In a groundbreaking study published in NeuroImage, Dr. Patrick Krauss and Dr. Achim Schilling of the Cognitive Computational Neuroscience Group at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) used artificial intelligence to gain major insights into how our brains work, potentially changing the comprehension of human thought processes and emotions.
A study published in Circulation: Cardiovascular Quality and Outcomes by Yale School of Medicine suggests that an AI tool could use ECG images to predict the likelihood of cardiac dysfunction in cancer patients.
Inventors and researchers have been developing robots for almost 70 years. To date, all the machines they have built – whether for factories or elsewhere – have had one thing in common: they are powered by motors, a technology that is already 200 years old.
ABION today announced the signing of a Memorandum of Understanding (MOU) with Deep Bio, a pioneering leader in AI-powered digital pathology.
A noninvasive method that has been developed by researchers at the Icahn School of Medicine at Mount Sinai has the potential to significantly enhance the way physicians monitor intracranial hypertension, a condition where elevated brain pressure can result in serious consequences such as hemorrhages and strokes.
A group of researchers led by Auburn University, in collaboration with the University of Basel and ETH Zurich, have made a revolutionary breakthrough in the fight against cancer.
Sapio Sciences, the science-aware™ lab informatics platform, today announced significant enhancements to Sapio ELaiN, the pioneering AI-powered lab assistant that helps scientists streamline processes and work more efficiently.
Ainnocence Inc., a leader in AI-driven biotech solutions, is proud to announce the launch of CellulaAI™, an innovative AI engine designed to transform the landscape of CAR-T therapy.
A professor at Boston University's Chobanian & Avedisian School of Medicine is exploring how AI can revolutionize manuscript writing, peer review, and editorial decision-making.
This study combines microRNA sequencing and machine learning to develop a non-invasive diagnostic model for early pancreatic cancer detection. The approach achieved high sensitivity and specificity, offering a promising tool for improving patient outcomes through earlier diagnosis.
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