Another tech company doing something it said it wouldn't. Another eye roll, another shrug? On Tuesday, the London-based artificial intelligence company DeepMind announced that the team behind Streams – an app designed to monitor people in hospital with kidney disease – will be joining DeepMind's sister company Google. The tech giant wants to turn Streams into an AI-powered assistant for doctors and nurses. To create Streams, DeepMind used identifiable medical records of 1.6 million people obtained in a deal with the Royal …
When working in healthcare, a lot of the relevant information for making accurate predictions and recommendations is only available in free-text clinical notes. Much of this data is trapped in free-text documents in unstructured form. This data is needed in order to make healthcare decisions. Hence, it is important to be able to extract data in the best possible way such that the information obtained can be analyzed and used. State-of-the-art NLP algorithms can extract clinical data from text using deep learning techniques such as healthcare-specific word embeddings, named entity recognition models, and entity resolution models.
Even if machines are not yet universally better than doctors, the challenge to make them better is technical rather than fundamental because of the near unlimited capacity for data processing and subsequent learning and self correction. This "deep learning" is part of "machine learning," where systems learn constantly without the potential cultural and institutional difficulties intrinsic to human learning, such as schools of thought or cultural preferences. These systems continually integrate new knowledge and perfect themselves with speed that humans cannot match. Even complex clinical reasoning can be simulated, including ethical and economic concerns. Increasing amounts of more comprehensive health data from apps, personal monitoring devices, electronic medical records, and social media platforms are being integrated into harmonised systems such as the Swiss Personalised Health Network.3
Robotic devices for clinical rehabilitation of patients with neurological impairments come in a wide variety of shapes and sizes and employ different kinds of actuators. The design process for rehabilitation robots is driven by the intention that the technical system will be paired with a human being; it is of paramount importance that safety and flexibility of operation are ensured. When designing a robotic device for people with paretic limbs it is usually desirable to specify the actuators and controllers in such a way that a degree of compliance and yielding is retained, rather than forcing the limbs to rigidly follow a pre-programmed trajectory. This reduces the likelihood of injury which might result from forcing a stiff joint to move in a non-physiological manner, and it allows the patient to positively interact with the system and actively guide the therapy. It is not uncommon to come across the viewpoint that electric actuators are not well suited to applications having compliant design requirements: in traditional control engineering, DC motors are programmed to provide accurate and fast setpoint tracking; it is often thought that they are not ideally suited for clinical rehabilitation tasks where "soft" behavioural characteristics are called for.
Artificial and pervasive intelligence are paving the way for the transition into Industry 4.0 and are likely to have a lasting impact on the manufacturing sector. Internet of Things (IoT) devices, mobility, and cloud services have given rise to smart machines. Medical devices such as pacemakers, smartphones and tablets, security systems, and manufacturing equipment on the factory floor are only some examples of technologies which are becoming linked to Wi-Fi and the cloud, and this shift towards connectivity is a major element of Industry 4.0. Industry 4.0 is the transition from traditional manufacturing processes and equipment to smart devices, IoT, machine-to-machine (M2M) technologies and data analytics. While a shift towards modern solutions -- when implemented properly -- can result in better visibility on the factory floor and in supply chains, a boost in revenue and an uptick in efficiency, artificial intelligence (AI) may have the potential to push Industry 4.0 even further forward.
Toronto's Humber River Hospital (HRH), which opened in 2015, is the first hospital in the world to install a medical imaging "tile"--or app--into its NASA-style, 4,500 square-foot digital command center in collaboration with GE Healthcare Partners. Recognized as North America's first fully digital hospital, installing a medical imaging tile into its digital command center in July 2018 made sense for HRH, which serves a region of more than 850,000 people. Six other hospitals in the U.S. and one in Europe have already installed or have plans to open command centers next year, a GE spokeswoman told HealthImaging. By 2020, GE hopes digital command centers will become a feature hospitals can't survive without. Essentially, HRH's command center is a literal wall continuously processing real-time data from multiple source systems across the hospital.
Artificial intelligence systems simulate human intelligence by learning, reasoning, and self correction. This technology has the potential to be more accurate than doctors at making diagnoses and performing surgical interventions, says Jörg Goldhahn, MD, MAS, deputy head of the Institute for Translational Medicine at ETH Zurich, Switzerland. It has a "near unlimited capacity" for data processing and subsequent learning, and can do this at a speed that humans cannot match. Increasing amounts of health data, from apps, personal monitoring devices, electronic medical records, and social media platforms are being brought together to give machines as much information as possible about people and their diseases. At the same time machines are "reading" and taking account of the rapidly expanding scientific literature.
For years people have been baffled by the incredible sounds produced by beatboxers. This musical art form involves performers using their vocal tract to create percussive sounds that are not heard in any language. Sometimes individual performers can even sing and beatbox at the same time. Now scientists have used real-time MRI scans to show the incredible way vocal cords move to create these strange sounds. For years people have been baffled by the incredible sounds produced by beatboxers.
For today's leading deep learning methods and technology, attend the conference and training workshops at Predictive Analytics World for Healthcare, June 16-19, 2019 in Las Vegas. Moving patient data online has been a great boon for the practice of medicine. Patient records, formerly pieces of paper in a folder, are transitioning to electronic health records, or EHRs. While this has done wonders for transferring records between offices and aiding in connecting technology like wearables and providing big data for machine learning, the quantity also raises questions of patient privacy and data security. The start of this story is in the volume of data.
Patients and their families often want continuous monitoring and care. Traditional health insurance providers are partnering with telehealth companies, to address those concerns. Anthem is working with American Well, Cigna is working with MDLive, Bupa is working with Babylon Health and Aflac is working with MeMD to deliver benefits of telehealth to it's existing customers. Health insurance providers such as Oscar Health is redefining health-insurance by building the whole customer experience around its own telehealth services. As telehealth continues to replace traditional health care, it is going to inherit some of its challenges. These include increased cost of care due to multiple vendors, complex care pathways, and government policies. However, the question that remains to be answered is will this advanced technology that we call telehealth, be able to redefine the quality, equity and affordability of healthcare throughout the world.