Sign in to report inappropriate content. Recent advances in artificial intelligence and machine learning are changing the way doctors practice medicine. Can medical data actually improve health care? At this seminar, Harvard Medical School scientists and physicians will discuss how AI assists doctors in diagnosing disease, determining the best treatments and predicting better outcomes for their patients.
Technology developed by Israel's MedAware could potentially save the United States health system $800 million annually by preventing medication errors, based on a study published earlier this week in the Joint Commission Journal on Quality and Patient Safety.MedAware developed an AI-based patient safety solution. The new study that was conducted by two Harvard doctors validates both the significant clinical impact and anticipated ROI of MedAware's machine learning-enabled clinical decision support platform designed to prevent medication-related errors and risks.MedAware uses AI methods similar to those used in the finance sector to stop fraud, by identifying "outliers" from a trend or practice in order to recognize suspicious or erroneous transactions. Most other electronic health record alert systems are rule based.In the US alone, prescription drug errors result in "substantial morbidity, mortality and excess health care costs estimated at more than $20 billion annually in the United States," according to Dr. Ronen Rozenblum, assistant professor at Harvard Medical School and director of business development for patient safety research and practice at Brigham and Women's Hospital. Rozenblum was the study's lead author, along with Harvard professor Dr. David Bates. Rozenblum, an Israeli who has been living in Boston for more than a decade, has been testing MedAware for the past five years.
Abstract--In medicine, a communicating virtual patient or doctor allows students to train in medical diagnosis and dev elop skills to conduct a medical consultation. In this paper, we describe a conversational virtual standardized patient sy stem to allow medical students to simulate a diagnosis strategy o f an abdominal surgical emergency. We exploited the semantic properties captured by distributed word representations t o search for similar questions in the virtual patient dialogue syste m. We created two dialogue systems that were evaluated on dataset s collected during tests with students. The first system based on handcrafted rules obtains 92.29% as F 1-score on the studied clinical case while the second system that combines rules an d semantic similarity achieves 94.88%. It represents an error reduction of 9.70% as compared to the rules-only-based system. The medical diagnosis practice is traditionally bedside taught. Theoretical courses are supplemented by internshi ps in hospital services. The medical student observes the practi ce of doctors and interns and practices himself under their contr ol. This type of learning has the disadvantage to confront immediately the medical student with complex situations withou t practical training (technical and human) beforehand.
"The attention around AI tends to focus on the latest technologies," Iansiti argues, "but the firms that are thriving have harnessed the subtle, inherent power of AI to break down traditional operational constraints, capture new value, and accelerate growth and innovation." What sets AI-driven firms apart is their ability to avoid the inefficiencies and bottlenecks that plague growth when complexity -- primarily caused by humans -- outstrips organizational capacity. These firms strive to construct a model for operational execution that does not require human intervention (ideally, no real-time "human bottlenecks"). In the new digital operating model, most operational tasks circumvent humans entirely. The ultimate aim is to automate and digitize as many operational processes as possible to take advantage of digital reliability and scalability.
But if the narrative of the present is one of "prediction machines," referencing the book of the same title by Ajay Agrawal, Joshua Gans, and Avi Goldfarb, the narrative of the future will belong to "decision machines." If the narrative of the present is one of managers who are valued for showing judgment in decision making -- don't tell me whether someone will do well on the job, or whether a new product will win in the marketplace, but tell me instead who I should hire, which products I should bet on -- then the narrative of the future will be one in which we are valued for our ability to judge and shape the decision-making capabilities of machines. Artificial intelligence (AI) is the pursuit of machines that are able to act purposefully to make decisions towards the pursuit of goals. Machines need to be able to predict to decide, but decision making requires much more. Decision making requires bringing together and reconciling multiple points of view.
The quest to understand what's happening inside the minds and brains of animals has taken neuroscientists down many surprising paths: from peering directly into living brains, to controlling neurons with bursts of light, to building intricate contraptions and virtual reality environments. In 2013, it took the neurobiologist Bob Datta and his colleagues at Harvard Medical School to a Best Buy down the street from their lab. At the electronics store, they found what they needed: an Xbox Kinect, a gaming device that senses a player's motions. The scientists wanted to monitor in exhaustive detail the body movements of the mice they were studying, but none of the usual laboratory techniques seemed up to the task. So Datta's group turned to the toy, using it to collect three-dimensional motor information from the animals as they explored their environment.
A few years ago, I was invited to Minnesota Public Radio to speak about various legal issues related to cybersecurity. To my left was Bruce Schneier, a famous and respected cybersecurity researcher and prolific author. There wasn't much disagreement between us during the interview, though I recall emphasizing a bit more the FTC's cybersecurity efforts, noting that I thought they were doing a pretty good job in the current regulatory vacuum, building a de-facto common law as they went along. In his latest book, "Click Here to Kill Everybody," Schneier argues, among other things, that there is a systemic lack of security in all things computer (something he calls "Internet ", essentially an extension of IoT) and that what is needed to fix this is government intervention. Schneier's call for intervention comes in the form of a new government agency, one that has the ability to "coordinate and advise with other agencies" on the Internet .
Artificial intelligence (AI) is poised to help deliver precision medicine and health.1,2 The clinical and biomedical research communities are increasingly embracing this modality to develop tools for diagnosis and prediction as well as to improve delivery and effectiveness of healthcare. New breakthroughs are being developed in an unprecedented fashion and the developed ones have obtained regulatory approval and found their way into routine medical practice.3,4,5 Yet, the medical school curriculum as well as the graduate medical education and other teaching programs within academic hospitals across the United States and around the world have not yet come to grips with educating students and trainees on this emerging technology. Several expert opinions have pointed to the benefits and limitations associated with the use of ML in medicine,1,2,6,7,8,9,10 but the aspect related to formally educating the younger generation of medical professionals has not been openly discussed.
Global health care expenditure has been projected to grow from US $7.7 trillion in 2017 to US $10 trillion in 2022 at a rate of 5.4% . This translates into health care being an average of 9% of gross domestic product among developed countries [2,3]. Some key global trends that have led to this include tax reform and policy changes in the United States that could impact the expansion of health care access and affordability (Affordable Care Act) , implications on the United Kingdom's health care spend based on the decision to leave the European Union , population growth and rise in wealth in both China and India [6-8], implementation of socioeconomic policy reform for health care in Russia , attempts to make universal health care effective in Argentina , massive push for electronic health and telemedicine in Africa , and the impact of an unprecedented pace of population aging around the world . From clinicians' perspective there are many important trends that are affecting the way they deliver care of which the growth in medical information is alarming. It took 50 years for medical information to double in 1950. In 1980, it took 7 years. In 2010, it was 3.5 years and is now projected to double in 73 days by 2020 .