health & medicine


Interpreting AI Is More Than Black And White

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Any sufficiently advanced technology is indistinguishable from magic. In the world of artificial intelligence & machine learning (AI & ML), black- and white-box categorization of models and algorithms refers to their interpretability. That is, given a model trained to map data inputs to outputs (e.g. And just as the software testing dichotomy is high-level behavior vs low-level logic, only white-box AI methods can be readily interpreted to see the logic behind models' predictions. In recent years with machine learning taking over new industries and applications, where the number of users far outnumber experts that grok the models and algorithms, the conversation around interpretability has become an important one.


Nuance begins selling AI system to automate physician note-taking - STAT

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The medical speech recognition company Nuance said Monday it will begin widely selling an artificial intelligence system to automate physician note-taking. The system, built in a partnership with Microsoft, uses technology wired into the walls of the exam room to record and build a narrative of each patient encounter that is uploaded into electronic health records. Physicians can use voice commands to fill in specific fields within the health record, including the patient's list of medical problems and medication orders. Unlock this article by subscribing to STAT Plus and enjoy your first 30 days free! STAT Plus is STAT's premium subscription service for in-depth biotech, pharma, policy, and life science coverage and analysis.


Artificial Intelligence in Emergency Medicine: Surmountable Barriers With Revolutionary Potential

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Artificial intelligence just might be the next major technologic breakthrough to affect health care delivery, with seemingly endless possibilities for the improvement of patient care and optimization of the health care system overall.1 Simply defined, artificial intelligence is a field of computer science focused on enabling computers to complete tasks or generate knowledge that, in the traditional sense, would typically require human intelligence. Within the topic of artificial intelligence, there are the fields of machine learning and deep learning.


Artificial intelligence yields new antibiotic: A deep-learning model identifies a powerful new drug that can kill many species of antibiotic-resistant bacteria

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The computer model, which can screen more than a hundred million chemical compounds in a matter of days, is designed to pick out potential antibiotics that kill bacteria using different mechanisms than those of existing drugs. "We wanted to develop a platform that would allow us to harness the power of artificial intelligence to usher in a new age of antibiotic drug discovery," says James Collins, the Termeer Professor of Medical Engineering and Science in MIT's Institute for Medical Engineering and Science (IMES) and Department of Biological Engineering. "Our approach revealed this amazing molecule which is arguably one of the more powerful antibiotics that has been discovered." In their new study, the researchers also identified several other promising antibiotic candidates, which they plan to test further. They believe the model could also be used to design new drugs, based on what it has learned about chemical structures that enable drugs to kill bacteria.


Google backs six artificial intelligence-based research projects – Details

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Artificial Intelligence (AI) is opening up the next phase of technological advances. Riding the AI wave, Google has started six AI-based research projects in India. These projects would focus on addressing social, humanitarian and environmental challenges in sectors such as healthcare, education, disaster prevention and conversation. Google Research India, based in Bengaluru, will provide funding and computational resources besides supporting the efforts with expertise in computer vision, natural language processing, and other deep learning techniques, says Manish Gupta, director of Google Research Team in India. The research team will focus on two pillars: First, advancing fundamental computer science and AI research by building a strong team and partnering with the research community across the country and secondly, applying this research to tackle big problems in fields such as healthcare, agriculture and education while also using it to make apps and services more helpful.


Bringing deep learning to life

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Gaby Ecanow loves listening to music, but never considered writing her own until taking 6.S191 (Introduction to Deep Learning). By her second class, the second-year MIT student had composed an original Irish folk song with the help of a recurrent neural network, and was considering how to adapt the model to create her own Louis the Child-inspired dance beats. "It was cool," she says. "It didn't sound at all like a machine had made it." This year, 6.S191 kicked off as usual, with students spilling into the aisles of Stata Center's Kirsch Auditorium during Independent Activities Period (IAP).


Affective Computing Market to Witness a Pronounce Growth During 2017 to 2025 – Market Research Sheets

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The global affective computing market is envisioned to create high growth prospects on the back of the rising deployment of machine and human interaction technologies. With enabling technologies already making a mark with their adoption in a range of industry verticals, it could be said that the market has started to evolve. Facial feature extraction software collecting a handsome demand in the recent years is expected to augur well for the growth of the deployment of cameras in affective computing systems. Detection of psychological disorders, facial expression recognition for dyslexia, autism, and other disorders in specially-abled children, and various other applications could increase the use of affective computing technology. Life sciences and healthcare are prognosticated to showcase a promising rise in the demand for affective computing.


How AI Can Live Up To Its Hype In The Healthcare Industry

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"What's the problem you're trying to solve?" Clayton Christensen, the late Harvard business professor, was famous for posing this aphoristic question to aspiring entrepreneurs. By asking it, he was teaching those in earshot an important lesson: Innovation, alone, isn't the end goal. To succeed, ideas and products must address fundamental human problems. This is especially true in healthcare, where artificial intelligence is fueling the hopes of an industry desperate for better solutions. But here's the problem: Tech companies too often set out to create AI innovations they can sell, rather than trying to understand the problems doctors and patients need solved.


AI system discovers powerful new antibiotic to tackle superbugs

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Bacteria are evolving resistance to antibiotics much faster than new drugs can be developed, potentially leading us to a dangerous future where infections are more likely to be deadly. Now, an artificial intelligence model has identified a powerful new antibiotic called halicin, which cleared infections of most superbugs in mouse tests. Ever since antibiotics were invented in the early 20th century, we've been locked in an arms race with bacteria. Antibiotics work for a while, but eventually the bugs evolve resistance to those in wide use. Scientists develop new ones, so bacteria continue to evolve, and so on.


AI identifies new antibiotic that can kill drug-resistant bacteria

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Researchers in the US have used artificial intelligence (AI) to discover a powerful new type of antibiotic capable of killing drug-resistant bacteria. Scientists at MIT trained a machine learning algorithm to analyse the molecular structures of chemical compounds and pick out potential antibiotics. The deep learning model was designed to identify compounds capable of killing bacteria using different mechanisms to those of existing drugs. After analysing some 2,500 different molecules, the AI system identified a new antibiotic compound which, in lab tests, killed many of the world's most problematic disease-causing bacteria, including drug-resistant strains. The new antibiotic compound has been dubbed halicin, named after the the rogue AI system, Hal 9000, from 1968 film 2001: A Space Odyssey.