oncology


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?


RE•WORK Women in AI in Healthcare Dinner

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Scientists have developed a new test that can pick out women at high risk of relapsing from breast cancer within 10 years of diagnosis. Their study looked for immune cell'hotspots' in and around tumours, and found that women who had a high number of hotspots were more likely to relapse than those with lower numbers. The new test could help more accurately assess the risk of cancer returning.


How Bayesian Networks Are Superior in Understanding Effects of Variables

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Bayes Nets (or Bayesian Networks) give remarkable results in determining the effects of many variables on an outcome. They typically perform strongly even in cases when other methods falter or fail. These networks have had relatively little use with business-related problems, although they have worked successfully for years in fields such as scientific research, public safety, aircraft guidance systems and national defense. Importantly, they often outperform regression, particularly in determining variables' effects. Regression is one of the most august multivariate methods, and among the most studied and applied.


Study AI: 'I believe we could see the end of cancer in our lifetime'

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Examining images and data is time-consuming and relies on the judgement and skills of highly specialised experts. Here, artificial intelligence (AI) – or deep learning – can save vast amounts of time and give much more accurate results. We're using deep learning to try and improve cancer diagnosis, as well as accelerate the search for new drugs against cancer. Using AI, a system can look at a tumour biopsy and diagnose what type it is. Algorithms generally give a more accurate diagnosis, as they are unbiased and can pick up on subtle features that are often really difficult to spot with the human eye.


Google strikes deal to offer AI medical scanning to detect cancer

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Zebra Medical Vision, an artificial intelligence (AI) health start-up, has partnered with Google to offer its algorithms on the search giant's cloud. The Israeli firm has created AI algorithms to read medical scans and detect anything untoward before humans can. Currently, its software can spot issues such as liver and lung disease and it is working on capabilities to recognise breast cancer and lung cancer. Zebra recently announced that all of its algorithms will be available to use for US$1 per scan. So, each time a hospital uses the algorithm to study a medical scan it will be charged US$1.


Skin cancer detecting device picks up £30,000 Dyson prize

Daily Mail

An affordable and effective device for detecting skin cancer has picked up an award of £30,000 ($40,000) from Britain's best-known inventor. This year's James Dyson prize for engineering was given to a group of four Canadian graduates, for their sKan system. The gadget picks up on subtle changes in the skin's ability to retain heat, which can indicate the presences of cancerous tissue. The device costs £760 ($1,000), compared with the £20,000 ($26,000) for high-resolution thermal imaging cameras. An affordable and effective device for detecting skin cancer has picked up an award of £30,000 ($40,000) from Britain's best-known inventor.


SC17: AI and Machine Learning are Central to Computational Attack on Cancer

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Enlisting computational technologies in the war on cancer isn't new but it has taken on an increasingly decisive role. At SC17, Eric Stahlberg, director of the HPC Initiative at Frederick National Laboratory for Cancer Research in the Data Science and Information Technology Program, and two colleagues will lead the third Computational Approaches for Cancer workshop being held the Friday, Nov. 17, at SC17. It is hard to overstate the importance of computation in today's pursuit of precision medicine. Given the diversity and size of datasets it's also not surprising that the "new kids" on the HPC cancer fighting block – AI and deep learning/machine learning – are also becoming the big kids on the block promising to significantly accelerate efforts understand and integrate biomedical data to develop and inform new treatments. In this Q&A, Stahlberg discusses the goals of the workshop, the growing importance of AI/deep learning in biomedical research, how programs such as the Joint Design of Advanced Computing Solutions for Cancer (JDACS4C) are progressing, the need for algorithm assurance and portability, as well as ongoing needs where HPC technology has perhaps fallen short.


Microsoft wants to use AI and machine learning to discover a cure for cancer

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Earlier this year, Microsoft launched Healthcare NExT, a new initiative that aims to bring together artificial intelligence, health research, and the expertise of its industry partners in order to provide people with the means to live healthier lives and cure deadly disease. In a blog post detailing the initiative, Microsoft noted how few other industries have the problems as complex as that of health care, though the company believes it can make headway by incorporating new innovative technology. "It's a big challenge," said Peter Lee, Corporate Vice President at Microsoft Research NExT. "But we believe technology – specifically the cloud, AI and collaboration and business optimization tools – will be central to health care transformation." As reported by Digital Journal, Microsoft is expanding Healthcare NExT to cancer research in an effort to further work done to find a cure or effective treatment for the disease.


Top 5 Deep Learning and AI Stories - November 3, 2017

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Pentagon official: AI and machine learning to revolutionize the US intelligence community 2. How AI could spot lung cancer sooner – and save lives 3. AI researchers can now access optimized deep learning framework containers in the cloud 4. AI4ALL improves student access to AI resources through NVIDIA partnership 5. READ BLOG 6. HOW AI COULD SPOT LUNG CANCER SOONER – AND SAVE LIVES Lung cancer is the most common cancer worldwide. It's also one of the most deadly. More than 80 percent of people with lung cancer die within five years of being diagnosed, and half die within a year. H. Michael Park, co- founder of startup Innovation DX, is working to improve those odds. In December, his St. Louis-based medical analytics company plans to release its first product -- a GPU-accelerated AI system that detects lung cancer in its early stages from a simple chest X-ray.


Artificial intelligence helps detect ovarian cancer early and accurately

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Ovarian cancer is difficult to diagnose, particularly in its early stages, when survival rates are much higher. Because there is no consistently reliable screening test to detect ovarian cancer, most women are diagnosed with the disease when it's in an advanced stage. However, researchers at Brigham and Women's Hospital and Dana-Farber Cancer Institute have developed a non-invasive diagnostic test using artificial intelligence for the accurate detection of true cases of early-stage disease. Results of their study were published online this week in the journal eLife. By combining next generation sequencing with artificial intelligence, researchers have created a novel blood test based on serum microRNAs--small, non-coding pieces of genetic material that help control where and when genes are activated--for the early diagnosis of ovarian cancer.