Shaun Patel has such a tranquil voice that it's easy to see how he convinces patients to let him experiment in the depth of their brains. On the phone, in his office at Massachusetts General Hospital (he is also on faculty at Harvard Medical School), the neuroscientist spoke about gray matter almost as if he were guiding me in meditation. Or perhaps that was just the heady effect of him detailing a paper he had just published in Brain, showing how, using implants on his patients, he could enhance learning by stimulating the caudate nucleus, which lies near the center of the brain.1 You have to time the electric pulse just right, he told me, based on the activity of certain neurons firing during an active learning phase of a game. A perfectly timed pulse could speed up how quickly his patients made the right associations. Using similar methods, he said he has induced people to make more financially conservative bets.
The International Society for Pharmacoeconomics and Outcomes Research (ISPOR) is set to host a webinar at noon EST Nov. 15 on some challenges involved with using machine learning algorithms in prediction and causal inference for health policy research questions. Harvard Medical School Associate Professor Sherri Rose has been tapped as the speaker for the 1-hour online event, "Machine Learning for Health Economics and Outcomes: Prediction and Causal Inference." To learn more about this webinar, click here.
Registration for this conference is now closed. This conference is anchored and building on the preview of the Special National Academy of Medicine (NAM) publication titled: "Artificial Intelligence in Healthcare: The Hope, The Hype, The Promise, The Peril." Co-led by Michael Matheny and Sonoo Thadaney Israni. Registration includes course materials, certificate of participation, breakfast and lunch. CME Certificate Fee: $25.00 Note: If you would like to receive CE Credit for your attendance, there will be a $25.00 fee option after the conference evaluation is completed and your conference attendance is verified. Your email address is used for critical information, including registration confirmation, evaluation, and certificate.
Pathologists agreed just three-quarters of the time when diagnosing breast cancer from biopsy specimens, according to a recent study. The difficult, time-consuming process of analyzing tissue slides is why pathology is one of the most expensive departments in any hospital. Faisal Mahmood, assistant professor of pathology at Harvard Medical School and the Brigham and Women's Hospital, leads a team developing deep learning tools that combine a variety of sources -- digital whole slide histopathology data, molecular information, and genomics -- to aid pathologists and improve the accuracy of cancer diagnosis. Mahmood, who heads his eponymous Mahmood Lab in the Division of Computational Pathology at Brigham and Women's Hospital, spoke this week about this research at GTC DC, the Washington edition of our GPU Technology Conference. The variability in pathologists' diagnosis "can have dire consequences, because an uncertain determination can lead to more biopsies and unnecessary interventional procedures," he said in a recent interview.
We keep hearing this from all quarters: 'The world is changing rapidly'. While it's a fact, we need to understand better what these changes are, how they can affect industry, and how management education needs to prepare students for these changes. Let us look at two prominent emerging areas that are both disrupting as well as enabling businesses: data science and machine learning. It's is a vast field that transcends businesses, mathematics, statistics and computer science. This truly multidisciplinary field extracts knowledge and insights from data in various forms with the use of scientific methods, algorithms, processes and computer systems; further, it is used for business objectives.
'Independently I had started to do my research on how we can apply artificial intelligence to investigate some of the challenges that beset major programmes,' said Quang. 'What stood out for me from the course, is how we can use artificial intelligence to tackle both complexity and behavioural decision making. This is now the basis of a multi-year research programme I have the privilege of conducting with Saïd Business School as an Associate Scholar.' The pair leveraged the Oxford network to build the business. Throughout their programme they regularly discussed and refined their ideas with academics and classmates, several of whom are now shareholders.
Major League Baseball has teamed with creative software developer Adobe to offer dozens of business school students access to data on fan behavior as part of the software giant's yearly analytics competition. For a chance at $60,000 in cash and prizes, the students will analyze the information, which includes stats like in-game purchases, web traffic and customer drop-off tallies, and distill it into recommendations for how the league can better expand its in-person stadium and retail experience to its digital properties. This year's contest will be the first in the decade-old Adobe Analytics Challenge to include machine learning software among the tools to which students have access, namely Adobe Sensei, the artificial intelligence engine that powers much of the creative software giant's customer targeting and predictive analytics suite. Specifically, students will look for anomalies and behavioral patterns in the data that might point to elements of the MLB's digital user experience that are driving people away, or particularly successful features upon which the league's developers should expand. The data is segmented by customer demographics and spans the MLB's flagship website, mobile apps and other digital properties.
Over the last decade, though, the tech industry has downsized ultrasound scanners into devices resembling TV remotes. It has also created digital stethoscopes that can be paired with smartphones to create moving pictures and readouts. Proponents say these devices are nearly as easy to use as stethoscopes and allow doctors to watch the body in motion and actually see things such as leaky valves. "There's no reason you would listen to sounds when you can see everything," Topol said. At many medical schools, it's the newer devices that really get students' hearts pumping.
Shaun Patel has such a tranquil voice that it's easy to see how he convinces patients to let him experiment in the depth of their brains. On the phone, in his office at Massachusetts General Hospital (he is also on faculty at Harvard Medical School), the neuroscientist spoke about gray matter almost as if he were guiding me in meditation. Or perhaps that was just the heady effect of him detailing a paper he had just published in Brain, showing how, using implants on his patients, he could enhance learning by stimulating the caudate nucleus, which lies near the center of the brain.1 You have to time the electric pulse just right, he told me, based on the activity of certain neurons firing during an active learning phase of a game. A perfectly timed pulse could speed up how quickly his patients made the right associations.
In the world of health care, artificial intelligence (AI) is an umbrella term for computer programs that aid medical diagnosis or enhance clinical decision making. So far, AI has proven most successful at analyzing medical images to improve diagnoses in dermatology, ophthalmology and oncology. Some experts estimate that AI could save the U.S. health care system $150 billion annually by 2026. Others caution that real-world challenges such as bias in data collection and ethical concerns remain formidable. Isaac S. Kohane, MD, PhD, chair of the Department of Biomedical Informatics at Harvard Medical School, is an expert on AI in health care.