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AI and the future of healthcare

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Discussed much more thoroughly in the last article AI in Banking, Artificial Intelligence (AI) is a powerful force for business. Does it have a place in Healthcare, too? In this country, healthcare is a business, even if it is full of altruistic individuals that are just seeking to help others. We thwart disease; we repair damage; we cope with aberrations in bell-curve physiology; and most importantly, we make lives better. But that doesn't work very well without a solid business foundation!


Can Artificial Intelligence Really Identify Suicidal Thoughts? Experts Aren't Convinced

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Australian experts have spoken out about a recent US study that claimed to show artificial intelligence can identify people with suicidal thoughts - by analysing their brain scans. It sounds promising - but it's worth pointing out only 79 people were studied, so are the results enough to show this is a path worth pursing? The research, published in Nature, studied brain activity in subjects when presented with a number of different words - like death, cruelty, trouble, carefree, good and praise. A machine-learning algorithm was then trained to see the nureal response differences between the two groups involved - those with suicidal thoughts, and those with non-suicidal thoughts. And it showed promise - the algorithm correctly identified 15 of 17 patients as belonging to the suicide group, and 16 of 17 healthy individuals as belonging to the control group.


Can Artificial Intelligence Really Identify Suicidal Thoughts? Experts Aren't Convinced

#artificialintelligence

Australian experts have spoken out about a recent US study that claimed to show artificial intelligence can identify people with suicidal thoughts - by analysing their brain scans. It sounds promising - but it's worth pointing out only 79 people were studied, so are the results enough to show this is a path worth pursing? The research, published in Nature, studied brain activity in subjects when presented with a number of different words - like death, cruelty, trouble, carefree, good and praise. A machine-learning algorithm was then trained to see the nureal response differences between the two groups involved - those with suicidal thoughts, and those with non-suicidal thoughts. And it showed promise - the algorithm correctly identified 15 of 17 patients as belonging to the suicide group, and 16 of 17 healthy individuals as belonging to the control group.


This Algorithm Can Detect Pneumonia More Accurately Than a Radiologist

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An algorithm developed by researchers at Stanford University proved more effective than human radiologists in diagnosing cases of pneumonia. Much research has been shared on the potential of Artificial Intelligence applied to medicine, and in some cases, can reach a level of accuracy that exceeds the performance of professionals. Following this line, Stanford researchers published a document on CheXNet, the convolutional neuronal network, which they developed with the ability to detect pneumonia symptoms. To do this, he uses the traditional method, chest radiographs. It works with 112,120 images of chest X-rays referring to 14 types of diseases.


How Healthcare Providers Can Future-Proof Technology Investments

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Now that artificial intelligence is living up to the potential that future-minded commentators have touted for a long time, many healthcare providers are considering how to factor AI and big data projects into their processes to improve care and increase efficiency. However, investing in one platform or one focus area can be risky because of the pace of change. Putting millions or billions into one platform or project, which could be obsolete or fall flat in a few years is a huge risk. Nooman Haque, Managing Director for Healthcare and Life Sciences at Silicon Valley Bank believes the industry needs "runaway successes" to drive wider global adoption. The key issue for me is around workflow.


How will artificial intelligence change healthcare?

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When Amazon first came out with a smart recommendation algorithm for customers, millions of consumers receive their first tailored shopping experience personalized to their own interests. This changed the consumer world and introduced us to a whole new era of shopping. Amazon's algorithms, using a method called "item-to-item collaborative filtering", are able to provide targeted shopping recommendations by creating a personalized experience for each person. Even in a very basic form, this was the beginning of using machine learning in a very practical manner. But can such artificial intelligence and machine learning also act as an enabler for changes in medicine and healthcare, as much as Amazon's algorithm changed consumerism?


Improving clinical trials with machine learning

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Machine learning could improve our ability to determine whether a new drug works in the brain, potentially enabling researchers to detect drug effects that would be missed entirely by conventional statistical tests, finds a new UCL study published in Brain. "Current statistical models are too simple. They fail to capture complex biological variations across people, discarding them as mere noise. We suspected this could partly explain why so many drug trials work in simple animals but fail in the complex brains of humans. If so, machine learning capable of modelling the human brain in its full complexity may uncover treatment effects that would otherwise be missed," said the study's lead author, Dr Parashkev Nachev (UCL Institute of Neurology).


How Artificial Intelligence Will Transform Healthcare Industry In Next 5 Years

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We've been overusing the term Artificial Intelligence and AI with everyone we meet online and offline. Mostly inspired by its influence in multiple industries. The way industries are employing this smart technology is indeed overwhelming. In fact, the use of machine learning, artificial intelligence, and deep learning is becoming pervasive in all walks of life. This ubiquitous and generous use of AI gives us a tonne of hope and curiosity about how Artificial Intelligence is going to help us deal with our day-to-day hardships.


Questions for healthcare artificial intelligence to answer

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The pace of artificial intelligence technology adoption in healthcare varies considerably. Some medical establishments are undertaking small incremental changes; others centers have seen several years of innovation; and a proportion remain tied to the traditional healthcare model of the 1990s. This is the view of Dr. Ameet Bakhai, deputy director of research at the Royal Free London NHS Foundation Trust. Dr. Bakhai was expressing his views in advance of a major conference that is set to look at artificial intelligence in healthcare: Digital Healthcare Transformation Summit 2017, which takes place in London in December. A key theme is that although there are more advanced machines, from ultra-high-resolution imaging instruments to surgical robots, these tend to remain fully controlled by humans rather than with decisions made by artificial intelligence.


Improving clinical trials with machine learning Science

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Machine learning could improve our ability to determine whether a new drug works in the brain, potentially enabling researchers to detect drug effects that would be missed entirely by conventional statistical tests, finds a new UCL study published today in Brain. "Current statistical models are too simple. They fail to capture complex biological variations across people, discarding them as mere noise. We suspected this could partly explain why so many drug trials work in simple animals but fail in the complex brains of humans. If so, machine learning capable of modelling the human brain in its full complexity may uncover treatment effects that would otherwise be missed," said the study's lead author, Dr Parashkev Nachev (UCL Institute of Neurology).