Researchers have created a machine learning algorithm that can detect subtle signs of osteoarthritis on an MRI scan taken years before symptoms even begin.
Artificial intelligence has unimaginable potential to revolutionize medicine. We cover the latest technology breakthroughs of machine learning and deep learning algorithms that process mindboggling amounts of data, spot even the smallest detail in medical images and help medical professionals in designing treatment plans.
AI experts report that they have successfully addressed a major obstacle to increasing AI capabilities.
Digital phenotyping and machine learning have emerged as promising tools for monitoring patients with psychosis spectrum illnesses.
Researchers make the case that Artificial Intelligence tools have the potential to help researchers separate the wheat from the chaff.
Artificial intelligence is developing at an enormous speed and intelligent instruments will profoundly change surgery and medical interventions.
Scientists have used machin -learning to organize the chemical diversity found in the ever-growing databases for the popular metal-organic framework materials.
AI is playing a key role in the Covid-19 response, but it could also be exacerbating inequalities within our health systems – a critical concern that is dragging the technology’s limitations back into the spotlight.
Researchers have shown that machine learning techniques helped an individual with paralysis learn to control a computer cursor using their brain activity.
In the next-generation operating room interconnected sensors will collect data, analyse it in real-time and make it available to digital assistance functions.
This is the first known time that AI has used causation instead of correlations to support doctors with diagnosis in simulated cases.
Researchers develop new machine learning approach that shows promise in predicting Necrotizing enterocolitis; could lead to improved medical decision-making in neonatal ICUs.
Researchers have developed an algorithm that not only predicts hospital readmissions of heart failure patients, but also tells you why these occur.
Researchers have shown that federated learning is successful in the context of brain imaging, by being able to analyze MRI scans of brain tumor patients and distinguish healthy brain tissue from cancerous regions.
Research shows how so-called “critical states” can be used to optimize artificial neural networks running on brain-inspired neuromorphic hardware.
Scientists have assembled a combination of data mining, machine-learning algorithms and compression-based analytics to bring the most useful data to the fore on an office computer.