Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study


We performed an integrative analysis on 7 independent datasets across 5 institutions totaling 1,194 NSCLC patients (age median 68.3 years [range 32.5–93.3], Using external validation in computed tomography (CT) data, we identified prognostic signatures using a 3D convolutional neural network (CNN) for patients treated with radiotherapy (n 771, age median 68.0 years [range 32.5–93.3], We then employed a transfer learning approach to achieve the same for surgery patients (n 391, age median 69.1 years [range 37.2–88.0], We found that the CNN predictions were significantly associated with 2-year overall survival from the start of respective treatment for radiotherapy (area under the receiver operating characteristic curve [AUC] 0.70 [95% CI 0.63–0.78], The CNN was also able to significantly stratify patients into low and high mortality risk groups in both the radiotherapy (p 0.001) and surgery (p 0.03) datasets.

Artificial Intelligence: The evolution of Healthcare


Over the past five decades, technology has evolved at a breakneck speed. However, it is important to note that technological advancements, both big and small, do not just appear out of thin air. It takes copious amounts of brain power across multiple fields to ensure that the technology used in everyday life is constantly pushing the envelope, and in turn, making these breakthroughs become second nature. The impact of new technology has opened the floodgates for innovation, allowing once obscure ideas to manifest into reality. One of these ideas that are now coming to fruition is artificial intelligence (AI).

IBM AI researchers say 'what is the question' is the real question


There is a place somewhere between today's machine learning technology and some future "AI" that is murky and difficult and conflicted. Into that breach, IBM endeavors to insert itself as a voice of competency and experience. At the prestigious NeurIPS machine learning conference in Montreal this week, IBM executives John Smith, manager of AI technology at IBM, and Kush Varshney, a principal research scientist with IBM Research, were making the case that the company has a role in how a still very "brittle" machine learning field can be more reliable and "trustworthy," depending on what one means by that phrase. "It's about moving from narrow AI, where all of this really powerful technology has been highly accurate, but within a limited area of application, and making it something broader, something less brittle and something explainable," Smith told ZDNet. Perhaps not "Artificial General Intelligence," says Smith, but something that lies between that Holy Grail of AI and today's actual implementations of neural networks.

Five MIT students named 2019 Marshall Scholars

MIT News

Radha Mastandrea, Kathryn O'Nell, Anna Sappington, Kyle Swanson '18, and Crystal Winston -- have been awarded Marshall Scholarships to pursue graduate studies in the United Kingdom. This class represents the largest number of Marshall Scholars from the Institute in a single year, and continues MIT students' exceptional record of achievement in this elite fellowship program. Funded by the British government, the Marshall Scholarship provides outstanding young Americans with the opportunity to earn advanced degrees in any academic subject at any university in the United Kingdom. Scholars are chosen through a rigorous national competition that assesses academic merit, leadership, and ambassadorial potential. Up to 40 Marshall Scholarships are granted each year.

The AI will see you now


This article is part of the Global Policy Lab: Decoding Cancer. BERLIN -- When a woman gets a mammogram in Europe, it's standard practice virtually everywhere for two radiologists to take a look at the X-rays to check for signs of breast cancer. Soon that second opinion could come from a computer. A range of private companies around the world, from small startups to global tech behemoths, are developing software that uses artificial intelligence to analyze medical images -- a field that has been the domain of radiologists since German engineer Wilhelm Röntgen discovered X-rays in 1895. Advances in the field mean computers will be able to spot irregularities in medical images and make independent decisions on whether or not a second physician needs to take a look at the scan.

The First Frontier for Medical AI Is the Pathology Lab

IEEE Spectrum Robotics Channel

This is how a pathologist could save your life. Imagine you're coughing up blood, and a chest scan reveals a suspicious mass in your lungs. A surgeon removes a small cylindrical sample from the potential tumor, and the pathologist places very thin slices of the tissue on glass slides. After preserving and staining the tissue, the pathologist peers through a microscope and sees that the cells have the telltale signs of lung cancer. You start treatment before the tumor spreads and grows. And this is how a pathologist could kill you: The expert physician would just have to miss the cancer.

Amazon's newest machine learning product makes sense of unstructured medical text


Called Amazon Comprehend Medical, the HIPAA-eligible service is able to pull out medically-relevant information such as patient diagnoses, symptoms, medical test details, treatments and dosages, while simultaneously highlighting any protected health information. Amazon also stressed in the announcement that the service does not require an organization to manage any servers, and does not require any data to be stored or saved for model training. The Comprehend Medical service has already been cutting its teeth with some real-world application -- according to Amazon, both the Fred Hutchinson Cancer Research Center and Roche Diagnostics have been previewing the service to, respectively, identify patients applicable to specific cancer therapies and inform decision support portfolios. "With petabytes of unstructured data being generated in hospital systems every day, our goal is to take this information and convert it into useful insights that can be efficiently accessed and understood," Anish Kejariwal, director of software engineering for Roche Diagnostics Information Solutions, said in an Amazon blog post. "Amazon Comprehend Medical provides the functionality to help us with quickly extracting and structuring information from medical documents, so that we can build a comprehensive, longitudinal view of patients, and enable both decision support and population analytics."

JOHN NAISH: China's Frankenstein babies and new genetic experiment

Daily Mail

This is the Frankenstein breakthrough that the medical world has long been dreading. A Chinese scientist yesterday declared that he has changed the fundamental genetic code of human babies, using methods that are banned in most of the world. The potential consequences are as alarming as they are unpredictable. No less an authority than Professor Stephen Hawking feared such experiments would one day create a race of'super-humans', ending mankind as we know it. Researchers have already discovered that gene editing may cause a host of cancers as a result of interfering in a genetic code so complex we will perhaps never be capable of understanding it fully.

From Gene Editing to A.I., How Will Technology Transform Humanity?


That could be the setup for a very bad joke -- or a tremendously fascinating conversation. Fortunately for us, it was the latter. On a blustery evening in late September, in a private room at a bar near Times Square, the magazine gathered five brilliant scientists and thinkers around a table for a three-hour dinner. In the (edited) transcript below -- moderated by Mark Jannot, a story editor at the magazine and a former editor in chief of Popular Science -- you can see what they had to say about the future of medicine, health care and humanity. MARK JANNOT: For years, many pregnant women have undergone amniocentesis to test for rare metabolic disorders and other fetal issues. And couples who use in vitro fertilization can screen the embryos for genetic abnormalities. What sorts of advances in genetic screening and manipulation are coming, and where do you see that taking us? CATHERINE MOHR: When I was pregnant with my daughter, my husband and I were joking, "Well, if she gets the best of both of us, she'll be a superhero, and if she gets the worst of both of us, she's not going to make it out of first grade." And so we were rolling the genetic dice, which you do when you choose to have a child. It's not totally random, of course; there's all kinds of great things about your mate -- that's why you chose them -- and hopefully there's some pretty good things about you, too. But the temptation to engineer what you think of as the best combination, as we become more capable of doing it, I think it's going to be irresistible for a lot of people. You're investing so much of your life into this little being, and you're going to love this child, and you want to give them every advantage in life. We are already screening for diseases to avoid passing on our "bad" genes, but this same technology will let us start screening for our "best" genes -- the ones we really want to pass on.

Top 10 Ways Artificial Intelligence is Impacting Healthcare


As the ability of artificial intelligence grows it is increasingly having an effect on many areas of our everyday lives. One area where is could have the biggest impact is artificial intelligence in healthcare. Smart technologies, machine learning programs and robotic devices are all contributing to the positive impact that artificial intelligence is having in the healthcare world. As technologies and our understanding of the possibilities provided by these technologies develops the impact of artificial intelligence in healthcare can only grow. What follows are 10 of the most important ways in which artificial intelligence is impacting positively on healthcare both now and in the future. For many years it has been possibly to obtain images of the insides of the human body through non-invasive means such as X-rays, CT scans and MRI scans. However many forms of diagnosis still require invasive action such as taking tissue samples or biopsies.