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Découvrez le portrait de Alan Turing, informaticien et cryptologue révolutionnaire. Un des plus grands génie du 20ème siècle. Via Thierry Pires 
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Algorithms based on machine learning and deep learning, intended for use in diagnostic imaging, are moving into the commercial pipeline.
However, providers will have to overcome multiple challenges to incorporate these tools into daily clinical workflows in radiology.
There now are numerous algorithms in various stages of development and in the FDA approval process, and experts believe that there could eventually be hundreds or even thousands of AI-based apps to improve the quality and efficiency of radiology.
The emerging applications based on machine learning and deep learning primarily involve algorithms to automate such processes in radiology as detecting abnormal structures in images, such as cancerous lesions and nodules. The technology can be used on a variety of modalities, such as CT scans and X-rays. The goal is to help radiologists more effectively detect and track the progression of diseases, giving them tools to enhance speed and accuracy, thus improving quality and reducing costs.
While the number of organizations incorporating these products into daily workflows is small today, experts expect many providers to adopt these solutions as the industry overcomes implementation challenges.
Data dump
Radiologists also are grappling with the growth in data from sources outside radiology, such as lab tests or electronic medical records. This is another area where AI could help radiologists by analyzing data from disparate sources and pulling out key pieces of information for each case,.
There are other issues that AI could address as well, such as “observer fatigue,” which is an “aspect of radiology practice and a particular issue in screening examinations where the likelihood of finding a true positive is low,” wrote researchers from Massachusetts General Hospital and Harvard Medical School in a 2018 article in the Journal of the American College of Radiology.
These researchers foresee the utility of an AI program that could identify cases from routine screening exams with a likely positive result and prioritize those cases for radiologists’ attention. AI software also could help radiologists improve worklists of cases in which referring physicians already suspect that a medical problem exists.
read more at the original source: https://www.healthdatamanagement.com/news/algorithms-begin-to-show-practical-use-in-diagnostic-imaging Via nrip
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Ehlers Web Design's curator insight,
September 22, 2017 4:46 AM
Chatbots: A load of hype or fancy lifehack for the lazy IT person? 
Rebecca Boschma's curator insight,
October 19, 2017 7:42 AM
I’m sure it can increase productivity numbers, but I’m old school and would prefer to talk to a person. Even via email, it’s about the relationship I build with a brand that bots kill for me.
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Machine learning could train software to spot verbal tics associated with schizophrenia, depression, and bipolar disorder. Via Julie O'Donnell
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Two scientific leaps, in machine learning algorithms and powerful biological imaging and sequencing tools , are increasingly being combined to spur progress in understanding diseases and advance AI itself.
Cutting-edge, machine-learning techniques are increasingly being adapted and applied to biological data, including for COVID-19.
Recently, researchers reported using a new technique to figure out how genes are expressed in individual cells and how those cells interact in people who had died with Alzheimer's disease.
Machine-learning algorithms can also be used to compare the expression of genes in cells infected with SARS-CoV-2 to cells treated with thousands of different drugs in order to try to computationally predict drugs that might inhibit the virus.
While, Algorithmic results alone don't prove the drugs are potent enough to be clinically effective. But they can help identify future targets for antivirals or they could reveal a protein researchers didn't know was important for SARS-CoV-2, providing new insight on the biology of the virus
read the original article which speaks about a lot more at https://www.axios.com/ai-machine-learning-biology-drug-development-b51d18f1-7487-400e-8e33-e6b72bd5cfad.html
Via nrip
nrip's curator insight,
April 15, 2021 10:26 AM
The insight in this article is shared among a number of early adopters and tinkerers in the Healthcare ML space. A number of specific problems which are being worked on within the Machine learning space which relate to life sciences are stimulants which help us advance the science of machine learning much faster than other areas.
This is because the science of Biology requires more than patterns being found and re-applied to identify something. It requires understanding the interaction of all the contributing factors behind that pattern being created in the first place. So, creating a drug to target a protein involved in a disease does require understanding how the genes that give rise to that protein are regulated.
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Are consumers and clinicians prepared for innovation?
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Nombre d'auteurs parlent indifféremment d'Intelligence Artificielle (IA), de machine learning et de Data Science. Qu'en est-il exactement ? Via Mick LEVY
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Here are five ways that companies are using machine learning to transform health care. Via Julie O'Donnell, eMedToday
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