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Artificial Intelligence for Healthcare Applications Tractica 201808
Artificial Intelligence for Healthcare Applications Medical Image Analysis, Healthcare VDAs, Computational Drug Discovery, Medical Treatment Recommendation, Patient Data Processing, and Other Use Cases: Market Analysis and Forecasts REPORT DETAILS PRICE: Log In to View PAGES: 82 TABLES, CHARTS,      & FIGURES: 85 PUBLICATION DATE: 3Q 2018 Log In to Purchase/Access Report DOWNLOADS Register or Log In to download a free Executive Summary and brochure for this report. PRESS RELEASES Healthcare Artificial Intelligence Software, Hardware, and Services Market to Surpass $34 Billion Worldwide by 2025 Healthcare has undergone a significant transformation over the past several years, moving from an antiquated, paper-based records system to a more efficient and integrated system that often incorporates physician, payer, and patient-generated health data. This explosion of data means there is a strong need and des ire for systems that not only will help extract and organize this information, but will also analyze and even provide insights and recommendations on how best to utilize the data. It is no secret that healthcare is expensive. Controlling and reducing costs is a major driver of many healthcare initiatives, and incorporating artificial intelligence (AI) technology is no exception. Contrary to consumer markets, there is little desire to deploy new technology for technology’s sake; healthcare has many safety and operational issues that prevent the frivolous introduction of technology, which yields little benefit. AI applications generally are designed to address specific, real-world use cases that make the diagnosis, monitoring, and treatment of patients more efficient, accurate, and available to populations around the world. In the context of an industry fueled by these key market drivers, Tractica forecasts that the global market for AI solutions in the healthcare sector will increase from $1 billion in 2017 to more than $34 billion by 2025. This Tractica report focuses on 22 healthcare-focused use cases for artificial intelligence, including an assessment of the market opportunity for AI software, hardware, and services in the healthcare market. The market analysis and forecasts within the study cover industry dynamics in five major world regions and are based on an in-depth assessment of major companies as well as startup-level activity in the healthcare AI space. Revenue forecasts for each use case are segmented by world region, and the study also includes profiles of 22 key industry players.
#hc  #ai  #analystreport  #vendors  #startups  #lists  #A+  #applications  +Tractica 
6 hours ago by phil_hendrix
Ethics and Data Science eBook 20180725
Ethics and Data Science Kindle Edition by Mike Loukides (Author), Hilary Mason (Author), DJ Patil (Author)

As the impact of data science continues to grow on society there is an increased need to discuss how data is appropriately used and how to address misuse. Yet, ethical principles for working with data have been available for decades. The real issue today is how to put those principles into action. With this report, authors Mike Loukides, Hilary Mason, and DJ Patil examine practical ways for making ethical data standards part of your work every day.

To help you consider all of possible ramifications of your work on data projects, this report includes:

A sample checklist that you can adapt for your own procedures
Five framing guidelines (the Five C’s) for building data products: consent, clarity, consistency, control, and consequences
Suggestions for building ethics into your data-driven culture
Now is the time to invest in a deliberate practice of data ethics, for better products, better teams, and better outcomes. Get a copy of this report and learn what it takes to do good data science today.
#datascience  #ethics  #issues  #challenges  #applications  +HilaryMason  #O'Reilly 
3 days ago by phil_hendrix
machineVantage - Tools for Non-Conscious AI
Machine Learning and Artificial Intelligence Applied to Marketing. Trend Spotting The ideation funnel is time-consuming, laborious and dependent on guesswork. We can provide a scientific approach to complement your process. Product Innovation Product design is a n-factor problem. We can help you discover variables that influence Product Design. Digital and Creative Messaging Marketing is performance and analytics driven. We can co-develop a the meta-marketing model with you that is pre-emptive not reactive.
#ai  #marketing  #applications  #vendor  +MachineVantage 
3 days ago by phil_hendrix
Prescription AI Series on AI Applications in Healthcare Quartz
Artificial intelligence and machine learning are already shifting the way we understand, diagnose, and treat disease, and in the near future, will become an integral part of every aspect of healthcare, from diagnosis to treatment to end-of-life care. Prescription: AI explores the promise of artificial intelligence to personalize, democratize, and advance medicine—and interrogates the potential dangers of handing over decision-making to data and machines.
#ai  #hc  #applications  #disease  #detection 
11 days ago by phil_hendrix
Five myths about cognitive technology Tom Davenport Deloitte Insights 20171218
Dispelling five myths about cognitive technology Tom Davenport, David Schatsky December 18, 2017 Dispelling five myths about cognitive technology A survey of “cognitive-aware” executives of large firms helps correct some misconceptions about the adoption of cognitive technologies and its implications. ONE of the most frequently discussed topics in business today is artificial intelligence (AI). Its impacts and implications are mentioned in many press accounts. Some of this information is accurate, of course, but it also includes many myths. It’s still early days for this technology, and there are not many sources of data. We have been researching Deloitte’s work in AI as well as that of market leaders and it’s important to dispel myths whenever possible. In the 2017 State of Cognitive Survey, Deloitte surveyed 250 “cognitive-aware” US executives from large companies. These managers were knowledgeable about AI/cognitive technologies and informed about what their companies were doing with the technology. Their responses about their companies are subjective, but they shed considerable light on the current state of cognitive technology within organizations. Five myths that the respondents dispel are discussed below.
#cognitivecomputing  #applications  #status  #critique  #myths  +TomDavenport 
15 days ago by phil_hendrix
Getting Started With Decision Modeling Inquiry Response IIA 20180521
NQUIRY RESPONSE: GETTING STARTED WITH DECISION MODELING BY IIA EXPERT, MAY 21, 2018 Download the PDF INQUIRY: How does decision modeling factor into organizational design and our analytics efforts? RESPONSE: THE BIG IDEAS: Current state of decision modeling Stateless decision modeling The model comes first CURRENT STATE OF DECISION MODELING Decision modeling came about as a way to understand business rules and decision logic. It describes “How do I do this task?” types of questions. When modeling business processes, there are decisions within those processes that you have to account for. However, to model those decisions using process models typically results in horrible processes. A standard was created to articulate decisions independent of whatever processes may use them. There are three components to the standard: a decomposition technique, a standard format that represents decision tables (tabular logic), and standard definitions of the syntax for executing the logic. Decomposition is a top-down approach that starts with the highest level decision and deconstructs it to its sub-decisions.
#decisionmaking  #applications  #decisionmodeling  +IIA  #usecases  #prioritizing  #framework 
15 days ago by phil_hendrix
AI for Business and IT Leaders Workshop IDC 20181129
The definitive event for business professionals looking to understand the most important developments, strategies, use cases, breakthroughs, and best practices associated with implementing artificial intelligence technology across the enterprise
#ai  #applications  #workshop  #IDC 
18 days ago by phil_hendrix
Sentiment analysis in more than 1,000 web tools MonkeyLearn 20180830
Sentiment analysis in more than 1,000 web tools by Hernán Correa August 30, 2018 · 7 min read Getting your work done involves using different tools and apps. In fact, businesses today use 16 apps on average, up 33% from last year. Say that you work in customer support, most probably your tech stack includes tools for email, support tickets, team communication, NPS surveys, project management, and more. When you have such a diverse set of apps, it can be a struggle to sort through the data; there’s just too much information to process manually. This is why more and more companies are using sentiment analysis in combination with Zapier to automate workflows and get insights from their data. This combo makes it super easy and straightforward to analyze data at scale, no matter what apps you use, with zero lines of code. With this in mind, we’ve created the following step-by-step guide to show how you can use Zapier and MonkeyLearn to do sentiment analysis in more than 1,000 web tools. Let’s get started!
#analytics  #text  #NLP  #sentiment  #applications  #advice  %Zapier  +MonkeyLearn 
20 days ago by phil_hendrix
What Executives Should be Asking about AI Use-Cases in Business Podcast 20180820
What Executives Should be Asking about AI Use-Cases in Business Last updated on August 20, 2018 by Pamela Bump When contemplating a new venture into AI or machine learning, companies need to take on a number of important considerations that relate to talent, existing data, and limitations. One way executives can judge how successful or appropriate and AI project would be for their company is to examine use cases of businesses that have previously done something similar. With AI and machine learning news increasing in tech media, a business leader may find it challenging to cut through the hype and identify valid, useful case studies. We talked to Ben Lorica, PhD,  Chief Data Scientist at O’Reilly Media, to get his insights on what key details executives should be looking for within a case study.
#ai  #enterprise  #deployment  #applications  #advice  #catalysts 
4 weeks ago by phil_hendrix
Hitachi to launch AI analysis of hospital leftovers to hasten inpatient recovery The Japan Times 20180820
Hitachi to launch AI analysis of hospital leftovers to hasten inpatient recovery KYODO AUG 20, 2018 ARTICLE HISTORY PRINT SHARE A unit of Hitachi Ltd. will launch a new venture that uses artificial intelligence to analyze the leftovers of carefully planned hospital meals to ensure patients get the nutrients needed to better facilitate recovery, company officials said Monday. While AI is already being used to calculate calories and nutrients in meals from images taken before people eat, using AI to analyze leftovers from images is rare, according to officials at Hitachi Solutions Create Ltd., which is set to start the business by the end of March 2019.
#ai  #applications  #hc  #patients  #diet  #scanning 
4 weeks ago by phil_hendrix
Managing human resources is about to become easier Economist 20180331
Managing human resources is about to become easier AI is changing the way firms screen, hire and manage their talent Print edition | Special report Mar 31st 2018 HUMAN RESOURCES (HR) is a poorly named department. It usually has few resources other than overworked staff, clunky technology and piles of employee handbooks. Hassled recruiters have to sort through reams of applications that vastly outnumber the jobs available. For example, Johnson & Johnson (J&J), a consumer-goods company, receives 1.2m applications for 25,000 positions every year. AI-enabled systems can scan applications far more quickly than humans and work out whether candidates are a good fit. Oddly enough, they may also inject more humanity into hiring. According to Athena Karp of HiredScore, a startup that uses algorithms to screen candidates for J&J and others, only around 15-20% of applicants typically hold the right qualifications for a job, but they are rarely told why they were not hired, nor are they pointed to more suitable jobs. Technology is helping “give respect back to candidates”, she says.
#ai  #applications  #hr  #hiring  #casestudies  #startups 
5 weeks ago by phil_hendrix
Customer service could start living up to its name Economist 20180331
Customer service could start living up to its name How AI can make businesses look more caring Print edition | Special report Mar 31st 2018 “YOUR CALL IS important to us,” a recorded voice tells resigned customers as they wait endlessly to speak to a human agent. AI is starting to help companies improve the quality and consistency of their service in order to persuade customers that they do in fact care about them. Ocado, a British online grocer, receives around 10,000 e-mails from customers every day and uses AI to detect the prevailing sentiment in them. It now replies to the most urgent ones first, and is planning to route complaints to agents with expertise in the relevant field. “Like other applications of AI, it’s about trying to make humans more efficient, not take them out of the process entirely,” says Paul Clarke, Ocado’s chief technology officer. Between 2017 and 2021 the share of customer-service interactions worldwide handled entirely by AI will rise fivefold, to 15%, and by 2019 at least 40% of such interactions will involve an element of AI, according to Gartner, a research firm.
#ai  #applications  #customerservice  #contactcenter  #casestudies 
5 weeks ago by phil_hendrix
How AI is spreading throughout the supply chain Economist 20180331
How AI is spreading throughout the supply chain AI is making companies swifter, cleverer and leaner Print edition | Special report Mar 31st 2018 DELIVERING 25 PACKAGES by lorry or van might seem straightforward enough, but it is devilishly complex. The number of possible routes adds up to around 15 septillion (trillion trillion), according to Goldman Sachs, an investment bank. Integrating AI into the complex web of production and distribution—the supply chain—will have a bigger economic impact than any other application of the technology and affect a larger number of businesses, says Sudhir Jha of Infosys, a large IT company. McKinsey estimates that firms will derive between $1.3trn and $2trn a year in economic value from using AI in supply chains and manufacturing (see chart). Many firms are already using robots powered by machine learning to improve the running of their factories and warehouses. But AI will transform several other aspects of supply chains as well.
#ai  #applications  #casestudies  #supplychain 
5 weeks ago by phil_hendrix
AI in the Enterprise Susan Etlinger Altimeter 2018
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  #enterprise  #deployment  #advice  #cases  #applications  +Altimeter 
7 weeks ago by phil_hendrix
Machine learning: What developers and business analysts need to know InfoWorld 20180307
Machine learning: What developers and business analysts need to know There is more to a successful application of machine learning than data science.
Machine learning is undergoing a revolution because of new technologies and methods. Machine learning is a process of using a program to develop capabilities—like the ability to tell spam from desirable email—by analyzing data instead of programming the exact steps, freeing the user from needing to make every decision about how the algorithm functions. Machine learning is a powerful tool, not only because over a million people focus on tedious programming steps every day, but also because it sometimes finds better solutions than humans engaged in manual effort.
#ml  #applications  #deployment  #advice  #explainer 
8 weeks ago by phil_hendrix
Computerworld - Artificial Intelligence
Artificial Intelligence Artificial Intelligence | News, how-tos, features, reviews, and videos
#ai  #newsource  #cases  #applications  #enterprise 
8 weeks ago by phil_hendrix
Artificial intelligence in healthcare: past, present and future Fei Jiang et al 2017
Artificial intelligence in healthcare: past, present and future Fei Jiang1, Yong Jiang2, Hui Zhi3, Yi Dong4, Hao Li5, Sufeng Ma6, Yilong Wang7, Qiang Dong4, Haipeng Shen8, Yongjun Wang9 Author affiliations Abstract Artificial intelligence (AI) aims to mimic human cognitive functions. It is bringing a paradigm shift to healthcare, powered by increasing availability of healthcare data and rapid progress of analytics techniques. We survey the current status of AI applications in healthcare and discuss its future. AI can be applied to various types of healthcare data (structured and unstructured). Popular AI techniques include machine learning methods for structured data, such as the classical support vector machine and neural network, and the modern deep learning, as well as natural language processing for unstructured data. Major disease areas that use AI tools include cancer, neurology and cardiology. We then review in more details the AI applications in stroke, in the three major areas of early detection and diagnosis, treatment, as well as outcome prediction and prognosis evaluation. We conclude with discussion about pioneer AI systems, such as IBM Watson, and hurdles for real-life deployment of AI. This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
#hc  #ai  #applications  #explainer  #status  #outlook  #academic  #survey  #A+ 
9 weeks ago by phil_hendrix
Health Insurers Are Vacuuming Up Consumer Data That Could Be Used to Raise Rates | HealthLeaders Media 20180717

Without any public scrutiny, insurers and data brokers are predicting health costs based on data about things like race, marital status, how much TV consumers watch, whether they pay their bills on time or even buy plus-size clothing.This article was first co-published by ProPublica and NPR on July 17, 2018.By Marshall AllenTo an outsider, the fancy booths at last month's health insurance industry gathering in San Diego aren't very compelling. A handful of companies pitching "lifestyle" data and salespeople touting jargony phrases like "social determinants of health."But dig deeper and the implications of what they're selling might give many patients pause: A future in which everything you do — the things you buy, the food you eat, the time you spend watching TV — may help determine how much you pay for health insurance.
#hc  #data  #thirdparty  #privacy  #insurers  #uses  #concerns  #issues  #applications 
9 weeks ago by phil_hendrix
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 
10 weeks ago by phil_hendrix

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