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A real-life application for artificial intelligence in health care 20180711
A real-life application for artificial intelligence in health care July 11, 2018Optum Artificial intelligence (AI) is everywhere. It’s in our shopping experiences, our search engines, our dining and entertainment recommendations. We’ve seen AI-based medicine on TV for decades, and we can make that a reality — if we go deeper. I’m more excited than ever about the possibilities of AI in health care, but we need to get past the hype on ideas like robots replacing doctors and demonstrate that AI applications can really pay off. We need to find clear connections between AI capabilities and real needs in the health care system. One of those needs is new ways to diagnose and treat chronic diseases. For example, take Alzheimer’s disease — a debilitating condition that slowly degrades memory and other mental functions for millions of people living with the disease. Alzheimer’s has been difficult to treat and impossible to cure, with more than 99 percent of the Alzheimer’s drug trials failing over the past decade. However, progress is being made.
#hc  #ai  #applications  +Optum 
4 days ago by phil_hendrix
Artificial Intelligence: The new normal 2018
Managing data the way you always have doesn’t cut it By Ipsita Pradhan – IRM-SIG in ERM in Banking & Financial Services Artificial Intelligence (AI) Game changing Cognitive computing, a role in risk management Data and Risk Critics views Way ahead AI in Risk Management (free event) Artificial Intelligence: Artificial intelligence (AI) dates back to 1956 when it was introduced as a subdivision of computer science, which was expected to operate intelligently and respond as efficiently as humans. Artificial intelligence is generally divided into four different categories: narrow, general, weak and strong. Narrow AI is programmed to complete a task as it is whereas general AI is programmed to rationalise. Weak AI is programmed to behave in the way humans do whereas strong AI is programmed to mimic humans’ way of thinking. Organizations are now looking for autonomous algorithm and talented Human capital to design the algorithms. The new lingo is “Elite MBA” (Management by Algorithm). This is the new normal.
#ai  #applications  #fraud  #detection  #EU 
5 days ago by phil_hendrix
AgShift Expands its Deep Learning Food Inspection to Nuts and Seafood | The Spoon
AgShift Expands its Deep Learning Food Inspection to Nuts and Seafood By Chris Albrecht - June 29, 2018 0 Share on Facebook Tweet on Twitter AgShift, a startup that uses computer vision and deep learning to determine food quality in the supply chain, is broadening its capabilities beyond produce into edible nuts and seafood. We named AgShift as one of our Food Tech 25 companies last month because of the way it allows produce buyers and sellers to come to an objective price. AgShift’s software lets inspectors take a picture of fruit with their mobile phone and upload it to the cloud where AgShift’s deep learning algorithms would assess its quality (color, bruising, etc.) and base that against USDA, or other customized ratings, to provide a food grade. AgShift is expanding this type of objective algorithmic grading to inspect edible nuts, starting with cashews, and is in the process of exploring seafood inspections.
#dl  #applications  #ag  #inspection  #images  #supplychain  >Deere 
16 days ago by phil_hendrix
Mobile Innovation: 5G Use Cases in the Mobile Space Dragon Army 2018
Mobile Innovation: 5G Use Cases in the Mobile Space Published by: Jessica Carruth 0 Mobile innovation was a major theme at CES and Mobile World Congress this year. This comes as no surprise given the impending rise of 5G, which promises to enable a new wave of mobile advancements including mass-scale iOT, low-latency communications and enhanced event and media experiences. Outlined below are a few ways this new wave of innovation will affect the mobile industry: How we got to 5G: A brief history of mobile wireless systems In the 1980’s, the 1st generation of wireless cellular technology (1G) used analog radio signals. This early form of cellular technology was quickly replaced with 2G. 2G used digital radio signals to communicate with radio towers, thus allowing for more efficient wireless connectivity and communications such as SMS and MMS text messaging. 3G allowed for the birth of mobile applications and higher data transfer rates, while 4G allowed for faster interactions and network connectivity. Fast forward to the impending launch of 5G — and you’ll find a few questions on everyone’s minds: 1.) What use cases will 5G allow for? and 2.) How will it change the mobile industry?
#mobile  #5G  #usecases  #applications  #trends  #innovation 
19 days ago by phil_hendrix
Advancing the use of real-world data to help speed treatments to patients OptumLabs 20180212
Advancing the use of real-world data to help speed treatments to patients Created Feb 12 2018, 1:08 PM OptumLabs Real World Evidence RCT Randomized Controlled Trial By William Crown, PhD, OptumLabs For more than 20 years, I used medical claims data to compare the effects of medications on patient outcomes and cost. In the back of my mind (and sometimes the front) I was always worried about what I might be missing since claims data contained limited clinical information. For example, I knew that it would be helpful to know the body mass index, cholesterol levels and blood pressure of patients when studying heart failure drugs. And, when studying breast cancer treatment, it would be critical to control for cancer stage. But none of this information was available in claims data alone. This changed in 2009 with the HiTECH Act, which mandated meaningful use of electronic medical record (EMR) data for health care providers. The HiTECH Act transformed the data landscape almost overnight by making it possible to broaden our view into patient care and outcomes by linking patient-level EMR and claims data. My previous concerns about possibly missing something important turned to curiosity: Could using real-world data such as linked patient claims and EMR records simulate a randomized controlled trial (RCT)? Could important evidence be produced in months, rather than years, to dramatically cut development costs and accelerate patient access to effective therapies? OptumLabs is working to answer these questions.
#hc  #data  #applications  +OptumLabs  +Optum 
4 weeks ago by phil_hendrix
What a Visit to an AI-Enabled Hospital Might Look Like Ray Wang HBR 20161116
The combination of machine learning, deep learning, natural language processing, and cognitive computing will soon change the ways that we interact with our environments. AI-driven smart services will sense what we’re doing, know what our preferences are from our past behavior, and subtly guide us through our daily lives in ways that will feel truly seamless. Perhaps the best way to explore how such systems might work is by looking at an example: a visit to a hospital.
#hc  #ai  #applications  #provider  #casestudy  #exemplar  +RayWang 
4 weeks ago by phil_hendrix
AI in the Enterprise Report Altimeter 201806
AI in the Enterprise Real-World Strategies for Artificial Intelligence New Research from Altimeter By Susan Etlinger, Industry Analyst, Altimeter For enterprise companies considering investing in AI and implementing AI applications, the current landscape can seem overwhelming. Companies like Amazon, Facebook, Google, Apple, and Microsoft dominate the news, but how applicable are their strategies to companies with vastly different business models? This report examines the real use cases, challenges, and opportunities of AI for organizations. It includes interviews with executives from large, well-known companies and start-up entrepreneurs who are envisioning the many ways that machine intelligence can fuel innovation and growth. Finally, the report offers recommendations for companies thinking about where to focus, how to build their partnership ecosystem, and how to measure value in the short and long term as AI becomes a critical driver of digital transformation. In this new report by Susan Etlinger, you will find:  A framework for understanding how you can apply AI within your organization Interviews and in-depth case studies from organizations using AI to fuel innovation and create tangible business value Five specific guiding recommendations for enterprises planning AI implementations
#ai  #applications  #innovation  #enterprise  #deployment  #advice  #analyst  #report  +Altimeter 
4 weeks ago by phil_hendrix
What are the opportunities and challenges of patient-generated health data (PGHD) use? HealthIT.gov 201801
What are the opportunities and challenges of patient-generated health data (PGHD) use? The proliferation of consumer health technologies, including smartphones, mobile applications (apps), and wearable devices, has increased the frequency, amount, and types of PGHD available. These advances can enable patients and their caregivers to independently and seamlessly capture and share their health data electronically with clinicians and researchers more frequently and from any location. Various stakeholder groups can benefit from new opportunities yet also face some challenges arising from increasing PGHD use. A few are described below; more in-depth discussion can be found in the final white paper [PDF - .2.7MB]
#hc  #patient  #data  #PGHD  #benefits  #applications  #privacy 
4 weeks ago by phil_hendrix
Machine Learning in Population Health: Defining “Disease Prediction” Dan Addyson 20180501
Machine Learning in Population Health: Defining “Disease Prediction”
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DISCLAIMER: I am a data analyst at a large US health insurance company. My writing is based on general industry knowledge and experiences, not any specific knowledge or practices that might be used by my own employer or any companies cited in this article. All information provided in this article is non-proprietary and publicly available. My writing is independent and neither endorsed by, nor an endorsement of, any specific organization.

Anticipating disease burden is of great & obvious interest in healthcare. There is a lot of buzz about the ability of AI & ML to “predict disease”. With that said, there is important and non-trivial nuance in what comprises “disease prediction”. These kinds of claims need to be carefully evaluated for their underlying assumptions & analytical proxies.

What is a disease?
A disease is defined in Wikipedia as “…any condition which results in the disorder… of a living organism… not due to any external injury… Disease is often construed as a medical condition associated with specific symptoms and signs.” And this last part which I have bolded, is the nuance that is so important here.

When a clinician makes his or her diagnosis, they rely on a variety of information to support their decision: patient behaviors and symptoms, lab values, radiological tests, etc. The results ultimately get codified into a formal classification system, such as ICD-10. There may be follow-up tests, second and third opinions before a disease is finally confirmed. Rarely is there a single determining factor.
#hc  #analytics  #pophealth  #ml  #applications  #disease  #prediction 
6 weeks ago by phil_hendrix
PayPal acquires AI predictive retail startup Jetlore 20180529
PayPal acquires AI predictive retail startup Jetlore
KYLE WIGGERS@KYLE_L_WIGGERS MAY 29, 2018 2:30 PM

Image Credit: PayPal

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Payments provider PayPal today announced plans to acquire Jetlore, a San Mateo-based startup that specializes in artificial intelligent retail systems, for an undisclosed sum.

Jetlore, which was founded in January 2011 by students from Stanford University’s computer science program, leverages machine learning to deliver “personalized” experiences from retail chains like Uniqlo and Nordstrom Rack. The startup’s predictive technology analyzes “billions” of customer data points and product data, including apparel attributes like sizes, color, fit, and style preferences, as well as brands and preferred materials, which inform its customer targeting products.
#ai  #applications  #retail  #personalization  #impact  +Paypal  #M&A 
6 weeks ago by phil_hendrix
Expert Systems Fuel a New Era of Digital Medicine SapientRazorfish 201805
Information collected from medical devices, combined with consumer-reported data and genomics, is innovating a new era of digital medicine. In this piece from SapientRazorfish, we show healthcare executives how AI capabilities are decoding the origins of disease to cure and prevent illness while delivering breakthrough operational efficiencies.

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Oceans of Data are Fueling Creativity and Innovation Across the Industry
The availability of patient information is exploding, which in turn is fueling the creative minds of IT leaders that collect, index, and store healthcare information. In response, business leaders are collaborating with IT architects, application designers and data scientists to develop their organizations’ analytical skills to deliver breakthroughs in disease management and other healthcare-related processes. The trend began, of course, with the EHR (electronic health record). Now, information is being made more robust by data from personal health devices (such as Apple Watches and Fitbits) and other remote monitoring devices. 

Add insight from genomics (which in terms of sheer volume rivals Twitter, YouTube, and astronomical data1), and you have an environment flush with oceans of data, which can often paralyze an organization. With AI, it’s quite the opposite as volumes of unstructured data sets keep flowing across a wide number of systems. Interest in AI, to correlate information, recognize patterns, and generate actionable insights, are all trends that are explored in a recent comprehensive, in-depth study of the market (which will grow to $6 billion by 20212).
#healthcare  #ai  #applications  +SapientRazorfish 
7 weeks ago by phil_hendrix
Nvidia teaches robots how to mimic human actions with new deep learning process 20180521
Nvidia teaches robots how to mimic human actions with new deep learning process
Robots learn after just one observation period
By Greg Synek on May 21, 2018, 8:10 AM

A team of researchers from Nvidia have developed a new deep learning technique that allows robots to be taught to mimic human actions just by observing how people perform certain tasks. This technology can greatly reduce the amount of time it takes to program robots to perform their desired workloads.

One of the most impressive parts of Nvidia's research is that a human only needs to perform a task one time for the robot to learn how to repeat the actions. A video camera streams a live feed to a pair of neural networks that handle object recognition.

Then, the location data is handed off to another network to manage how the locations of movable objects change over time. A final network plans the robot's movements and attempts to account for any potential interference from the environment. A human-readable list of steps to be taken is generated and allows for a human to manually correct any errors in the process.
#dl  #applications  #advances  #status 
7 weeks ago by phil_hendrix
How Companies Are Putting AI to Work Through Deep Learning O'Reilly 2018
How Companies Are Putting AI to Work Through Deep Learning

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We're at an exciting point with artificial intelligence (AI). Years of research are yielding tangible results, specifically in the area of deep learning. New projects and related technologies are blossoming. Enthusiasm is high.

Yet, the path toward real and practical application of AI and deep learning remains unclear for many organizations. Business and technology leaders are searching for clarity. Where do I start? How can I train my teams to perform this work? How do I avoid the pitfalls?

We conducted this survey to help leaders better understand how organizations are applying AI through deep learning and where they’re encountering the biggest obstacles.
#ai  #dl  #survey  #stats  #status  #applications  #deployment  #O'Reilly 
7 weeks ago by phil_hendrix
Data or Algorithms – Which is More Important? 20171128
Data or Algorithms – Which is More Important?
Posted by William Vorhies on November 28, 2017 at 10:36amView Blog
Summary:  Which is more important, the data or the algorithms?  This chicken and egg question led me to realize that it’s the data, and specifically the way we store and process the data that has dominated data science over the last 10 years.  And it all leads back to Hadoop.

 

Recently I was challenged to speak on the role of data in data science.  This almost sounds like a chicken and egg problem.  How can you have one without the other?  But as I reflected on how to explain this it also struck me that almost everything in the press today is about advances in algorithms.  That’s mostly deep learning and reinforcement learning which are driving our chatbots, image apps, and self-driving cars.

So if you are fairly new to data science, say within the last five or six years you may have missed the fact that it is and was the data, or more specifically how we store and process the data that was the single most important factor in the explosion of data science over the last decade.  In fact there was a single innovation that enabled data lakes, recommenders, IoT, natural language processing, image and video recognition, AI, and reinforcement learning.

Essentially all of these areas of major innovation can be tracked back to the single enabler, NoSQL Hadoop.
#data  #importance  #impact  #datasources  #applications  #history  #analytics  #framework  #A+ 
8 weeks ago by phil_hendrix
Experts challenge digital health developers to show value in an often-hyped field 20180403
Experts challenge digital health developers to show value in an often-hyped field
By ANDREW JOSEPH @DrewQJoseph APRIL 3, 2018
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s new digital tools from wearables to electronic health records to boxes that can track patients’ movements in their homes promise to reshape health care, it is crucial to scrutinize the massive amount of data being generated to ensure that interventions are actually improving health outcomes.

That was a takeaway message from a group of regulatory and drug development experts at a conference held at MIT on Friday focused on how digital health and artificial intelligence are affecting translational medicine.

“The coin of the realm is improving the outcomes for people,” said Dr. Robert Califf, the former Food and Drug Administration commissioner who now splits his time between Duke University and Verily Life Sciences. “Please don’t make claims that aren’t true,” he added, differentiating between the need to sell one’s ideas and overstating what AI can do at this point.
#digitalhealth  #applications  #validation  #testing  #outcomes 
9 weeks ago by phil_hendrix
How Blockchain Will Upend the Customer Experience Customer Strategist 201805
How Blockchain Will Upend the Customer Experience
Bitcoin may have disrupted finance, but blockchain is poised to transform many more industries.
By Judith Aquino

THERE’S A LOT OF HYPE surrounding bitcoin, to put it mildly. What started as an obscure digital form of payment has morphed into a global phenomenon with eye-popping valuations. But some analysts believe bitcoin is just window dressing; the underlying technology—blockchain—is a far more transformative innovation with significant implications for businesses, and by extension, future customer experiences.

But first, let’s discuss bitcoin and its unique characteristics. It’s a digital asset that has been described as a virtual alternative to dollars or gold, but is not controlled by governments or financial institutions. Furthermore, it isn’t subject to the same monetary policies and regulations as traditional currency.

It can be used for direct transactions between individuals, exchanged for dollars, or to pay for purchases. Companies like Overstock.com and Expedia already accept bitcoin as a form of payment. And although each bitcoin transaction is recorded in a public ledger, the names of buyers and sellers are not revealed, which allows purchases to be made anonymously.
#blockchain  #applications  #CX 
9 weeks ago by phil_hendrix
The Rise of the Telcobot Customer Strategist 201805
The Rise of the Telcobot
AT&T, Vodafone, and other telecoms welcome chatbots as the future of the industry.
By Dylan Haviland

ONE OF THE KEY THEMES at this year’s Mobile World Congress was how to apply AI to telecom operations, employee tasks, and customer experience. The focus illustrates that machine learning and automation are critical applications for an industry striving to keep up with innovation.
#conversationalagent  #applications  #casestudies  #vertical  #telco 
9 weeks ago by phil_hendrix
How airlines aim to use big data to boost profits 20180508
How airlines aim to use big data to boost profits
Technology provides carriers with treasure trove of information to optimise customer service

Airlines want to harvest the data of travellers to create services tailored to their needs and preferences © FT montage
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Camilla Hodgson and Patti Waldmeir YESTERDAY Print this page88
“Good afternoon Mr Smith, and congratulations on reaching 500,000 air miles. Lunch will be served an hour after take off — we’re preparing your filet mignon just the way you like it. I’d like to apologise for the slight delay to your flight to Miami last week, and let you know we’re scheduled to arrive on time today.”

Mr Smith no longer needs to be sitting in first class, wearing a Rolex and flashing a gold Amex to qualify for this sort of treatment. Airlines are seeking to harness big data to personalise customer experiences for as many travellers as possible — and they have a lot of data.

“We need to be operating at the level of Google,” said Joe Leader, chief executive of the Airline Passenger Experience Association, in a report ahead of this year’s Aviation Festival.

“For every passenger, we know every detail that serves as a marketing treasure trove of personalisation. We have full names, address, exact birthdays, seat assignments, credit cards and everything that should make personalisation easy,” he added.
#analytics  #applications  #airlines  #impact  #growth 
9 weeks ago by phil_hendrix

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