At one time IBM Watson Health was featured in articles that claimed it might cure cancer. But the splashy coverage of IBM's artificial intelligence brand aimed at the healthcare industry was maybe an instance when the hype about a particular technology -- AI -- got ahead of that technology's actual capabilities. After a few high-profile public failures over the past several years, IBM has announced that it is selling the parts of its Watson Health business to private equity firm Francisco Partners -- a sale that many in the industry had expected for the past year. The assets sold include data sets and products from the many acquisitions IBM completed to roll into the Watson Health brand including Health Insights, MarketScan, Clinical Development, Social Program Management, Micromedex, and imaging software. Francisco Partners will employ key members of the Watson Health team and stand up its own business in the future, the companies said in the announcement of the deal.

Even if artificial general intelligence (AGI) could be achieved, a problem looms: The more complex a system is, the more can go wrong. If a computer could really match human thinking, a great deal could go wrong. In "When AI goes wrong" (podcast 160), Walter Bradley Center director Robert J. Marks is joined once again by members of his research group, Justin Bui and Samuel Haug, who is a PhD student in computer and electrical engineering. The topic is, what happens if AI starts behaving in bizarre and unpredictable ways? A partial transcript and notes, Show Notes, and Additional Resources follow. I want to start out with Paul Harvey's The Rest of the Story. Either Sam or Justin, have you ever heard of Paul Harvey? Justin Bui: I have not. Sam Haug: No, I have not.

When surgeons remove cancer, one of the first questions is, "Did they get it all?" Researchers from Rice University and the University of Texas MD Anderson Cancer Center have created a new microscope that can quickly and inexpensively image large tissue sections, potentially during surgery, to find the answer. The microscope can rapidly image relatively thick pieces of tissue with cellular resolution, and could allow surgeons to inspect the margins of tumors within minutes of their removal. It was created by engineers and applied physicists at Rice and is described in a study published in the Proceedings of the National Academy of Sciences. "The main goal of the surgery is to remove all the cancer cells, but the only way to know if you got everything is to look at the tumor under a microscope," said Rice's Mary Jin, a Ph.D. student in electrical and computer engineering and co-lead author of the study.

DALLAS, TX / ACCESSWIRE / July 27, 2020 / Perimeter Medical Imaging, AI Inc. (TSXV:PINK) today announced the installation of their OTISTM device at the University of Texas MD Anderson Cancer Center (MD Anderson), to further develop ImgAssist AI technology marking an important milestone in this collaboration and Perimeter's ATLAS AI Project. Initiated in mid-July, the ATLAS AI Project allows Perimeter to collaborate with industry-leading cancer care centers that will use OTIS - its proprietary ultra-high resolution imaging platform - to collect images of breast tumors from approximately 400 patients for the purpose of training and testing Perimeter's ImgAssist AI technology. This technology, which is currently under development, is designed to utilize a machine learning model to help surgeons identify, in real-time, if cancer is still present when performing breast-conserving surgery (lumpectomy). This study was made possible, in part, by a $7.4 million grant awarded by the Cancer Prevention and Research Institute of Texas (CPRIT), a leading state body funding cancer research. Jeremy Sobotta, President and CFO stated, "Initiation at MD Anderson is an important milestone in part one of our ATLAS AI Project and marks the next step in our development and clinical validation efforts for our ImgAssist AI software. MD Anderson is one of the largest breast cancer centers in the United States, treating approximately 40,000 patients a year, and is a valued collaborator as we strive to help physicians improve surgical outcomes for breast cancer patients by providing an additional tool for real-time margin visualization and assessment."

These days, we've become all but desensitized to the miraculous convenience of AI. We're not surprised when we open Netflix to find feeds immediately and perfectly tailored to our tastes, and we're not taken aback when Facebook's facial recognition tech picks our face out of a group-picture lineup. Ten years ago, we might have made a polite excuse and beat a quick retreat if we heard a friend asking an invisible person to dim the lights or report the weather. Now, we barely blink -- and perhaps wonder if we should get an Echo Dot, too. We have become so accustomed to AI quietly incorporating itself into almost every aspect of our day-to-day lives that we've stopped having hard walls on our perception of possibility.

In 2014, IBM opened swanky new headquarters for its artificial intelligence division, known as IBM Watson. Inside the glassy tower in lower Manhattan, IBMers can bring prospective clients and visiting journalists into the "immersion room," which resembles a miniature planetarium. There, in the darkened space, visitors sit on swiveling stools while fancy graphics flash around the curved screens covering the walls. It's the closest you can get, IBMers sometimes say, to being inside Watson's electronic brain. One dazzling 2014 demonstration of Watson's brainpower showed off its potential to transform medicine using AI--a goal that IBM CEO Virginia Rometty often calls the company's moon shot. In the demo, Watson took a bizarre collection of patient symptoms and came up with a list of possible diagnoses, each annotated with Watson's confidence level and links to supporting medical literature. Within the comfortable confines of the dome, Watson never failed to impress: Its memory banks held knowledge of every rare disease, and its processors weren't susceptible to the kind of cognitive bias that can throw off doctors. It could crack a tough case in mere seconds. If Watson could bring that instant expertise to hospitals and clinics all around the world, it seemed possible that the AI could reduce diagnosis errors, optimize treatments, and even alleviate doctor shortages--not by replacing doctors but by helping them do their jobs faster and better.

Before performing radiation therapy, radiation oncologists first carefully review medical images of a patient to identify the gross tumor volume -- the observable portion of the disease. They then design patient-specific clinical target volumes that include surrounding tissues, since these regions can hide cancerous cells and provide pathways for metastasis. Known as contouring, this process establishes how much radiation a patient will receive and how it will be delivered. In the case of head and neck cancer, this is a particularly sensitive task due to the presence of vulnerable tissues in the vicinity. Though it may sound straightforward, contouring clinical target volumes is quite subjective.

AI and Machine Learning were two of 2017's hottest technological buzzwords. It's not difficult to understand why: the potential benefits of these technologies are exciting and profound. But artificial intelligence and machine learning both rely on other foundational technologies in order to achieve the results that they promise. Consequently, innovation within the realm of AI is constrained by the limitations of other technologies. Access to high-quality, usable data is one factor that has significant implications for the development of AI.

Last week, hundreds of digital health entrepreneurs, investors, and executives met in Boston for the annual Digital Healthcare Innovation Summit. Among the more frequently discussed trends and technologies was artificial intelligence -- its promise, its stumbles, and how it should be implemented to best serve healthcare. "I started marketing Watson for Oncology in January of 2016. I'm almost approaching two years," Deborah DiSanzo, general manager at IBM Watson Health, said during a session. "By the end of this year, I will have over 20,000 patients [and] over 120 hospitals using it, and really seeing helping oncologists all over the world. Nothing that I have done in my life in healthcare technology has gone as fast as that, and that is not hype."

In 2016, venture capitalists invested $5 billion in startups involving artificial intelligence, representing a 40 percent increase from 2012. With hopes of securing a foothold in what promises to be a multi-billion dollar industry, some of the most influential companies in the world--including IBM, Apple, and Google--are pouring hundreds of millions of dollars into their AI research-and-development labs. Health care in particular has been a favourite target for these investments. Google's research website states that "machine learning has dozens of possible application areas, but healthcare stands out as a remarkable opportunity to benefit people." As with any burgeoning industry, there have been gaffes along the way.