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December 1, 2019 Fujifilm Medical Systems U.S.A. is showcasing REiLI, the company's global medical imaging and informatics artificial intelligence (AI) technology initiative at the 2019 Radiological Society of North America's (RSNA) annual meeting.

"At RSNA 2019, we look forward to sharing the AI insights and advances we've made by working closely with clinical and research partners for several years," said Takuya Shimomura, chief technology officer and executive director, Fujifilm. "Ultimately, the long-term goal of our AI initiative is to help providers make better decisions that improve patient lives."

Under the REiLI brand, Fujifilm is developing AI technologies that strongly support diagnostic imaging workflow, leveraging the combination of its deep learning innovations and distinct image processing heritage. Applications currently in development include, but are not limited to: Region Recognition, an AI technology that helps to accurately recognize and consistently extract organ regions, regardless of deviations in shape, presence or absence of disease, and imaging conditions; Computer Aided Detection, an AI technology to reduce the time of image interpretation and support radiologists' clinical decision making; Workflow Support, using AI technology to realize optimal study prioritization, alert communications of AI findings, and report population automation.

"Our latest Synapse 7x brings diagnostic radiology, mammography and cardiology together on the server-side, enabling immediate interaction with these modality imaging data sets through a single AI-enabled platform," said Bill Lacy, vice president, medical informatics, Fujifilm. "We're excited to debut this solution for our U.S. customers at RSNA 2019, showing our commitment to progressing AI technology to empower physicians to make more efficient and impactful care decisions."

RSNA attendees are encouraged to learn more about REiLI at Booth #4111 and participate in the following Fujifilm-hosted activities.

At booth #4111, attendees can visit Fujifilm's AI Lab. The lab will feature dedicated workstations demonstrating REiLI use cases within Synapse PACS. Attendees can witness first-hand the speed and depth of the integrated workflows achieved by unifying Fujifilm's REiLI technology with the company's server-side PACS system. Featured in the AI lab will be Fujifilm developed algorithms, to include CT lung nodule, intracerebral hemorrhage, cerebral infarction MR and CT, spine label and bone temporal subtraction to name a few. In addition to the Fujifilm AI development, the AI lab will showcase its strengths by supporting a multitude of integration points in support of partner vendor and provider developed algorithms. This will include Riverain's lung nodule, MaxQ's stroke, Lunit's Chest and 2-D Mammography, LPixel's MR Aneurysm, Koios' US breast, Aidoc's pulmonary embolism and Gleamer's bone fracture.

For more inform rsna.fujimed.com

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Fujifilm Showcases Artificial Intelligence Initiative And Advances at RSNA 2019 - Imaging Technology News

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Yes, artificial intelligence (AI) is proving itself to be a worthwhile tool in the business arena at least in focused, preliminary projects. Intelligent chatbots are a classic example. Now its a question of how quickly it can be expanded to deliver on a wider basis across the business to automate decisions around inventory or investments, for example.

Theres progress on this front, as shown in McKinseys latest survey of 2,360 executives, which shows a nearly 25 percent year-over-year increase in the use of AI in various business processes and there has been a sizable jump in companies spreading AI across multiple processes.

A majority of executives in companies that have adopted AI report that it has increased revenues in areas where it is used, and 44 percent say it has reduced costs, the surveys authors, Arif Cam, Michael Chui, and Bryce Hall, all with McKinsey, state.

The results also show that a small share of companies the authors call them AI high performers are attaining outsize business results from AI. Close to two in three companies, 63 percent, report revenue increases from AI adoption in the business units. Respondents from high performers are nearly three times likelier than their lagging counterparts to report revenue gains of more than 10 percent, the survey shows.

The leading AI use cases include marketing and sales, product and service development, and supply-chain management. In marketing and sales, respondents most often report revenue increases from AI use in pricing, prediction of likelihood to buy, and customer-service analytics, the surveys authors report. In product and service development, revenue-producing use cases include the creation of new AI-based products and new AI-based enhancements. And in supply-chain management, respondents often cite sales and demand forecasting and spend analytics as use cases that generate revenue.

What are these high performers doing differently? Strategy is a key area. For example, 72 percent of respondents from AI high performers say their companies AI strategy aligns with their corporate strategy, compared with 29 percent of respondents from other companies. Similarly, 65 percent from the high performers report having a clear data strategy that supports and enables AI, compared with 20 percent from other companies. Also, the application of standardized tools to be used across the enterprise is more likely to be seen at high performers.

Adoption of Strategic AI Approaches:

Retraining workers is also a key differentiator, the survey shows. One-third of high performers, 33%, indicate the majority of their workforce has received AI-related training over the past year, compared to five percent of lagging organizations. Over the next three years, 42% of high performers intend to extend such training to most of their workers, versus only 17% of their lagging counterparts.

For AI to take hold, the McKinsey authors urge ramping up workforce retraining. Even the AI high performers have work to do in several key areas, the surveys authors point out. Only 36 percent of respondents from these companies say their frontline employees use AI insights in real time for daily decision making. A minority, 42 percent, report they systematically track a comprehensive set of well-defined key performance indicators for AI. Likewise, only 35 percent of respondents from AI high performers report having an active continuous learning program on AI for employees.

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It Pays To Break Artificial Intelligence Out Of The Lab, Study Confirms - Forbes

AI talk: FX Week Europe panellists dont see much use for complex machine learning in FX

Artificial intelligence can be particularly useful in asset classes where there are thousands of instruments available to trade, but it is not deemed as practical in a market such as foreign exchange, where the overall number of currency pairs is limited and even less so in the majors, remarked panellists at the 2019 FX Week Europe conference.

While the panellists did not completely disregard the potential for AI in FX, they did not believe it is as relevant as it is for equities, for example.

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Artificial intelligence in FX 'may be hype' - FX Week

Artificial intelligence (AI) -- the capability of a machine to mimic human thinking and behavior -- is one of the biggest growth trends today.Spending on AI systems will increase by more than two and a half times between 2019 and 2023, from $37.5 billion to $97.9 billion, for a compound annual growth rate of 28.4%,according to estimates by research firm IDC. Other sources are projecting even more torrid growth rates.

There are two broad ways you can get exposure to the AI space:

With this background in mind, let's look at which AI stocks are performing the best so far this year (through Nov. 25) and which one is my choice for best AI stock for 2020.

Image source: Getty Images.

The following chart isn't meant to be all-inclusive, as that would be impossible, and the chart has limits on the number of metrics. Notable among the companies missing areAdvanced Micro Devices and Intel. They were left out largely because NVIDIA is currently the leader in supplying AI chips. While there are things to like about shares of both of these companies, NVIDIA stock is the better play on AI, in my view.

Data by YCharts.

Graphics processing unit (GPU) specialist NVIDIA (NASDAQ:NVDA), e-commerce and cloud computing service titanAmazon, computer software and cloud computer service giant Microsoft, Google parent and cloud computing service provider Alphabet, old technology guard and multifaceted AI player IBM, and Micron Technology, which makes computer memory chips and related storage products, would best be put in the first category above. They produce and sell AI-related products and/or services. They're all also probably using AI internally, with Amazon and Alphabet being notably heavy users of the tech to improve their products.

iPhone makerApple (NASDAQ:AAPL), social media leader Facebook (NASDAQ:FB), video-streaming king Netflix, and Stitch Fix, an online personal styling service provider, would best be categorized in the second group since they're either primarily or solely using AI to improve their products and services.

Now let's look at some basic stats for the three best performers of this group.

Company

Market Cap

P/E(Forward)

Wall Street's 5-Year Estimated Average Annual EPS Growth

5-Year Stock Return

Apple

NVIDIA

Facebook

S&P 500

--

--

Data sources: YCharts (returns) and Yahoo! Finance (all else). P/E = price-to-earnings ratio. EPS = earnings per share. Data as of Nov. 25, 2019.

On a valuation basis alone, Facebook stock looks the most compelling when we take earnings growth estimates into account. Then would come Apple and then NVIDIA. However, there are other factors to consider, with the biggie being that projected earnings growth is just that, projected.

There's a good argument to be made that NVIDIA has a great shot at exceeding analysts' earnings estimates. Why? Because it has a fantastic record of doing so, and all one needs to do is listen to enough quarterly earnings calls with Wall Street analysts to realize why this is so: A fair number of them don't seem to have a strong grasp of the company's operations and products. (I'm not knocking, as most analysts don't have technical backgrounds, and they cover a lot of companies.)

Facebook stock probably has the potential to continue to be a long-term winner. But it's relatively high regulatory risk profile makes it not a good fit for all investors. Moreover, it will likely have to keep spending a ton of money to help prevent "bad actors" from using its site for various nefarious purposes. Indeed, this is one of the major internal functions for which the company is using AI. It also uses the tech to recognize and tag uploaded images, among other things.

Apple uses AI internally in various ways, with the most consumer-facing one being powering its voice assistant Siri. It's the best of these three stocks for more conservative investors, as it has a great long-term track record and pays a modest dividend.NVIDIA, however, is probably the better choice for growth-oriented investors who are comfortable with a moderate risk level.

Image source: Getty Images.

NVIDIA is the leading supplier of graphics cards for computing gaming, with AMD a relatively distant second. In the last several years, it's transformed itself into a major AI player, or more specifically, a force to be reckoned with in the fast-growing deep-learning category of AI. Its GPUs are the gold standard for AI training in data centers, and it's now making inroads into AI inferencing. (Inferencing involves a machine or device applying what it's learned in its training to new data. It can be done in data centers or "at the edge" -- meaning at the location of the machine or device that's collecting the data.)

NVIDIA is in the relatively early stages of profiting from many gigantic growth trends, including AI, esports, driverless vehicles, virtual reality (VR), smart cities, drones, and more. (There is some overlap in these categories, as AI is involved to some degree in most of NVIDIA's products.) There are no pure plays on AI, to my knowledge, but NVIDIA would probably come the closest.

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The Best Artificial Intelligence Stocks of 2019 -- and The Top AI Stock for 2020 - The Motley Fool

close up man hand press on display screen tablet turn on wifi technology for using home automation ... [+] function to looking inside or outside home and property , ai innovation future concept

How are big data and AI changing the insurance industry?originally appeared onQuora:the place to gain and share knowledge, empowering people to learn from others and better understand the world.

AnswerbyPeter Colis, CEO and Co-founder atEthos Life, onQuora:

Big data is shaping the next wave of insurtech technology and has already helped us build better products and experiences. However, the insurance industry as a whole is tasked with striking a careful balance between the desire to keep innovating with the need to remain vigilant about how and when consumer data is used. As available data sources multiply, theres a growing conversation around the types of online data providers use to assess an individuals risk. This becomes even more thorny amidst the backdrop of increasingly strict data sharing and privacy regulations.

Its increasingly common for life insurance companies to use non-traditional sources of data such as credit scores, court documents, and motor vehicle records in assessing risk. To keep this practice ethical and avoid becoming invasive, its on insurance industry to find ways to innovate while still putting public good first.

Beyond the public data sources Ive already listed, fitness trackers and wearables are an emerging source of risk data in the insurance world. While sharing your fitness tracker data could benefit some people applying for life insurance policies (as a way to prove physical health), it also poses the risk of creating inaccurate or biased data pools. The nature of an opt-in program could negatively impact those who choose not to participate, incentivizing giving up ones right to keep that data private. Additionally, the data from those who do opt-in may not provide an honest representation of health status. Studies have shown many fitness trackers haveerror rates of 10 to 20 percent.

Finally, social media data is another contentious topic. Using information derived from peoples social media profiles, like publicly accessible Instagram accounts, is a slippery slope when it comes to assessing risk. Its not a consistent source of client data and it can chip away at consumer trust. Ultimately, the data youd glean is likely not even substantive or actionable enough for quality underwriting.

Insurance will continue to evolve into an increasingly data-driven industry. Already, its how we make informed decisions on anything, from marketing to underwriting. But at the end of the day, we can never lose sight of the main objective: protecting families.

This questionoriginally appeared onQuora- the place to gain and share knowledge, empowering people to learn from others and better understand the world. You can follow Quora onTwitterandFacebook. More questions:

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How Is Artificial Intelligence Changing The Insurance Industry? - Forbes