The need for new medications is higher than ever, but so is the cost and time to bring them to market. Developing a new drug can cost billions and take as long as 14 years, according to the U.S. Food and Drug Administration. Yet with all that effort, only 8 percent of drugs make it to market, the FDA said. "We need to make smarter decisions about which potential medicines we develop and test," said Abraham Heifets, co-founder of San Francisco-based startup Atomwise. The six-year-old company, a member of our Inception startup incubator program, is working to make that happen by using GPU-accelerated deep learning to predict which molecules are most likely to lead to treatments.
The first FDA-approved AI system for diagnosing eye diseases caused by diabetes is completely autonomous, and doesn't require a doctor to interpret the results. Several corporations including Google and DeepMind have been working on building algorithms for diabetic retinography, a leading cause of blindness amongst adults. The first biz to release a device approved by the US Food and Drug Administration (FDA) earlier this year in April, however, is less well-known. IDx LLC, an AI diagnostics company based in Iowa, developed the tool known as IDx-DR. The details about the system were published in a paper in Nature Digital Medicine on Tuesday.
Recent advances in artificial intelligence have led to speculation that AI might one day replace human radiologists. Researchers have developed deep learning neural networks that can identify pathologies in radiological images such as bone fractures and potentially cancerous lesions, in some cases more reliably than an average radiologist. For the most part, though, the best systems are currently on par with human performance and are used only in research settings. That said, deep learning is rapidly advancing, and it's a much better technology than previous approaches to medical image analysis. This probably does portend a future in which AI plays an important role in radiology.
Great White North is on the top of that list. Because of its powerful academic research labs, Toronto has supplied a lot of talent in the field but has been experiencing a brain drain. As an effort to retain talent and make Toronto a global supplier of AI capability, the University of Toronto gathered a team of globally renowned researchers and founded the Vector Institute. The independent, non-profit AI research institution has created a lot of buzz and attracted a great deal of funding to its ongoing projects. With a combination of research and commercial goals, according to The Toronto Star, It will be backed by more than $150 million in public and corporate funding.