Dr. Daniel Kraft, a Stanford-educated MD who now serves as chair of medicine for Singularity University, a learning community founded by Ray Kurzweil and Peter Diamandis, sees himself as one of those leaders. Kraft will be sharing his observations, predictions, and advice at Health 2.0's Annual Fall Conference in two weeks in Santa Clara, California. "The bottom line is that for the last nine years I've had an interesting journey doing medicine for Singularity University and started this program called Exponential Medicine, which in its essence is that the future of health and medicine isn't digital, mobile, connected health, or AI," Kraft told MobiHealthNews. Click here to register for Health 2.0's Annual Fall Conference.]
The half-day conference, called Healthcare A.I., is happening at Pfizer's offices in Cambridge, MA, and you can still snag a ticket here. We're hard at work on the agenda, but here are some of the key speakers: Esther Dyson, chairman of EDventure Holdings, who is focusing on health and wellness Mirza Cifric, founder and CEO of Veritas Genetics Jessica Zeaske, director of healthcare investments, GE Ventures Dan Karlin, head of experimental medicine, informatics, and regulatory strategy, Pfizer Innovative Research Lab Mark Michalski, executive director of the MGH and BWH Center for Clinical Data Science Charles Koontz, president and CEO of GE Healthcare IT Shahram Ebadollahi, chief science officer, IBM Watson Health Iya Khalil, co-founder and chief commercial officer of GNS Healthcare Jamie Goldstein, founder and partner, Pillar VC Andrew Beck, co-founder and CEO of PathAI Shilpa Lawande, co-founder and CEO of Postscript.us These leaders will discuss how they are using (and investing in) A.I.-related technologies and data-driven approaches to help solve big problems in medicine and healthcare. What's at stake is nothing less than the future of a trillion-dollar industry and the well-being of humans in the age of increasingly intelligent machines. Gregory T. Huang is Xconomy's Deputy Editor, National IT Editor, and Editor of Xconomy Boston.
While progress was slow during the first few decades, AI advancement has rapidly accelerated during the last decade. But, before companies or people can obtain the numerous improvements AI promises to deliver, they must first start with good quality, clean data. Recently, I had the opportunity to interview Nicholas Piette and Jean-Michel Franco from Talend, which is one of the leading big data and cloud integration company. Nicholas Piette added that ensure data quality is an absolutely necessary prerequisite for all companies looking to implement AI.
IBM has over 1,000 researchers working to advance AI science, and as if that's not enough, the company is seeking further help at one of the world's most prestigious universities. With a total budget of $240 million USD over ten years, the MIT-IBM Watson AI Lab will bring together about 100 top AI researchers and specialists to work on new software and hardware primarily for consumer tech, healthcare, and cybersecurity. MIT and IBM researchers will have 4 key areas to focus on: AI Algorithms, the Physics of AI, the Application of AI to industries, and Advancing shared prosperity through AI. Under the umbrella of "shared prosperity", the IBM-MIT collaboration will be looking for more general applications of AI tech benefits.
Is there a robot doctor in the house? Artificial intelligence is a hot topic in many industries, but particularly healthcare, where doctors and patients are now looking to AI-based apps for help with everything from diagnosing minor ailments to sizing up heart problems. In fact, AI seems set to transform several corners of health and medicine, from cancer prognoses to genomics. In just a few minutes, Quid sifted through 2,000 recent academic papers about AI in healthcare.
The strategic investment by HCSC Ventures, Inc., a wholly-owned subsidiary of Health Care Service Corporation which specializes in investments in innovative health care companies, will support the product-line expansion for anomaly detection and real-time operational decision support solutions for healthcare payers. Cogitativo brings a new scientific paradigm to the rapidly growing market for healthcare performance improvements by enabling payers and providers to challenge system complexity through Cogitativo's machine learning platform. "Cogitativo brings a unique blend of computational scientists with nationally recognized health care operators and advanced data science capabilities to help address complex health care challenges." Within the next several months, Cogitativo expects to release expanded machine learning solutions for improving payment accuracy, care anomaly detection and real-time monitoring of payers' care delivery networks.
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.