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Why Did AI Fall Short In Slowing The Spread Of COVID-19?

#artificialintelligence

The healthcare industry hoped that AI would play a crucial tool in curbing the spread of the COVID-19 virus across the world. The results up till now are a letdown. Dr Isaac Kohane (Department of Biomedical Informatics at Harvard Medical School) states that in a few cases, they were anti-constructive. He even states that they were shooting for the moon in healthcare, but they weren't even out of their own backyard. He felt that weren't getting anywhere due to the lack of high-grade data. Yet faith isn't lost on the AI contribution to address the pandemic.


What do medical students actually need to know about artificial intelligence?

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With emerging innovations in artificial intelligence (AI) poised to substantially impact medical practice, interest in training current and future physicians about the technology is growing. Alongside comes the question of what, precisely, should medical students be taught. While competencies for the clinical usage of AI are broadly similar to those for any other novel technology, there are qualitative differences of critical importance to concerns regarding explainability, health equity, and data security. Drawing on experiences at the University of Toronto Faculty of Medicine and MIT Critical Data’s “datathons”, the authors advocate for a dual-focused approach: combining robust data science-focused additions to baseline health research curricula and extracurricular programs to cultivate leadership in this space.


Machine learning will help to grow artificial organs – IAM Network

#artificialintelligence

Akbar Solo Researchers in Moscow and America have discovered how to use machine learning to grow artificial organs, especially to tackle blindness Researchers from the Moscow Institute of Physics and Technology, Ivannikov Institute for System Programming, and the Harvard Medical School-affiliated Schepens Eye Research Institute have developed a neural network capable of recognizing retinal tissues during the process of their differentiation in a dish. Unlike humans, the algorithm achieves this without the need to modify cells, making the method suitable for growing retinal tissue for developing cell replacement therapies to treat blindness and conducting research into new drugs. The study was published in Frontiers in Cellular Neuroscience. How would this enable easier organ growth? This would allow to expand the applications of the technology for multiple fields including the drug discovery and development of cell replacement therapies to treat blindnessIn multicellular organisms, the cells making up different organs and tissues are not the same.


Machine learning helps grow artificial organs – Tech Check News

#artificialintelligence

Credit: CC0 Public Domain Researchers from the Moscow Institute of Physics and Technology, Ivannikov Institute for System Programming, and the Harvard Medical School-affiliated Schepens Eye Research Institute have developed a neural network capable of recognizing retinal tissues during the process of their differentiation in a dish. Unlike humans, the algorithm achieves this without the need to modify cells, making the method suitable for growing retinal tissue for developing cell replacement therapies to treat blindness and conducting research into new drugs.


Machine learning helps grow artificial organs

#artificialintelligence

IMAGE: Researchers from the Moscow Institute of Physics and Technology, Ivannikov Institute for System Programming, and the Harvard Medical School-affiliated Schepens Eye Research Institute have developed a neural network capable of... view more Researchers from the Moscow Institute of Physics and Technology, Ivannikov Institute for System Programming, and the Harvard Medical School-affiliated Schepens Eye Research Institute have developed a neural network capable of recognizing retinal tissues during the process of their differentiation in a dish. Unlike humans, the algorithm achieves this without the need to modify cells, making the method suitable for growing retinal tissue for developing cell replacement therapies to treat blindness and conducting research into new drugs. In multicellular organisms, the cells making up different organs and tissues are not the same. They have distinct functions and properties, acquired in the course of development. They start out the same, as so-called stem cells, which have the potential to become any kind of cell the mature organism incorporates.


A deep reinforcement learning framework to identify key players in complex networks

#artificialintelligence

Network science is an academic field that aims to unveil the structure and dynamics behind networks, such as telecommunication, computer, biological and social networks. One of the fundamental problems that network scientists have been trying to solve in recent years entails identifying an optimal set of nodes that most influence a network's functionality, referred to as key players. Identifying key players could greatly benefit many real-world applications, for instance, enhancing techniques for the immunization of networks, as well as aiding epidemic control, drug design and viral marketing. Due to its NP-hard nature, however, solving this problem using exact algorithms with polynomial time complexity has proved highly challenging. Researchers at National University of Defense Technology in China, University of California, Los Angeles (UCLA), and Harvard Medical School (HMS) have recently developed a deep reinforcement learning (DRL) framework, dubbed FINDER, that could identify key players in complex networks more efficiently.


Coronavirus Researchers Are Dismantling Science's Ivory Tower--One Study at a Time

WIRED

As the pandemic wears on, I've begun to forget what the inside of my office looks like. The last time I saw it was the second week of March, when my colleagues and I were told to work from home. Most of us had an easy enough time making the transition: At the Computational Health Informatics Program, an initiative jointly run by Boston Children's Hospital and Harvard Medical School, we spend much of our time in front of screens anyway. We had been studying Covid-19 since late January, modeling its spread in hopes of understanding how it might evolve in the weeks and months ahead. I switched off my office mood lamp and fairy lights, grabbed my laptop, and quickly familiarized myself with the VPNs I would need to gain remote access to our institutional computing services.


China and scientists dismiss study suggesting coronavirus spread in August 2019

The Japan Times

LONDON – Beijing dismissed as "ridiculous" a Harvard Medical School study of hospital traffic and search engine data that suggested the novel coronavirus may already have been spreading in China last August, and scientists said it offered no convincing evidence of when the outbreak began. The research, which has not been peer-reviewed by other scientists, used satellite imagery of hospital parking lots in Wuhan -- where the disease was first identified in late 2019 -- and data for symptom-related queries on search engines for terms such as "cough" and "diarrhea." The study's authors said increased hospital traffic and symptom search data in Wuhan preceded the documented start of the coronavirus pandemic, in December 2019. "While we cannot confirm if the increased volume was directly related to the new virus, our evidence supports other recent work showing that emergence happened before identification at the Huanan Seafood market (in Wuhan)," they said. Paul Digard, an expert in virology at the University of Edinburgh, said that using search engine data and satellite imagery of hospital traffic to detect disease outbreaks "is an interesting idea with some validity."


China pushes back against Harvard coronavirus study

Al Jazeera

Beijing has dismissed as "ridiculous" a Harvard Medical School study of hospital traffic and search engine data that suggested the new coronavirus may already have been spreading in China last August, and scientists said it offered no convincing evidence of when the outbreak began. Chinese Foreign Ministry spokeswoman Hua Chunying, asked about the research at a news briefing on Tuesday, said: "I think it is ridiculous, incredibly ridiculous, to come up with this conclusion based on superficial observations such as traffic volume." The research, which has not been peer-reviewed by other scientists, used satellite imagery of hospital parking lots in Wuhan - where the disease was first identified in late 2019 - and data for symptom-related queries on search engines for things such as "cough" and "diarrhoea". The study's authors said increased hospital traffic and symptom search data in Wuhan preceded the documented start of the coronavirus pandemic in December 2019. "While we cannot confirm if the increased volume was directly related to the new virus, our evidence supports other recent work showing that emergence happened before identification at the Huanan Seafood market (in Wuhan)," they said.


Dozens of Zuckerberg-funded scientists attack Facebook over its stance on Trump posts

The Independent - Tech

Dozens of scientists funded by Mark Zuckerberg have protested against his decision to leave inflammatory Donald Trump posts on the site. Mr Zuckerberg is allowing the president to use the social network to "spread both misinformation and incendiary statements", the researchers warn. Scientists, including 60 professors at leading US research institutions, wrote to the Facebook boss asking Mr Zuckerberg to "consider stricter policies on misinformation and incendiary language that harms people," especially during the current turmoil over racial injustice. The letter calls the spread of "deliberate misinformation and divisive language" contrary to the researchers' goals of using technology to prevent and eradicate disease, improve childhood education and reform the criminal justice system. Their mission "is antithetical to some of the stances that Facebook has been taking, so we're encouraging them to be more on the side of truth and on the right side of history as we've said in the letter," said Debora Marks of Harvard Medical School, one of three professors who organised it.