artificial intelligence (AI)

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The Microsoft Empire Strikes Back: Makes Major Inroads into Healthcare

It seems deeply ironic that a week after I wrote about how even giant companies eventually get surpassed, I'm writing about the resurgence of one such giant, Microsoft. Last week Microsoft won back the title of world's most valuable company (as measured by market cap), passing Apple. Apple had that distinction since 2012; Microsoft hasn't had it since 2002. Admittedly, Microsoft was only able to pass Apple because a recent tech stock downturn dropped Apple from its record trillion-dollar valuation, and, as of this writing, Apple has pulled back in front again, but the fact that it is a race again says a lot about Microsoft.

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The Most Important Health Care Jobs of the Future

Fast Company ran an interesting article The Most Important Design Jobs of the Future, predicting 18 of the most important design jobs of the future (at least 3 to 5 years out).  A couple of them were in health care, and arguably all of them would have some impact on health care, but I thought it might be fun to do a similar list specific to health care, and not limited to design. Let's hope no one comes back in a few years to show how wrong I was. I'll skip the usual suspects -- e.g., doctors, nurses, pharmacists.  Yes, those jobs will (almost) certainly still be around, but they may not be central as they are today.  And those jobs will evolve in ways that reflect the trends illustrated by the jobs I list...

The New Rules of Healthcare Platforms (Part 2): Pipe Scale vs. Platform Scale

Platform businesses scale differently than traditional businesses. Platforms scale through network effects. In the previous post, we introduced and described a widely used metaphor: pipes vs. platforms. Traditional businesses are pipes. Their value chains are linear. Value is added at sequential stages before a final product or service is delivered to consumers at the end of the pipeline. Platforms do not produce goods or services themselves—they make connections among stakeholders and facilitate value exchange among those stakeholders. Value is created outside the platform. Both pipeline businesses and platform businesses strive to achieve scale—but the type of scale they strive for is vastly different. In this post, we’ll explain how pipeline businesses strive for economies of scale (on the supply side) and how platform businesses scale through network effects (on the demand side).

The Next Generation of EHRs Will Be Fundamentally Different

Robert Rowley | CIO | March 29, 2017

Electronic Health Records (EHRs) have come a long way. Over 80 percent of physicians use them in their offices, and nearly all hospitals have implemented EHRs as well. Spurred by the HITECH portion of the 2009 Recovery act, Meaningful Use has put money on the table for physicians and hospitals to adopt and use EHRs. It also defined what kinds of features an EHR must have in order to be Certified. Legacy systems took on these new requirements by adding to their offerings (sometimes referred to as “bolt-on solutions”)...

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The Renaissance Continues for Open Source Artificial Intelligence

Sam Dean | Ostatic | November 10, 2016

Recently, in an article for TechCrunch, Spark Capital's John Melas-Kyriazi weighed in on how startups can leverage artificial intelligence to advance their businesses or even give birth to brand new ones. As a corollary avenue on that topic, it's worth noting that some very powerful artificial intelligence engines have recently been open sourced. Quite a few of them have been tested and hardened at Google, Facebook, Microsoft and other companies, and some of them may represent business opportunities...

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The Robots Are Coming...to Healthcare!

Ready or not, there are robots in your future.  And some of them will be for health care. There has been growing concern that the rise of robots, along with artificial intelligence (AI), will create huge impacts on jobs.  Within the last few months both McKinsey and PwC have issued white papers on the topic.  The former found that nearly half of jobs have the potential to be automated (although most not totally), while the latter expects 38% of U.S. jobs at at high risk of automation within 20 years. Health care is not high on most lists of sectors whose jobs are soonest to be heavily impacted by robots, but it is on the list -- and it will happen...

The State Of Artificial Intelligence

Kathryn Sadasivan | FedScoop | June 25, 2013

In recent years, the U.S. military has increased its focus on artificial intelligence to enhance war-fighting capabilities, shore up mission critical programs and even support mental health work. Today, FedScoop brings you a closer look at just a few of these fascinating AI programs and what they bring to the federal government table. Read More »

This Actually Is a Test

When it comes to health care, testing is not what it used to be, or what it is going to be in the not-too-distant future. For example, confirmation of a cancer diagnosis is getting much easier.  The New York Times reported that blood tests -- known as "liquid biopsies" -- have now been shown to generally match the results of a tumor biopsy.  The blood tests look for DNA fragments from the tumor that signal its presence.  The liquid biopsies are useful for both detecting the presence of a tumor and its ongoing monitoring. The current generation of tests are not perfect, with as many as 15% of tumors not generating enough DNA to be detected, but they do offer the advantage of not requiring an invasive procedure...

To Err Is Human, To Diagnose Artificial Intelligence is...?

A new study found that physicians have a surprisingly poor knowledge of the benefits and harms of common medical treatments.  Almost 80% overestimated the benefits, and two-thirds overestimated the harms.  And, as Aaron Carroll pointed out, it's not just that they were off, but "it's how off they often were." Anyone out there who still doesn't think artificial intelligence (AI) is needed in health care? The authors noted that previous studies have found that patients often overestimate benefits as well, but tended to minimize potential harms.  Not only do physicians overestimate harm, they "underestimate how often most treatments have no effects on patients -- either harmful or beneficial"...

To Trust Artificial Intelligence, It Must Be Open And Transparent. Period.

Machine learning has been around for a long time. But in late 2022, recent advancements in deep learning and large language models started to change the game and come into the public eye. And people started thinking, “We love Open Source software, so, let’s have Open Source AI, too.” But what is Open Source AI? And the answer is: we don’t know yet. Machine learning models are not software. Software is written by humans, like me. Machine learning models are trained; they learn on their own automatically, based on the input data provided by humans. When programmers want to fix a computer program, they know what they need: the source code. But if you want to fix a model, you need a lot more: software to train it, data to train it, a plan for training it, and so forth. It is much more complex. And reproducing it exactly ranges from difficult to nearly impossible.

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Top 8 Open Source Artificial Intelligence (AI) Technologies in Machine Learning

Artificial intelligence (AI) technologies are quickly transforming almost every sphere of our lives. From how we communicate to the means we use for transportation, we seem to be getting increasingly addicted to them. Because of these rapid advancements, massive amounts of talent and resources are dedicated to accelerating the growth of the technologies. Here is a list of 8 best open source AI technologies you can use to take your machine learning projects to the next level.

Top Open Source Projects to Watch in 2017

No one has a crystal ball to see the future of technology. Even for projects developed out in the open, code alone can't tell us whether or not a project is destined for success—but there are hints along the way. For example, perhaps it's not unreasonable to assume that the projects that will help shape our future are those projects that have first seen rapid growth and popularity among the developer community. So which new projects should an open source developer watch in 2017? Let's take a look at a few projects that emerged in 2016 to achieve rapid notoriety in the GitHub community...

Transforming Health Care Through A 360-Degree View Of Data

How medical care can be substantially improved through a full spectrum view of all factors that affect health was the topic of Payam Etminani's presentation at the 2019 IDGA Veterans Benefits Conference in Washington D.C. Etminani, the CEO of Bitscopic, argued that the ability to view all health data including social, environmental and genomic information in addition to the traditional clinical measures (vital signs, blood work, history of illness etc), would lead to significant improvement in care. Etminani described how recent advances in Big Data and Artificial Intelligence (AI) make combining and using these large and widely varied sets of information possible. Read More »

University of Chicago Awarded $20 Million To Host COVID-19 Medical Imaging Center

Press Release | University of Chicago | August 7, 2020

A new center hosted at the University of Chicago-co-led by the largest medical imaging professional organizations in the country-will help tackle the ongoing COVID-19 pandemic by curating a massive database of medical images to help better understand and treat the disease. Led by Prof. Maryellen Giger of UChicago Medicine, the Medical Imaging and Data Resource Center (MIDRC) will create an open-source database with medical images from thousands of COVID-19 patients. The center will be funded by a two-year, $20 million contract from the National Institute of Biomedical Imaging and Bioengineering at the National Institutes of Health (NIH).

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Using Open Technology To Build a Biodefense Against the Coronavirus

As the number of US cases of the coronavirus rises, how will healthcare professionals be able to tell the difference between which panicked patients with similar symptoms has what? Even if the patient hasn't traveled to Wuhan or China recently, what if they sat at a Starbucks with someone who did? With the incubation time-lag before symptoms appear, who would even know? The challenge of monitoring 330 million people for infectious disease outbreaks is daunting. Take the flu as an example. During the last flu season which, as already discussed, was not as complex as this year's season, approximately 35.5 million Americans had flu symptoms, 16.5 million received medical care, 490,600 were hospitalized and 34,200 died.

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