From the acceleration of regulatory submissions - by identifying data gaps that have led to delays or rejections in the past - to the transformation of the conduct of clinical trials and patient safety monitoring, artificial intelligence (AI) has substantial potential to change the way life sciences organisations operate. Back-end technology already exists to facilitate more intelligent and proactive health monitoring by taking things forward as drug companies rely on finding the optimum ways for patients to interact with and use the tools. There is also important safety monitoring potential and drug feedback potential, as long as intelligent tools based on AI and machine learning are in the background offering companies what to look for and ways of deciphering what it all means. As more and more companies identify opportunities to turn AI-enabled insights into timely and beneficial outcomes - whether by accelerating market entry, successfully mining social media for potential adverse events and other patient feedback, discovering new indications, or improving the manufacturing and supply chain process - advanced automation through increased machine intelligence looks set to be the way forward.
An end-of-life chatbot that helps terminally ill patients struggling with tough decisions is being tested by researchers. An end-of-life chatbot that helps terminally ill patients struggling with tough decisions is being tested by researchers. It automatically alerts caregivers and family members when a patient is ready to formalise end-of-life plans. It automatically alerts caregivers and family members when a patient is ready to formalise plans.
Furthermore, through machine learning, the data collected will be analyzed and processed in order to provide personalized feedback to users about their own medical issues. The intermingling of the burgeoning technology of Artificial Intelligence and equally revolutionary Blockchain has seen Doc.ai's team propose their platform can answer personal medical questions - from masses of data collected - at a touch of a button. "We are making it possible for lab tests to converse directly with patients by leveraging advanced artificial intelligence, medical data forensics, and the decentralized Blockchain. The details of this platform may sound a lot like science fiction, but it is essentially the manipulation of data which is analyzed by machine learning, to provide medical answers.
Companies like Alphabet Inc. (NASDAQ:GOOGL) (NASDAQ:GOOG), International Business Machines (NYSE:IBM), and NVIDIA Corporation (NASDAQ:NVDA) are using the predictive power of AI to provide doctors with additional tools to fight disease. Researchers at Alphabet's Google Brain have developed an AI system that can examine images of the retina and detect DR as well as human ophthalmologists, according to a paper published in the Journal of the American Medical Association. HeartFlow is medical technology company that is using graphics processors from NVIDIA Corporation (NASDAQ:NVDA) and deep learning to create 3D mapping used to provide early detection of heart disease. The Motley Fool owns shares of and recommends Alphabet (A shares), Alphabet (C shares), and Nvidia.
Intelligent machines are taking over thousands of jobs, and being qualified is no longer enough to keep your job. Even specialized professions like medicine, law and banking are feeling the heat of Artificial Intelligence (AI). So while "smarter computers are one key to success, doing a smarter job of humans and machines working together is far more important". Recognizing the importance of this skill is Geoff Colvin in his book Humans Are Underrated: What High Achievers Know That Brilliant Machines Never Will.
DXC Labs has been leading the R&D for our industrialized AI offering by rapidly developing prototypes of machine learning solutions for various "data stories." For customers, our prototypes provide visualizations of the predicted results as "actionable insights" in Microsoft's Power BI that can be drilled into for further analysis. The data story was: "Reduce patient care cost and improve patient care and logistics for elective care." Predicting diabetes risk to reduce patient care costs -- visualizations of the actionable insights obtained from otherwise disparate data.
Artificial intelligence (AI), with its capability to draw "intelligent" inferences based on vast amounts of raw data, may hold the solution. Follow the money, and you'll see big bets on healthcare AI across the globe: 63% of healthcare executives worldwide already actively invest in AI technologies, and 74% say they are planning to do so. PwC's Global Artificial Intelligence Study, which analyzed AI's potential impact on each industry, found that healthcare (along with retail and financial services) is poised to reap some of the biggest gains from AI in the form of improved productivity, enhanced product quality, and increased consumption. For example, 94% of survey respondents in Nigeria, 85% in Turkey, 41% in Germany, and 39% in the UK are willing to talk to and interact with a device, platform, or AI-guided robot that can answer health questions, perform tests, make diagnoses based on those tests, and recommend and administer treatment.
Recent applications of machine learning with big data are able to predict diseases--such as Alzheimer's and diabetes--with incredible accuracy, years before the onset of symptoms. To assess the likelihood of a patient developing a certain condition, physicians have traditionally relied on risk calculators such as this one. Bringing together the data collected in many large-scale studies across diverse medical specialties, together with information from our medical records and other sources, doctors can accurately calculate the likelihood of suffering from a disease, a patient's possible outcome, and even figure out what the main predictors for each illness are. The CS experts have brought to the table the capacity to identify, develop, and fine-tune machine learning algorithms and techniques to predict conditions with better accuracy and speed.
It's important that organisations approach this transformation as a journey and not a quick fix: with the right technology, home healthcare companies can increase efficiency of processes, in turn improving client and caregiver satisfaction while reducing costs. Here's how tech is transforming home healthcare: Automation helps to ensure technology is seamless and remains invisible, allowing home healthcare companies to focus on delivering the best care possible to their patients. In addition, AI can provide predictive caregiver scheduling, predictive home appointment duration, predictive travel (to the detail of street-level predictive routing), and predictive cancellation which contributes to no-show appointment prevention, allowing an overall improved customer service. For example, rather than manually entering basic patient data, AI technology can automate this process and allow caregivers to concentrate on the deep analysis of patient data.
This application of population health AI data will occur only if the EHR companies can profit from the function by charging the physicians for the tabulated population data analysis. Without concomitant software to overcome prior authorization rationing of prescriptions by insurance companies and Pharmacy Benefit Managers or built-in EHR software to override diagnostic and treatment rationing by insurance bureaucrats, the benefits of AI clinically for the patient or physician will never be applied at the bedside. This function of automated overriding of prior authorization rationing of Artificial Intelligence (or NAI) suggestions could be easily delivered to physicians simply by cross-linking insurance company drug formularies with patients insurance plans using several prescription tracking companies already contracted with EMR companies and used daily in most pharmacies. I'm betting, the low earnings and low profitability potential of prior authorization API overriding software for the EHR industry combined with data (price and formulary) blocking by Pharmaceutical Industry Benefit Managers (PBM's) and the insurance companies will prevent implementation or this most desired clinical function.