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) …
There is a lot of research being done on the implementation of AI in medicine. In fact, healthcare chatbots are becoming more and more common. As chatbot technology improves our experiences with self-driving cars and virtual help desks, it's also improving health services through improved data entry, more detailed analytics, and better self-diagnosis. But exactly how can a chatbot improve your workplace? And what role does machine learning play in the process?
The once futuristic potential for artificial intelligence in healthcare is coming to fruition, integrating with key operational and clinical aspects of healthcare to improve patient care. But how far will it go? What the future holds The future holds huge potential for AI, as companies are testing algorithms to detect conditions such as pneumonia, flagging those patients for physicians to take a more nuanced look. Mark Hoffman, PhD, chief research information officer at Children's Research Institute at Children's Mercy Kansas City, anticipates the next step will be focused on methods to evaluate the accuracy of AI, as well as its value, in diagnostics. And as with any changes in healthcare, regulatory agencies will slow the process.
In today's business era, AI chatbots are redefining the way pharma companies interact and engage with their clients. These chatbots mimic human conversation via text or auditory means which is a huge opportunity for the pharma industry to have a one-to-one conversation with their customers, doctors, and patients. Apart from that, by using intelligent virtual assistants, pharmaceutical companies can build a strong relationship with doctors and patients by communicating with them and assisting them directly. The two main areas within this industry that will drastically benefit from developing a pharma chatbot are R&D and marketing. By developing a chatbot, a pharma company can have a virtual digital assistant to provide information to users on various topics, such as how to respond to inquiries on certain health conditions, a complex drug procedure, and the appropriate method of using a certain medical device.
What is the future of artificial intelligence (AI) in healthcare? It's a big question that almost every medical professional has had cause to ask recently, and the answer is even bigger. In fact, at this moment, the answer is something along the lines of, "We don't exactly know yet, but it's going to be monumental." There are, of course, current applications for AI being used and developed today that we can look at to inform our prediction of how AI will be used in healthcare in the future, and that's exactly what we're going to cover here. By the end of this article, we will have an answer to our question.
After years of stagnation and inadequate innovations, the call for care that is higher quality and more accessible and that costs less is beginning to be answered. We're starting to see incremental progress toward meaningful healthcare technology and reimagined delivery models. New developments in digital medicine, DIY care and AI are emerging, with the potential to advance the industry in ways that previous attempts have failed. Despite signs of progress, doctor's office wait times continue to rise. Middle- and low-income patients are in critical need of more affordable primary and specialty care.
A new study finds that an interactive voice application using artificial intelligence is an effective way to monitor the well-being of people being treated for serious mental illness. Researchers from UCLA followed 47 people for up to 14 months using an application called MyCoachConnect. The data were collected from 2013 and 2015. All of the patients were being treated by physicians for serious mental illnesses, including bipolar disorder, schizophrenia and major depressive disorder. For the study, published in PLOS One, participants called a toll-free number one or two times a week and answered three open-ended questions when prompted by a computer-generated voice.
Hyland, a vendor of content services and enterprise imaging technologies, will have a major presence at the HIMSS20 Global Conference. It's a big player in healthcare information technology, and has a team with decades of experience in the industry. Ahead of HIMSS20, Healthcare IT News interviewed Susan deCathelineau, senior vice president of healthcare sales and services at Hyland. She offers her perspective on the key trends impacting conference attendees. She identifies interoperability, AI for clinical uses, and providers finally embracing the cloud as three trends that healthcare CIOs and other health IT leaders should be on top of.
Two pharmaceutical companies have embarked on a brave new world, having begun human testing for the first time on a drug treatment for obsessive-compulsive disorder designed by artificial intelligence. British startup Exscientia and Japan's Sumitomo Dainippon Pharma used artificial intelligence to create the drug in less than 12 months, cutting four years from the average time it takes ordinary humans to develop a medication. Exscientia CEO Andrew Hopkins described the clinical human trial of the drug -- a molecule called DSP-1181 -- as a "key milestone in drug discovery." TOP STORIES Milwaukee teacher placed on leave after praising Rush Limbaugh's cancer diagnosis Impeachment 2.0? "Our driving motivation is to accelerate the range of innovative drugs from cutting-edge science entering into the clinic to increase the treatment options for patients. That means reducing the time to make and test a drug. The consumer should see benefits from faster progress to the clinic," Mr. Hopkins told The Washington Times.
Almost 400,000 babies were born prematurely--before 37 weeks gestation--in 2018 in the United States. One of the leading causes of newborn deaths and long-term disabilities, preterm birth (PTB) is considered a public health problem with deep emotional and challenging financial consequences to families and society. If doctors were able to use data and artificial intelligence (AI) to predict which pregnant women might be at risk, many of these premature births might be avoided. "Premature birth prediction has been an exceedingly challenging problem," said Ansaf Salleb-Aouissi, a senior lecturer in discipline from the computer science department. "But we are now at a point where we can use machine learning to develop a dynamic risk prediction system for pregnant women. Creating a system that can process large models of data with AI algorithms we develop would be a great benefit to supplement physicians' 'real-life' expertise."
Machine learning is accelerating the pace of scientific discovery across fields, and medicine is no exception. From language processing tools that accelerate research to predictive algorithms that alert medical staff of an impending heart attack, machine learning complements human insight and practice across medical disciplines. However, with all the "solutionism" around AI and machine learning technologies, healthcare providers are understandably cautious about how it will really help patients and bring a return on investment. Many AI solutions on the market for healthcare purposes are tailored to solve a very specific problem, such as identifying the risk of developing sepsis or diagnosing breast cancer. These out-of-the-box AI solutions make it difficult or impossible for companies to customize their models and get the most out of their investment.