AI News, 15 Machine Learning In Healthcare Examples To Know

Machine Learning for Medical Diagnostics – 4 Current Applications

Medical diagnostics are a category of medical tests designed to detect infections, conditions and diseases.

We will present our findings in three main sections: Our own recent healthcare AI industry research suggests that around a third of all healthcare AI SaaS companies are focusing partly or exclusively on diagnostics –

In fact, researchers attribute the cause of diagnostics errors to a variety of factors including: To provide additional context, a review of 25 years of malpractice claims payouts in the U.S. by Johns Hopkins researchers showed that diagnostic error claims had a higher occurrence in outpatient (68.8 percent) vs.

With the in vitro diagnostics market size projected to reach $76 billion by 2023, the aging population, prevalence of chronic diseases and the emergence of personalized medicine are contributing factors to the IVD market size.

Building on its 150,000 registered users who currently pay £7.99 ($11.40) per month to access Babylon’s flagship one-on-one doctor video consultations from a pool of 100 doctors (available 12 hours per day, 6 days a week) the chatbot is projected to run about £4.99 ($7.10) per month.

Aside from reviews published on the startup’s website and those posted by users on sites where the app is available for download, specific data on the impact of Ada’s platform on patient outcomes or an understanding of its funding model are not readily available.

Some of our past AI podcast guests have had guesses about what the healthcare chat interface experience might be like: “Five years down the road I wouldn’t be surprised if before you see a doctor, you talk to a chatbot…and become an informed patient before.” –

The algorithm was trained to detect skin cancer or melanoma using “130,000 images of skin lesions representing over 2,000 different diseases.” In the U.S., there are approximately 5.4 million new skin cancer diagnoses each year and early detection is critical for a greater rate of survival.

Stanford’s deep learning algorithm was tested against 21 board-certified dermatologists who reviewed a reported 370 images and were asked if “they would proceed with biopsy or treatment, or reassure the patient” based on each image.

In an effort to improve speed and accuracy of diagnoses, a team of researchers hailing from Beth Israel Deaconess Medical Centre and Harvard Medical School have used deep learning to train an algorithm capable of integrating multiple speech recognition and image recognition to diagnose tumors.

At about 6:30 into the video, there is a brief demonstration matching a child’s face to various syndromes that the child may have based on facial analysis: Machine vision is emerging as a common thread across these diagnostic applications, and it should be noted that improvements in that field will correlate closely with reliable applications in diagnostics.

With projected rapid growth in the medical device sector, companies making efforts to bring accurate and reliable medical diagnostics based on machine and deep learning applications to market may be poised to capture a percentage of this profitable market (the huge venture investments the healthcare AI sector would seem to suggest that AI stands a chance to make a dent in the next wave of medical diagnostic tech).

While there is great promise, AI in medical diagnostics is still a relatively new approach, with many clinicians still left to be convinced of its reliability, sensitivity and how it will be practically integrated into clinical practice without undermining clinical expertise.

     “The [healthcare teams] that are most exciting are the ones that include both health care professionals and AI professionals, because in general the AI people show up saying “as soon as someone gives us lots of data we’re going to find all the secrets”, and then the healthcare professionals don’t really know what’s possible — when you get them together you start to see some compelling applications…” –

How AI and Machine Learning Are Revolutionizing Healthcare

To understand the basic premise of artificial intelligence (AI) and machine learning (ML) in healthcare, you have to understand how the human brain works.

For example, we take wine for granted, but it never occurs to most people that making wine is not a simple process.

The aforementioned fits perfectly into the realm of healthcare, as it requires an enormous amount of accumulated knowledge due to the complexity of the subject matter and the systems in question.

However, what’s important is that the backlog of data available for analysis is astonishing, and it deals with the same framework (the human body).

And this same information is why the healthcare industry sees a wide variety of AI and machine learning applications being developed all the time.

It’s mining millions of healthcare records to learn and build predictive models around specific diseases and health conditions.

From cancer research to diabetic retinopathy – disease propensities will soon be easy to tackle, with the proliferation of AI and machine learning in healthcare.

And this is why R is also popular for traditional machine learning algorithms in healthcare among data scientists, who happen to pioneer the ML landscape, as ML makes their job a lot easier.

Any of these specific levels of organic life could be potentially subjected to a dynamic machine learning approach in healthcare analysis and the use of specific research tools.

Radiology is a good example of a healthcare application, where AI is progressing at a steady pace, with computer programs getting better at CT scans and other imaging applications.

InData Labs provides custom services and solutions in the field of healthcare and serves organizations of various sizes and business objectives.

a mobile period tracker capable of making accurate predictions for women facing the challenge of irregular periods.

Our business partner opted for using deep learning in healthcare as the best available option to process user information stored in the database and make AI inference.

There was enough personal data, including cycle history, ovulation, pregnancy test results, age, height, weight, lifestyle, statistics about sleep, activity, and nutrition.

It took us plenty of project time spent on feature engineering to ensure that the input for model training will be just right to yield outstanding results.

Designing such a type of business intelligence (BI) solution required from our team to dive into working over complicated data migration, data analysis, and data visualization issues.

Health institutions want to cut costs by lowering readmission rates, and insurance companies want to optimize their risk management techniques, while pharmacological companies want to cure viruses.

Everyday Examples of Artificial Intelligence and Machine Learning

With all the excitement and hype about AI that’s “just around the corner”—self-driving cars, instant machine translation, etc.—it can be difficult to see how AI is affecting the lives of regular people from moment to moment. What are examples of artificial intelligence that you’re already using—right now?

You’ve also likely used AI on your way to work, communicating online with friends, searching on the web, and making online purchases.  We distinguish between AI and machine learning (ML) throughout this article when appropriate.

According to a 2015 report by the Texas Transportation Institute at Texas A&M University, commute times in the US have been steadily climbing year-over-year, resulting in 42 hours of rush-hour traffic delay per commuter in 2014—more than a full work week per year, with an estimated $160 billion in lost productivity.

driving to a train station, riding the train to the optimal stop, and then walking or using a ride-share service from that stop to the final destination), not to mention the expected and the unexpected: construction;

Engineering Lead for Uber ATC  Jeff Schneider discussed in an NPR interview how the company uses ML to predict rider demand to ensure that “surge pricing”(short periods of sharp price increases to decrease rider demand and increase driver supply) will soon no longer be necessary.

Glimpse into the future In the future, AI will shorten your commute even further via self-driving cars that result in up to 90% fewer accidents, more efficient ride sharing to  reduce the number of cars on the road by up to 75%, and smart traffic lights that reduce wait times by 40% and overall travel time by 26% in a pilot study.

“filter out messages with the words ‘online pharmacy’ and ‘Nigerian prince’ that come from unknown addresses”) aren’t effective against spam, because spammers can quickly update their messages to work around them.

In a research paper titled, “The Learning Behind Gmail Priority Inbox”, Google outlines its machine learning approach and notes “a huge variation between user preferences for volume of important mail…Thus, we need some manual intervention from users to tune their threshold.

The researchers tested the effectiveness of Priority Inbox on Google employees and found that those with Priority Inbox “spent 6% less time reading email overall, and 13% less time reading unimportant email.” Glimpse into the future Can your inbox reply to emails for you?

Smart reply uses machine learning to automatically suggest three different brief (but customized) responses to answer the email. As of early 2016, 10% of mobile Inbox users’ emails were sent via smart reply.

A brute force search comparing every string of text to every other string of text in a document database will have a high accuracy, but be far too computationally expensive to use in practice. One MIT paper highlights the possibility of using machine learning to optimize this algorithm.

– Credit Decisions Whenever you apply for a loan or credit card, the financial institution must quickly determine whether to accept your application and if so, what specific terms (interest rate, credit line amount, etc.) to offer. FICO uses ML both in developing your FICO score, which most banks use to make credit decisions, and in determining the specific risk assessment for individual customers.

In early 2016, Wealthfront announced it was taking an AI-first approach, promising “an advice engine rooted in artificial intelligence and modern APIs, an engine that we believe will deliver more relevant and personalized advice than ever before.” While there is no data on the long-term performance of robo-advisors (Betterment was founded in 2008, Wealthfront in 2011), they will become the norm for regular people looking to invest their savings.

In a short video highlighting their AI research (below), Facebook discusses the use of artificial neural networks—ML algorithms that mimic the structure of the human brain—to power facial recognition software.

In June 2016, Facebook announced a new AI initiative: DeepText, a text understanding engine that, the company claims “can understand with near-human accuracy the textual content of several thousand posts per second, spanning more than 20 languages.” DeepText is used in Facebook Messenger to detect intent—for instance, by allowing you to hail an Uber from within the app when you message “I need a ride” but not when you say, “I like to ride donkeys.” DeepText is also used for automating the removal of spam, helping popular public figures sort through the millions of comments on their posts to see those most relevant, identify for sale posts automatically and extract relevant information, and identify and surface content in which you might be interested.

– Pinterest Pinterest uses computer vision, an application of AI where computers are taught to “see,” in order to automatically identify objects in images (or “pins”) and then recommend visually similar pins. Other applications of machine learning at Pinterest include spam prevention, search and discovery, ad performance and monetization, and email marketing.

– Instagram Instagram, which Facebook acquired in 2012, uses machine learning to identify the contextual meaning of emoji, which have been steadily replacing slang (for instance, a laughing emoji could replace “lol”).

This may seem like a trivial application of AI, but Instagram has seen a massive increase in emoji use among all demographics, and being able to interpret and analyze it at large scale via this emoji-to-text translation sets the basis for further analysis on how people use Instagram.

A few months later, it opened its messenger platform to developers, allowing anyone to build a chatbot and integrate’s bot training capability to more easily create conversational bots.

–Recommendations You see recommendations for products you’re interested in as “customers who viewed this item also viewed” and  “customers who bought this item also bought”, as well as via personalized recommendations on the home page,  bottom of item pages, and through email.

While Amazon doesn’t reveal what proportion of its sales come from recommendations, research has shown that recommenders increase sales (in this linked study, by 5.9%, but in other studies recommenders have shown up to a 30% increase in sales) and that a product recommendation carries the same sales weight as a two-star increase in average rating (on a five-star scale).

Square, a credit card processor popular among small businesses, charges 2.75% for card-present transactions, compared to 3.5% + 15 cents for card-absent transactions.

By utilizing AI that can learn your purchasing habits, credit card processors minimize the probability of falsely declining your card while maximizing the probability of preventing somebody else from fraudulently charging it.

We may soon see retailers take it one step further and design your entire experience individually for you. Google already does this with search, even with users who are logged out, so this is well within the realm of possibility for retailers.

however, a month later Amazon’s press release boasted a 9x increase in Echo family sales over the previous year’s holiday sales, suggesting that 5 million sold is a significant underestimate.

For example, casual chess players regularly use AI powered chess engines to analyze their games and practice tactics, and  bloggers often use mailing-list services that use ML to optimize reader engagement and open-rates.

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