Researchers from Kaunas universities in Lithuania developed a deep learning-based method that can predict the possible onset of Alzheimer's disease from brain images with an accuracy of over 99 per cent. The method was developed while analysing functional MRI images obtained from 138 subjects and performed better in terms of accuracy, sensitivity and specificity than previously developed methods. According to World Health Organisation, Alzheimer's disease is the most frequent cause of dementia, contributing to up to 70 per cent of dementia cases. Worldwide, approximately 24 million people are affected, and this number is expected to double every 20 years. Owing to societal ageing, the disease will become a costly public health burden in the years to come.

The artificial intelligence Maithra Raghu studies at Google Brain doesn't have a bedside manner. But she's betting it can still help restore a deeply human, disappearing aspect of modern medicine: personal connection. In a health care system flooded with paperwork and patient data, Raghu sees a natural place for neural networks, which analyze vast amounts of information to find patterns that escape the human eye and use them to churn out diagnoses or health care predictions. To her, the technology could prove to be a powerful tool for processing data that can spare providers more time to spend with patients one-on-one. "Machine learning isn't a magic tool here," said Raghu, a senior research scientist who was recently named a STAT Wunderkind.

The artificial intelligence Maithra Raghu studies at Google Brain doesn't have a bedside manner. But she's betting it can still help restore a deeply human, disappearing aspect of modern medicine: personal connection. In a health care system flooded with paperwork and patient data, Raghu sees a natural place for neural networks, which analyze vast amounts of information to find patterns that escape the human eye and use them to churn out diagnoses or health care predictions. To her, the technology could prove to be a powerful tool for processing data that can spare providers more time to spend with patients one-on-one. "Machine learning isn't a magic tool here," said Raghu, a senior research scientist who was recently named a STAT Wunderkind.

Researchers have invented a machine learning algorithm that forecasts the outcome of chemical reactions with much greater accuracy than proficient chemists and proposes ways to make complex particles, removing a weighty hurdle in drug innovation. University of Cambridge researchers have revealed that an algorithm can forecast the outcomes of complex chemical reactions with above 90% accuracy, overtaking trained chemists. The algorithm also displays chemists how to make target mixtures, providing the chemical "map" to the required destination. The outcomes are stated in two studies in the journals ACS Central Science and Chemical Communications. A central task in drug discovery and materials science is discovering ways to build complex organic molecules by chemically assembling together elementary building blocks. The problem arises when those building blocks often respond in surprising ways.

University of Cambridge researchers have shown that an algorithm can predict the outcomes of complex chemical reactions with over 90% accuracy, outperforming trained chemists. The algorithm also shows chemists how to make target compounds, providing the chemical'map' to the desired destination. The results are reported in two studies in the journals ACS Central Science and Chemical Communications. A central challenge in drug discovery and materials science is finding ways to make complicated organic molecules by chemically joining together simpler building blocks. The problem is that those building blocks often react in unexpected ways.

Researchers have designed a machine learning algorithm that predicts the outcome of chemical reactions with much higher accuracy than trained chemists and suggests ways to make complex molecules, removing a significant hurdle in drug discovery. University of Cambridge researchers have shown that an algorithm can predict the outcomes of complex chemical reactions with over 90% accuracy, outperforming trained chemists. The algorithm also shows chemists how to make target compounds, providing the chemical "map" to the desired destination. The results are reported in two studies in the journals ACS Central Science and Chemical Communications. A central challenge in drug discovery and materials science is finding ways to make complicated organic molecules by chemically joining together simpler building blocks.

Eric Topol sees a future in which doctors use artificial intelligence (AI) to analyze billions of pieces of medical, social, genetic, and environmental information--continuously and automatically collected--to produce diagnoses and treatments individually tailored to the patient in front of them. It's a future in which algorithms digest the peculiarities of your gut's microbiome (advising, say, strawberry Danishes rather than banana nut muffins) and in which the medication prescribed for your particular flavor of diabetes was discovered by a machine that sifted through trillions of molecules to identify the most promising chemical compounds. It's a future with AI-powered electronic medical assistants--Alexas with medical school degrees--who listen to your visit, place orders, schedule follow-up, and generate the required documentation. Topol, a cardiologist, geneticist, and director of the Scripps Research Translational Institute, is the author of three books about the digitalization of health care. His most recent, Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again, is an exhaustive tour-de-force review of the past, present, and future of AI in medicine,brought to life with compelling personal anecdotes about his life as a patient, physician, husband, and son.

Researchers have designed a machine learning algorithm that predicts the outcome of chemical reactions with much higher accuracy than trained chemists and suggests ways to make complex molecules, removing a significant hurdle in drug discovery. University of Cambridge researchers have shown that an algorithm can predict the outcomes of complex chemical reactions with over 90% accuracy, outperforming trained chemists. The algorithm also shows chemists how to make target compounds, providing the chemical "map" to the desired destination. The results are reported in two studies in the journals ACS Central Science and Chemical Communications. A central challenge in drug discovery and materials science is finding ways to make complicated organic molecules by chemically joining together simpler building blocks.

"Machine learning algorithms can have a better understanding of chemistry because they distil patterns of reactivity from millions of published …