AI is already on its way to transforming healthcare delivery and improving patient outcomes. However, while AI, Machine Learning, and Robotics are all designed to reduce human error and increase the predictability of patient care, they also create new risks across the healthcare liability landscape. In a situation where a healthcare provider uses AI to treat a patient who has a less than a desired outcome (or even simply an unanticipated one), we anticipate liability suits against those healthcare providers, healthcare systems, AI software companies, and robotic device manufacturers. In this post, we will consider what happens when lawsuits get ahead of science, insurance considerations in this new liability landscape, and possible modifications to legal doctrine to address this new science. What makes AI so compelling is its use of predictive, learning algorithms (Machine Learning) to improve the precision of the practice of medicine.
To predict 72-h and 9-day emergency department (ED) return by using gradient boosting on an expansive set of clinical variables from the electronic health record. This retrospective study included all adult discharges from a level 1 trauma center ED and a community hospital ED covering the period of March 2013 to July 2017. A total of 1500 variables were extracted for each visit, and samples split randomly into training, validation, and test sets (80%, 10%, and 10%). Gradient boosting models were fit on 3 selections of the data: administrative data (demographics, prior hospital usage, and comorbidity categories), data available at triage, and the full set of data available at discharge. A logistic regression (LR) model built on administrative data was used for baseline comparison. Finally, the top 20 most informative variables identified from the full gradient boosting models were used to build a reduced model for each outcome.
Machine learning, combined with neuroimaging data, has the potential to objectively determine whether a patient is suffering pain and where it's located. Accurate pain assessment is critical to provide proper diagnosis and treatment, medical experts agree. However, it's difficult to quantify pain, and most assessments are subjective. Subjective assessments are inconsistent and can't be used when a patient can't communicate, such as during surgery. They're also of limited value in understanding the neurophysiological processes underlying different types of pain.
Disease Diagnosis & Medication: Data privacy and regulatory barriers will cause a delay in disrupting this segment. If the patient is able to access their own data, they should be able to use AI for diagnosis of their X-rays or MRI scans as a second opinion. A soldier in war zones can get the AR/VR experience with instructions to help treat themselves and remove a bullet. DNA based personalized medicine to extend the life of humans. Robots to remind you to take medicine pills (e.g.
It is almost 40 years since a full-body magnetic resonance imaging (MRI) machine was used for the first time to scan a patient and generate diagnostic-quality images. The scanner and signal processing methods needed to produce an image were devised by a team of medical physicists including John Mallard, Jim Hutchinson, Bill Edelstein and Tom Redpath at the University of Aberdeen, leading to the widespread use of the MRI scanner, now a ubiquitous tool in radiology departments across the world. MRI was a game-changer in medical diagnostics because it didn't require exposure to ionising radiation (such as X-rays), and could generate images on multiple cross-sections of the body with superb definition of soft tissues. This allowed, for example, the direct visualisation of the spinal cord for the first time. Most people today will have undergone an MRI or know somebody who has.
The academic medical center of the University of Michigan is leveraging investments in artificial intelligence, machine learning and advanced analytics to unlock the value of its health data. According to Andrew Rosenberg, MD, chief information officer for Michigan Medicine, the organization currently has 34 ongoing AI and machine leaning projects, 28 of which have principal investigators. "There's a lot of collaboration around these projects--as there should be for the diversity of thought and background needed to deal with complex problems--working with at least seven other U of M schools," Rosenberg told the Machine Learning for Health Care conference on Friday in Ann Arbor, Mich. "That's one of the powers that we enjoy." One of the machine learning projects cited by Rosenberg leverages a combination of electronic health records, monitor data and analytics to predict acute hemodynamic instability--when blood flow drops and deprives the body of oxygen--which is one of the most common causes of death for critically ill or injured patients.
Thanks to a Machine Learning Research Award from Amazon Web Services (AWS) to a research alliance supported by UPMC Enterprises, a seed has been planted to accelerate the consortium's medical research initiatives, help participating entrepreneurs more rapidly scale their innovations, and, in some small fashion, contribute to positioning the Pittsburgh area as a healthcare technology innovation hub. The award provides researchers access to Amazon's cloud-based platform and machine learning tools, enabling them to incorporate sophisticated technology into innovations at an early stage of the development process. These innovations "will be able to be deployed more easily in the real world," says Rob Hartman, PhD, director of translational science, UPMC Enterprises. The Amazon award was made to the Pittsburgh Health Data Alliance (PHDA), which was formed four years ago by UPMC, the University of Pittsburgh, and Carnegie Mellon University. PDHA uses "big data" generated in health care--including patient information in the electronic health record, diagnostic imaging, prescriptions, genomic profiles, and insurance records--to transform the way that diseases are treated and prevented, and to better engage patients in their own care, according to a news release.
When Elon Musk first started talking about launching a brain-computer interface company, he made a number of comments that set expectations for what that idea might entail. The company, he said, was motivated by his concerns about AI ending up hostile to humans: providing humans with an interface directly into the AI's home turf might prevent hostilities from developing. Musk also suggested that he hoped to avoid any electrodes implanted in the brain, since that might pose a barrier to adoption. At his recent public launch of the company (since named Neuralink), worries about hostile AIs did get a mention--but only in passing. Instead, we got a detailed technical description of the hardware behind Neuralink's brain-computer interface, which would rely on surgery and implanted hardware.
German based Siemens Healthineers purpose is to enable healthcare providers to increase value by empowering them on their journey towards expanding precision medicine, transforming care delivery, and improving patient experience, all enabled by digitalising healthcare. An estimated five million patients globally everyday benefit from their innovative technologies and services in the areas of diagnostic and therapeutic imaging, laboratory diagnostics and molecular medicine, as well as digital health and enterprise services. They are a leading medical technology company with over 170 years of experience and 18,000 patents globally. With about 50,000 dedicated colleagues in over 70 countries, they will continue to innovate and shape the future of healthcare. Speaking to HEQ, Global Head of Digitalizing Healthcare Joerg Aumueller explains the integration of AI into healthcare and how Siemens Healthineers are leading the change.
Accelerating with an exponential growth, artificial intelligence (AI) is all set to move from experimental stages to live industry implementations and all is set to mark its presence across all industry verticals. AI is all about virtualizing human cognitive functions in the form of software brains. For organizations, harnessing AI is not optional, albeit it is critical to stay competitive. Gartner in its recent study (2018), predicts the business value derived from AI to reach $3.9 trillion by 2022. With the disruptive potential, the investments in AI are ever-increasing.