research shows. The study used radiologists’ eye movements to help guide AI systems to focus on the most […]
Researchers from Lancaster University have found many currently available breast cancer symptom-management apps do not meet important clinical or user needs.This post was originally published on this site
A team of researchers at the Icahn School of Medicine at Mount Sinai has developed a new method to identify and reduce biases in datasets used to train machine-learning algorithms—addressing a critical issue that can […]
A research team led by Prof. Wang Huanqin at the Institute of Intelligent Machines, the Hefei Institutes of Physical Science of the Chinese Academy of Sciences, recently proposed a semi-supervised medical image segmentation method.This post […]
How physicians feel about artificial intelligence in medicine has been studied many times. But what do patients think? A team led by researchers at the Technical University of Munich (TUM) has investigated this in a […]
The first complete activity map of the brain has been unveiled by a large international collaboration of neuroscientists. The International Brain Laboratory (IBL) researchers published their findings in two papers in Nature, revealing insights into […]
In the morning, before you even open your eyes, your wearable device has already checked your vitals. By the time you brush your teeth, it has scanned your sleep patterns, flagged a slight irregularity, and […]
SeeMe, a computer vision tool tested by Stony Brook University researchers, was able to detect low-amplitude, voluntary facial movements in comatose acute brain injury patients days before clinicians could identify overt responses.This post was originally […]
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