High-tech 3D facial scans to give us a better understanding of the genetic causes of autism.
Machine learning is a type of artificial intelligence that can be described as a mathematical model where computers are trained to learn to see connections and solve problems using different data sets.
More than 20 hospitals from across the world together with NVIDIA have used AI to predict Covid patients’ oxygen needs on a global scale.
An electronic “nose” is capable of detecting with 86% accuracy when a lung transplant is beginning to fail.
To enhance human-robot collaboration, researchers at Loughborough University have trained an AI to detect human intention.
Researchers have used machine learning to help reconstruct three-dimensional micro-CT images of fibrous materials.
A new approach to tackling the spread of malaria in sub-Saharan Africa, which combines affordable, easy-to-administer blood tests with machine learning and unbreakable encryption, has generated encouraging early results in Uganda.
Machine learning can accurately predict cardiovascular disease and guide treatment — but models that incorporate social determinants of health better capture risk and outcomes for diverse groups.
A tool, based on machine learning methods, that evaluates the potential contribution of all possible mutations in a gene in a given type of tumour to the development and progression of cancer.
AI tools models are a powerful tool in cancer treatment. However, unless these algorithms are properly calibrated, they can sometimes make inaccurate or biased predictions.
Every day, elderly people fall – be it at home or in care facilities. Lindera aims to reduce the risk of falling with the help of artificial intelligence.
A new study could help scientists mitigate the future spread of zoonotic and livestock diseases caused by existing viruses.
Scientists have used an implanted sensor to record the brain signals associated with handwriting, and used those signals to create text on a computer in real time.
Researchers have shown that a group of small autonomous, self-learning robots can adapt easily to changing circumstances. They connected the simple robots in a line, after which each individual robot taught itself to move forward as quickly as possible.