At the American Association of Physicists in Medicine (AAPM) 2019 meeting, new artificial intelligence (AI) software to assist with radiotherapy treatment planning systems was highlighted. The goal of the AI-based systems is to save staff time, while still allowing clinicians to do the final patient review. RaySearch demonstrated a new U.S. Food and Drug Administration (FDA)-cleared machine learning treatment planning system. The RaySearch RayStation machine learning algorithm is being used clinically by University Health Network, Princess Margaret Cancer Center, Toronto, Canada, where it was rolled out over several months in late-2019. Medical physicist Leigh Conroy, Ph.D., was involved in this rollout and helped conduct a study, showing the automated plans and traditionally made plans to radiation oncologists to get valuable feedback.

Moreover, across the healthcare and pharmaceutical industry, technology is making great advancements. Living under the reign of terror induced by a coronavirus, no other generation than us can understand the true benefits of technology in reshaping the healthcare industry. Today AI and its subsets are being used extensively for drug discovery and medical treatment planning. However, the upsurge of AI in this industry has been noted in 2019 when a Hong Kong biotech startup called InSilico Medicine partnered with the University of Toronto researchers to create a drug in order to advance the concept to initial testing. As noted by Fortune, the significance of AI on pre-clinical development and on the economics of healthcare is worth watching.

DataNavee ("DNV") was formed by an experienced team of professionals that have been involved in the Artificial Intelligence and data analytics sector …

At the American Association of Physicists in Medicine (AAPM) 2019 meeting, new artificial intelligence (AI) software to assist with radiotherapy treatment planning systems was highlighted. The goal of the AI-based systems is to save staff time, while still allowing clinicians to do the final patient review. RaySearch demonstrated a new U.S. Food and Drug Administration (FDA)-cleared machine learning treatment planning system. The RaySearch RayStation machine learning algorithm is being used clinically by University Health Network, Princess Margaret Cancer Center, Toronto, Canada, where it was rolled out over several months in late-2019. Medical physicist Leigh Conroy, Ph.D., was involved in this rollout and helped conduct a study, showing the automated plans and traditionally made plans to radiation oncologists to get valuable feedback.

I'd lost almost $200 million in October. It was 2008, after the Lehman Brothers bankruptcy. Banks were failing left and right. I worked at a major investment bank, and while I didn't think the disastrous deal I'd done would cause its collapse, my losses were quickly decimating its commodities profits for the year, along with the potential pay of my more profitable colleagues. I thought my career could be over. I'd already started to feel those other traders and salespeople keeping their distance, as if I'd contracted a disease. My eyes started to fill from a sudden wash of gratitude and relief that came, I think, from no longer being alone. I landed in London on the morning of November 4, having flown overnight from New York. I was a derivatives trader, but also the supervisor of the bank's oil options trading team, about a dozen guys split between Singapore, London, and New York.

A study led by University of Toronto alumna Kimberly Ren is among the first to quantify predictors that could lead women towards, or away from, pursuing careers in machine learning and artificial intelligence, or AI. Women currently make up 22 per cent of global AI professionals, with that proportion oscillating between 21 per cent and 23 per cent over a four-year trend, according to a 2018 report by the World Economic Forum. "The talent gap isn't closing," says Ren, who recently graduated from the Faculty of Applied Science & Engineering and was awarded the Best Paper Award at the American Society for Engineering Education Conference for her fourth-year thesis project. She led the study under the supervision of Alison Olechowski, an assistant professor in the department of mechanical and industrial engineering. "What I hope this research does is find some reasoning behind this gap, so that we can increase the persistence of women in the field going forward."

By now, it's almost old news that artificial intelligence (AI) will have a transformative role in medicine. Algorithms have the potential to work tirelessly, at faster rates and now with potentially greater accuracy than clinicians. In 2016, it was predicted that'machine learning will displace much of the work of radiologists and anatomical pathologists'. In the same year, a University of Toronto professor controversially announced that'we should stop training radiologists now'. But is it really the beginning of the end for some medical specialties?

OTTAWA, ON, June 15, 2020 /CNW/ - Kinaxis Inc. (TSX: KXS), the authority in driving agility for fast, confident decision-making in an unpredictable world, has signed a definitive agreement to acquire Toronto-based Rubikloud, a disruptive, emerging provider of AI solutions that automate supply chain prescriptive analytics and decision-making in the retail and consumer packaged goods (CPG) industries. Globally-recognized retailers and CPG manufacturers in the health and beauty, household and grocery segments use Rubikloud's AI-based products today. Their offerings include demand forecasting and automation to manage and optimize trade promotions, pricing and assortment to drive product demand and dramatically improve financial results. Kinaxis will enhance RapidResponse's demand planning capabilities with the Rubikloud offerings, anticipating initial opportunities in the company's rapidly-growing CPG customer base and over time for other industries such as life sciences. The acquisition also offers Kinaxis a springboard into the enterprise retail industry.

That year, numerous experienced computer chip designers set out on their own to design novel kinds of parts to improve the performance of artificial intelligence. It's taken a few years, but the world is finally seeing what those young hopefuls have been working on. The new chips coming out suggest, as ZDNet has reported in past, that AI is totally changing the nature of computing. It also suggests that changes in computing are going to have an effect on how artificial intelligence programs, such as deep learning neural networks, are designed. Case in point, startup Tenstorrent, founded in 2016 and headquartered in Toronto, Canada, on Thursday unveiled its first chip, "Grayskull," at a microprocessor conference run by the legendary computer chip analysis firm The Linley Group.

Naheed Kurji is the President and CEO of Cyclica, a Toronto-based biotechnology company that leverages artificial intelligence and computational biophysics to reshape the drug discovery process. Cyclica leverages artificial intelligence and computational biophysics to reshape the drug discovery process. Can you discuss in what way AI is used in this process? Technology has played a critical role in drug discovery dating back to the '80s. However, the drug discovery and development process is still very inefficient, time consuming and expensive, costing more than 2 billion dollars over 12 years.