If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
First, you are strapped from the chest upwards on to the table, with your feet hoisted into stirrups. The table is swung down backwards, so you are tilted, head-down, at an angle of 45 degrees. Then a machine, known by some surgeons as'the 800lb gorilla', can get to work. It sounds so medieval, but this is the most modern of surgical techniques -- robotic surgery. The extraordinary posture, known as the steep Trendelenburg, is necessary to position the patient precisely so the robot arms can reach inside them.
Scientists have designed a model using Artificial Intelligence that can predict risk of death in patients with coronary heart disease (CHD) better than expert-constructed models. According to a new study published in PLOS One, scientists from the Francis Crick Institute, working with University College London Hospitals NHS Foundation Trust and the Farr Institute of Health Informatics Research, developed the AI model using the data of 80,000 patients, available for researchers through UCL's CALIBER platform, which links four sources of electronic health data in England. The model that the AI one was compared to made predictions based on 27 variables chosen by medical experts, while the Crick team got their AI algorithms to train themselves, look for patterns and select the most relevant variables from a set of 600. Both machine learning and AI are picking up steam in healthcare, with hospitals testing or deploying the tech for a range of use cases from treating patients with pancreatic cancer to reducing surgical site infections while experts are saying the next generation of clinical decision support tools will include AI in workflow to improve diagnostics, imaging, radiology and pathology, among other functions. Consultancy McKinsey said last month that hospitals need a solid digital base comprising a modern infrastructure with cloud, mobile and web capabilities in place before starting down the road to AI and machine learning.
Big data, personalized medicine, artificial intelligence. String these three buzzphrases together, and what do you have? A system that may revolutionize the future of healthcare, by bringing sophisticated health data directly to patients for them to ponder, digest, and act upon--and potentially stop diseases in their tracks. At Singularity University's Exponential Medicine conference in San Diego this week, Dr. Ran Balicer, director of the Clalit Research Institute in Israel, painted a futuristic picture of how big data can merge with personalized healthcare into an app-based system in which the patient is in control. Picture this: instead of going to a physician with your ailments, your doctor calls you with some bad news: "Within six hours, you're going to have a heart attack. So why don't you come into the clinic and we can fix that."
As we relentlessly enter information into our EHRs, we typically perceive that we are just recording information about our patients to provide continuity of care and have an accurate representation of what was done. While that is true, the information we record is now increasingly being examined for many additional purposes. A whole new area of study has emerged over the last few years known as "real-world data," and innovators are beginning to explore how machine learning (currently employed in other areas by such companies as Amazon and Google) may be used to improve the care of patients. The information we are putting into our EHRs is being translated into discrete data and is then combined with data from labs, pharmacies, and claims databases to examine how medications actually work when used in the wide and wild world of practice. Let's first talk about why real-world data are important.
Healthcare has long considered technology as essential in improving the treatment and care of patients. With AI ushering in what's said to be the fourth industrial revolution, many hospitals are gearing up for new AI-based applications that will further improve patient outcomes, increase physician productivity and reduce errors. Based on several studies, AI-based applications can potentially save the U.S. healthcare industry $150 billion annually by 2026. As AI technology in healthcare continues to gain wider acceptance, several areas are predicted to experience the most success and make the greatest impact. Hospitals find it challenging to recruit nurses in several parts of the U.S., and in some rural areas, the nursing shortage is affecting patient care.
The entirety of clinical science has been shaped solely by an historic ability to measure something. We measure blood pressure only because 150 years ago someone found that they could, without at first understanding the full implications. A decade or so later, doctors showed that individuals with high blood pressures were having more life-threatening health events than those with a lower value, and this in turn encouraged the development of drugs that could lower high blood pressure. Several years later, conclusive evidence arrived that these agents could reduce life-threatening cardiovascular events, including stroke, fatal heart attack and kidney failure. And so a whole field of clinical science and an associated industry of diagnostics, investigation and treatment evolved from this initial enquiry into blood pressure.
The Mayo Clinic and health technology vendor Eko are working together to develop and commercialize a machine learning-based algorithm that screens patients for low ejection fraction, which is linked to heart failure. A low ejection fraction number, often measured by an echocardiogram, suggests problems with the heart's pumping function. However, echocardiography is an expensive and time-consuming medical imaging test using ultrasound that is less accessible than a doctor with a stethoscope. "With this collaboration, we hope to transform the stethoscope in the pocket of every physician and nurse from a hand tool to a power tool," said Paul Friedman, MD, chair of cardiovascular medicine at the Mayo Clinic. "The community practitioner performing high school sports physicals and the surgeon about to operate may be able to seamlessly tap the knowledge of an experienced cardiologist to determine if a weak heart pump is present simply by putting a stethoscope on a person's chest for a few seconds."
The Scripps Research Translational Institute is partnering with graphics firm NVIDIA to develop AI and deep learning best practices, tools and infrastructure to develop AI applications using genomic and digital health sensor data. With NVIDIA, California-based research organisation Scripps will establish a centre of excellence for artificial intelligence in genomics and digital sensors. Scripps and NVIDIA will work to advance the use of machine learning and deep learning to harness the exploding quantity of health data. The partnership will focus on data generated by faster, more affordable genome sequencing gear, and digital health sensors such as smartwatches, blood pressure cuffs and glucose monitors. NVIDIA AI experts and Scripps researchers and clinicians will use deep learning and machine learning, to tackle the deluge of genomics and sensor data.
No matter what industry you're in, Artificial Intelligence (AI) is all the rage. In pop culture alone it's the central theme of HBO's Westworld, where humanoid AI robots pretend to be people, or even the most recent season of Silicon Valley where a major character was an AI-powered robot named Fiona. AI is also the central, recurring theme at every conference. Even at giant tradeshows like the Consumer Electronics Show (CES) where, this year, we saw autonomous vehicles, voice-enabled bot driving assistants within cars, L'Oreal's thumbnail-sized UV sensor patch, and hundreds of other AI-enabled "smart" products. At South by Southwest (SXSW), it seemed every other session was about AI.