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health & medicine


Deep learning helps explore the structural and strategic bases of autism?

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Psychiatrists typically diagnose autism spectrum disorders (ASD) by observing a person's behavior and by leaning on the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), widely considered the'bible' of mental health diagnosis. However, there are substantial differences amongst individuals on the spectrum and a great deal remains unknown by science about the causes of autism, or even what autism is. As a result, an accurate diagnosis of ASD and a prognosis prediction for patients can be extremely difficult. But what if artificial intelligence (AI) could help? Deep learning, a type of AI, deploys artificial neural networks based on the human brain to recognize patterns in a way that is akin to, and in some cases can surpass, human ability.


The Promise of Artificial Intelligence

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As a healthcare leaders scrutinize costs and quality, they're looking to emerging technology to help them optimize processes and improve employee productivity. Artificial intelligence and machine learning are increasingly being used to automate critical business functions and support clinicians making complex clinical decisions. As the pandemic challenges healthcare organizations to think innovatively to improve cost effectiveness, AI is likely to play an even bigger role. Peter Durlach is senior vice president, healthcare strategy & new business development, at Nuance Communications. He holds a pivotal role in advancing the portfolio of healthcare solutions to align with the shifting needs of healthcare clients.


AI can detect how lonely you are by analysing your speech

Daily Mail - Science & tech

Artificial intelligence (AI) can detect loneliness with 94 per cent accuracy from a person's speech, a new scientific paper reports. Researchers in the US used several AI tools, including IBM Watson, to analyse transcripts of older adults interviewed about feelings of loneliness. By analysing words, phrases, and gaps of silence during the interviews, the AI assessed loneliness symptoms nearly as accurately as loneliness questionnaires completed by the participants themselves, which can be biased. It revealed that lonely individuals tend to have longer responses to direct questions about loneliness, and express more sadness in their answers. 'Most studies use either a direct question of "how often do you feel lonely", which can lead to biased responses due to stigma associated with loneliness,' said senior author Ellen Lee at UC San Diego (UCSD) School of Medicine.


Machine learning takes on synthetic biology: algorithms can bioengineer cells for you

#artificialintelligence

If you've eaten vegan burgers that taste like meat or used synthetic collagen in your beauty routine--both products that are "grown" in the lab--then you've benefited from synthetic biology. It's a field rife with potential, as it allows scientists to design biological systems to specification, such as engineering a microbe to produce a cancer-fighting agent. Yet conventional methods of bioengineering are slow and laborious, with trial and error being the main approach. Now scientists at the Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) have developed a new tool that adapts machine learning algorithms to the needs of synthetic biology to guide development systematically. The innovation means scientists will not have to spend years developing a meticulous understanding of each part of a cell and what it does in order to manipulate it; instead, with a limited set of training data, the algorithms are able to predict how changes in a cell's DNA or biochemistry will affect its behavior, then make recommendations for the next engineering cycle along with probabilistic predictions for attaining the desired goal.


A short guide for medical professionals in the era of artificial intelligence

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Artificial intelligence (A.I.) is expected to significantly influence the practice of medicine and the delivery of healthcare in the near future. While there are only a handful of practical examples for its medical use with enough evidence, hype and attention around the topic are significant. There are so many papers, conference talks, misleading news headlines and study interpretations that a short and visual guide any medical professional can refer back to in their professional life might be useful. For this, it is critical that physicians understand the basics of the technology so they can see beyond the hype, evaluate A.I.-based studies and clinical validation; as well as acknowledge the limitations and opportunities of A.I. This paper aims to serve as a short, visual and digestible repository of information and details every physician might need to know in the age of A.I. We describe the simple definition of A.I., its levels, its methods, the differences between the methods with medical examples, the potential benefits, dangers, challenges of A.I., as well as attempt to provide a futuristic vision about using it in an everyday medical practice.


Breakthrough in safety-critical machine learning could be just the beginning

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Safety is the central focus on driverless vehicle systems development. Artificial intelligence (AI) is coming at us fast. It's being used in the apps and services we plug into daily without us really noticing, whether it's a personalized ad on Facebook, or Google recommending how you sign off your email. If these applications fail, it may result in some irritation to the user in the worst case. But we are increasingly entrusting AI and machine learning to safety-critical applications, where system failure results in a lot more than a slight UX issue.


Japan considers facial recognition for contact tracing at big events

The Japan Times

The government aims to put a facial recognition system into practical use to prevent new coronavirus infections at large-scale events including the Tokyo Olympics and Paralympics, it was learned Friday. The government also hopes to improve the national capacity to conduct saliva-based polymerase chain reaction tests to simultaneously detect cases of influenza and novel coronavirus infection, informed sources said. The proposals are included in a draft program for developing new technologies for preventing coronavirus infection. The government will unveil the program shortly and carry out demonstration tests at relevant ministries and agencies. According to the draft, the government is looking at using security cameras equipped with a facial recognition system to record the movements of visitors to the Tokyo Games, which were postponed to 2021, and other large-scale events, the sources said.


Spotlight Interview with Dr Thomas Sander from Idorsia Pharmaceuticals - Collaborative Drug Discovery Inc. (CDD)

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Dr. Sander kindly agreed to give us this interview at the Idorsia headquarters in Basel, Switzerland. Asking the questions from CDD are Neil Chapman and Mariana Vaschetto. By education I am organic chemist. During my seventh year at school we started to have chemistry classes and soon I had made up my mind to study chemistry. Four years later while still at school I had an opportunity to access the local University's Tectronix graphics computers.


Modernising pharma patents: can AI be an inventor?

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Patents are used to grant exclusive property rights to an inventor and prevent their discovery from being copied by others. The main requirements for a patent are that the invention must be novel, non-obvious and be useful or have an industrial application. Patents are a central part of how pharma does business. Pharma products require longer and more complex research and development (R&D) cycles than products in other industries. Consequently, companies invest significant amounts of money into their new products early on in their development.


Rebooting the post-pandemic enterprise with AI automation

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The damage from pandemic-induced lockdowns, office and school closures and consumer retrenchment continue to reverberate through the economy. As the crisis drags into its seventh month, it has left businesses facing hard choices in adjusting to what now seems like many permanent changes. Required actions to address the COVID-19 crisis can be divided into three major stages: Respond, Recover and Thrive. These three stages are interspersed with two additional interim stages, and culminate in a long-term operating environment we call the'next normal'. The early months were focused on business survival through a series of reactionary changes, which was followed by mid-term operational stabilization in a world with diminished demand, continued socio-political restrictions and unpredictable events.