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A recent study found that two-thirds of health systems have experienced a significant decline in income, with 27% losing money in at least one of the three years studied. The drop in earnings from fiscal year (FY) 2015 to FY 2017 represented $6.8 billion, a startling 44% reduction. There was some improvement in FY 2018, with revenue growth exceeding expenses by 0.1% for the first time since 2015 but growth remains lackluster.

In order for hospitals to advance or maintain standards of care, and to grow in ways that best serve patient needs, they have to get smarter about resource utilization. Operating rooms (ORs) should be a top priority. Not only do ORs represent the majority of a hospitals margin, they are also ripe with ways to increase efficiency and improve capacity. (Read: Top 20 AI Use Cases: Artificial Intelligence in Healthcare)

Multiple factors go into OR utilization: how long procedures are expected to take, which provider is using the room, unforeseen complications that occur during surgeries, last-minute additions or removals to the schedule, and much more. Scheduling ORs is not an exact science, but thanks to advances in data analytics, its getting pretty close.

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New machine learning (ML) solutions can take a variety of data points gleaned from every procedure that happens in every OR in a health system. They analyze this information and make predictions that can be used to effect improvements and adjustments for the future whether its a slight shift in booking times, hiring more staff, or establishing new policies for rooms.

These solutions identify portions of time that can be repurposed, collecting underutilized blocks of time without impacting existing case volume. They also erase countless hours of manual research, conducting queries in mere seconds. (Read: How AI in Healthcare is Identifying Risks and Saving Money.)

Additionally, the insights they yield can save health systems considerable expense, while opening up more treatment opportunities, and thus increase revenue all while improving patient access and the overall patient experience.

Getting the right insights to the right people in OR environments can be an enormous challenge, especially when different groups are looking for answers to different questions. One unified data analytics platform can help, enabling each constituency to find exactly what they need to improve decision-making, which ultimately leads to advancing standards of care, through machine learning.

New machine learning solutions can uncover the various drivers behind high-level trends in volume and utilization across an entire group. For example, from one interface, an executive at a large health group could look at whats happening at a specific hospital or region over a designated time range (such as over the last quarter). The executive might want to generate a holistic view of measures such as OR turnover ratio, add-on ratio, on-time starts, and more.

With this information, it becomes much easier to see how the business of the facility is actually performing. Is it growing? How is its utilization trending?

Based on these results, the executive might want to take a more in-depth look, viewing certain categories year-over-year, grouped by month, in order to compare, spot trends, and get an aggregate historical progression.

Additionally, he could check case volume to look for drivers behind volume increases and which service lines (orthopedics, general surgery, or cardiac care, for example) have experienced the most growth.

Robots are valuable assets, so much so that many groups have committees focused on robots to establish how well they are being used. Robotic program leaders can leverage new data analytics solutions to monitor robot usage and ensure that assets are being fully utilized. With the right insights, committees can figure out if they need to buy additional robots or adjust policies around usage.

Reports can also separate out only robot cases or look at which providers do the most with robots. This helps robotic program leaders know how often ORs equipped with robots are being used for robotics cases only versus other cases, advancing standards of care through machine learning. (Read: What Do Patients Want From Healthcare Technology?)

They also can view specific rooms where robots live by day of the week and hour of the day. Maybe on Mondays a robot is not being used to its maximum potential or a particular provider who isnt using the robot consistently blocks the room. Insights such as these help robotic program leaders figure out where rooms are blocked and why so that they can be leveraged to the fullest.

New solutions can be invaluable to nurse managers as well, giving them the mechanism to make data-driven decisions about staffing across teams and service lines. They can use technology to see historical data (such as room occupancy) to help make future assignments.

Nurse managers might want to view aggregate room data for an entire facility, every hour of each day of the week over the last three months. This way they can spot the busiest (or least busy) times and staff accordingly.

Additionally, a nurse manager may hear from someone on staff that she is always working late. The manager can then run a query to validate if a problem exists by checking times in blocks that are running over and whether certain providers tend to take more time than budgeted. In this instance, the nurse manager could adjust staff hours to keep expectations in line with the likely reality.

These are just some examples of how hospitals are advancing standards of care through machine learning and data analytics, particularly in terms of their ORs. But in order for data to make an impact, it needs to be credible, timely and actionable. If it can be harnessed in this way, insights can change hospitals trajectories.

Data analytics and machine learning unlock the capacity of scarce assets by transforming core processes. Health systems are able to reduce operating costs and potentially defer the need for a facility or staff expansion and most importantly, they can reduce wait time for patients and increase access with greater OR availability. By making operations smarter, the profit-to-expense ratio moves closer to ideal, and organizations can grow efficiently into the future.

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Advancing Standards of Care Through Machine Learning - Techopedia

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.

The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.

But, using the classic algorithms of machine learning, text is considered as a sequence of keywords; instead, an approach based on semantic analysis mimics the human ability to understand the meaning of a text.

Machine learning algorithms are often categorized as supervised or unsupervised.

Machine learning enables analysis of massive quantities of data. While it generally delivers faster, more accurate results in order to identify profitable opportunities or dangerous risks, it may also require additional time and resources to train it properly. Combining machine learning with AI and cognitive technologies can make it even more effective in processing large volumes of information.

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Originally published March 2017, updated May 2020

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What is Machine Learning? A definition - Expert System

Its a startling image that illustrates the deep-rooted biases of AI research. Input a low-resolution picture of Barack Obama, the first black president of the United States, into an algorithm designed to generate depixelated faces, and the output is a white man.

Its not just Obama, either. Get the same algorithm to generate high-resolution images of actress Lucy Liu or congresswoman Alexandria Ocasio-Cortez from low-resolution inputs, and the resulting faces look distinctly white. As one popular tweet quoting the Obama example put it: This image speaks volumes about the dangers of bias in AI.

But whats causing these outputs and what do they really tell us about AI bias?

First, we need to know a little a bit about the technology being used here. The program generating these images is an algorithm called PULSE, which uses a technique known as upscaling to process visual data. Upscaling is like the zoom and enhance tropes you see in TV and film, but, unlike in Hollywood, real software cant just generate new data from nothing. In order to turn a low-resolution image into a high-resolution one, the software has to fill in the blanks using machine learning.

In the case of PULSE, the algorithm doing this work is StyleGAN, which was created by researchers from NVIDIA. Although you might not have heard of StyleGAN before, youre probably familiar with its work. Its the algorithm responsible for making those eerily realistic human faces that you can see on websites like ThisPersonDoesNotExist.com; faces so realistic theyre often used to generate fake social media profiles.

What PULSE does is use StyleGAN to imagine the high-res version of pixelated inputs. It does this not by enhancing the original low-res image, but by generating a completely new high-res face that, when pixelated, looks the same as the one inputted by the user.

This means each depixelated image can be upscaled in a variety of ways, the same way a single set of ingredients makes different dishes. Its also why you can use PULSE to see what Doom guy, or the hero of Wolfenstein 3D, or even the crying emoji look like at high resolution. Its not that the algorithm is finding new detail in the image as in the zoom and enhance trope; its instead inventing new faces that revert to the input data.

This sort of work has been theoretically possible for a few years now, but, as is often the case in the AI world, it reached a larger audience when an easy-to-run version of the code was shared online this weekend. Thats when the racial disparities started to leap out.

PULSEs creators say the trend is clear: when using the algorithm to scale up pixelated images, the algorithm more often generates faces with Caucasian features.

It does appear that PULSE is producing white faces much more frequently than faces of people of color, wrote the algorithms creators on Github. This bias is likely inherited from the dataset StyleGAN was trained on [...] though there could be other factors that we are unaware of.

In other words, because of the data StyleGAN was trained on, when its trying to come up with a face that looks like the pixelated input image, it defaults to white features.

This problem is extremely common in machine learning, and its one of the reasons facial recognition algorithms perform worse on non-white and female faces. Data used to train AI is often skewed toward a single demographic, white men, and when a program sees data not in that demographic it performs poorly. Not coincidentally, its white men who dominate AI research.

But exactly what the Obama example reveals about bias and how the problems it represents might be fixed are complicated questions. Indeed, theyre so complicated that this single image has sparked heated disagreement among AI academics, engineers, and researchers.

On a technical level, some experts arent sure this is even an example of dataset bias. The AI artist Mario Klingemann suggests that the PULSE selection algorithm itself, rather than the data, is to blame. Klingemann notes that he was able to use StyleGAN to generate more non-white outputs from the same pixelated Obama image, as shown below:

These faces were generated using the same concept and the same StyleGAN model but different search methods to Pulse, says Klingemann, who says we cant really judge an algorithm from just a few samples. There are probably millions of possible faces that will all reduce to the same pixel pattern and all of them are equally correct, he told The Verge.

(Incidentally, this is also the reason why tools like this are unlikely to be of use for surveillance purposes. The faces created by these processes are imaginary and, as the above examples show, have little relation to the ground truth of the input. However, its not like huge technical flaws have stopped police from adopting technology in the past.)

But regardless of the cause, the outputs of the algorithm seem biased something that the researchers didnt notice before the tool became widely accessible. This speaks to a different and more pervasive sort of bias: one that operates on a social level.

Deborah Raji, a researcher in AI accountability, tells The Verge that this sort of bias is all too typical in the AI world. Given the basic existence of people of color, the negligence of not testing for this situation is astounding, and likely reflects the lack of diversity we continue to see with respect to who gets to build such systems, says Raji. People of color are not outliers. Were not edge cases authors can just forget.

The fact that some researchers seem keen to only address the data side of the bias problem is what sparked larger arguments about the Obama image. Facebooks chief AI scientist Yann LeCun became a flashpoint for these conversations after tweeting a response to the image saying that ML systems are biased when data is biased, and adding that this sort of bias is a far more serious problem in a deployed product than in an academic paper. The implication being: lets not worry too much about this particular example.

Many researchers, Raji among them, took issue with LeCuns framing, pointing out that bias in AI is affected by wider social injustices and prejudices, and that simply using correct data does not deal with the larger injustices.

Others noted that even from the point of view of a purely technical fix, fair datasets can often be anything but. For example, a dataset of faces that accurately reflected the demographics of the UK would be predominantly white because the UK is predominantly white. An algorithm trained on this data would perform better on white faces than non-white faces. In other words, fair datasets can still created biased systems. (In a later thread on Twitter, LeCun acknowledged there were multiple causes for AI bias.)

Raji tells The Verge she was also surprised by LeCuns suggestion that researchers should worry about bias less than engineers producing commercial systems, and that this reflected a lack of awareness at the very highest levels of the industry.

Yann LeCun leads an industry lab known for working on many applied research problems that they regularly seek to productize, says Raji. I literally cannot understand how someone in that position doesnt acknowledge the role that research has in setting up norms for engineering deployments.

When contacted by The Verge about these comments, LeCun noted that hed helped set up a number of groups, inside and outside of Facebook, that focus on AI fairness and safety, including the Partnership on AI. I absolutely never, ever said or even hinted at the fact that research does not play a role is setting up norms, he told The Verge.

Many commercial AI systems, though, are built directly from research data and algorithms without any adjustment for racial or gender disparities. Failing to address the problem of bias at the research stage just perpetuates existing problems.

In this sense, then, the value of the Obama image isnt that it exposes a single flaw in a single algorithm; its that it communicates, at an intuitive level, the pervasive nature of AI bias. What it hides, however, is that the problem of bias goes far deeper than any dataset or algorithm. Its a pervasive issue that requires much more than technical fixes.

As one researcher, Vidushi Marda, responded on Twitter to the white faces produced by the algorithm: In case it needed to be said explicitly - This isnt a call for diversity in datasets or improved accuracy in performance - its a call for a fundamental reconsideration of the institutions and individuals that design, develop, deploy this tech in the first place.

Update, Wednesday, June 24: This piece has been updated to include additional comment from Yann LeCun.

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What a machine learning tool that turns Obama white can (and cant) tell us about AI bias - The Verge

The recent crisis has increased focus on autonomous robots being used for practical benefit. Weve seen robots cleaning hospitals, delivering food and medicines and even assessing patients. These are all amazing use cases, and clearly illustrate the ways in which robots will play a greater role in our lives from now on.

However, for all their benefits, currently the ability for a robot to autonomously map its surroundings and successfully locate itself is still quite limited. Robots are getting better at doing specific things in planned, consistent environments; but dynamic, untrained situations remain a challenge.

Age of PerceptionWhat excites me is the next generation of SLAM (Simultaneous Localization and Mapping) that will allow robot designers to create robots much more capable of autonomous operation in a broad range of scenarios. It is already under development and attracting investment and interest across the industry.

We are calling it the Age of Perception, and it combines recent advances in machine and deep learning to enhance SLAM. Increasing the richness of maps with semantic scene understanding improves localization, mapping quality and robustness.

Simplifying MapsCurrently, most SLAM solutions take raw data from sensors and use probabilistic algorithms to calculate the location and a map of the surroundings of the robot. LIDAR is most commonly used but increasingly lower-cost cameras are providing rich data streams for enhanced maps. Whatever sensors are used the data creates maps made up of millions of 3-dimensional reference points. These allow the robot to calculate its location.

The problem is that these clouds of 3D points have no meaning they are just a spatial reference for the robot to calculate its position. Constantly processing all of these millions of points is also a heavy load on the robots processors and memory. By inserting machine learning into the processing pipeline we can both improve the utility of these maps and simplify them.

Panoptic SegmentationPanoptic Segmentation techniques use machine learning to categorize collections of pixels from camera feeds into recognizable objects. For example, the millions of pixels representing a wall can be categorized as a single object. In addition, we can use machine learning to predict the geometry and the shape of these pixels in the 3D world. So, millions of 3D points representing a wall can be all summarized into a single plane. Millions of 3D points representing a chair can be all summarized into a shape model with a small number of parameters. Breaking scenes down into distinct objects into 2D and 3D lowers the overhead on processors and memory.

What excites me is the next generation of SLAM that will allow robot designers to create robots much more capable of autonomous operation in a broad range of scenarios. It is already under development and attracting investment and interest across the industry.

Adding UnderstandingAs well as simplification of maps, this approach provides the foundation of greater understanding of the scenes the robots sensors capture. With machine learning we are able to categorize individual objects within the scene and then write code that determines how they should be handled.

The first goal of this emerging capability is to be able to remove moving objects, including people, from maps. In order to navigate effectively, robots need to reference static elements of a scene; things that will not move, and so can be used as a reliable locating point. Machine learning can be used to teach autonomous robots which elements of a scene to use for location, and which to disregard as parts of the map or classify them as obstacles to avoid. Combining the panoptic segmentation of objects in a scene with underlying map and location data will soon deliver massive increases in accuracy and capability of robotic SLAM.

Perceiving ObjectsThe next exciting step will be to build on this categorization to add a level of understanding of individual objects. Machine learning, working as part of the SLAM system, will allow a robot to learn to distinguish the walls and floors of a room from the furniture and other objects within it. Storing these elements as individual objects means that adding or removing a chair will not necessitate the complete redrawing of the map.

This combination of benefits is the key to massive advances in the capability of autonomous robots. Robots do not generalize well in untrained situations; changes, particularly rapid movement, disrupt maps and add significant computational load. Machine learning creates a layer of abstraction that improves the stability of maps. The greater efficiency it allows in processing data creates the overhead to add more sensors and more data that can increase the granularity and information that can be included in maps.

Machine learning can be used to teach autonomous robots which elements of a scene to use for location, and which to disregard as parts of the map or classify them as obstacles to avoid.

Natural InteractionLinking location, mapping and perception will allow robots to understand more about their surroundings and operate in more useful ways. For example, a robot that can perceive the difference between a hall and a kitchen can undertake more complex sets of instructions. Being able to identify and categorize objects such as chairs, desks, cabinets etc will improve this still further. Instructing a robot to go to a specific room to get a specific thing will become much simpler.

The real revolution in robotics will come when robots start interacting more with people in more natural ways. Robots that learn from multiple situations and combine that knowledge into a model that allows them to take on new, un-trained tasks based on maps and objects preserved in memory. Creating those models and abstraction demands complete integration of all three layers of SLAM. Thanks to the efforts of the those who are leading the industry in these areas, I believe that the Age of Perception is just around the corner.

Editors Note: Robotics Business Review would like to thank SLAMcore for permission to reprint the original article (found HERE).

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SLAM + Machine Learning Ushers in the "Age of Perception - Robotics Business Review

Smartphones today have become so powerful that sometimes even mid-range handsets can support some fancy machine learning and AI applications. Most of those, however, still rely on cloud-hosted neural networks, machine learning models, and processing, which has both privacy and efficiency drawbacks. Contrary to what most would expect, Google has been moving to offload much of that machine learning activity from the cloud to the device and its latest machine learning development tool is its latest step in that direction.

Googles machine learning or ML Kit SDK has been around for two years now but it has largely been tied to its Firebase mobile and web development platform. Like many Google products, this creates a dependency on a cloud-platform that entails not just some latency due to network bandwidth but also risks leaking potentially private data in transit.

While Google is still leaving that ML Kit + Firebase combo available, it is now also launching a standalone software development kit or SDK for both Android and iOS app developers that focuses on on-device machine learning. Since everything happens locally, the users privacy is protected and the app can function almost in real-time regardless of the speed of the Internet connection. In fact, an ML-using app can even work offline for that matter.

The implications of this new SDK can be quite significant but it still depends on developers switching from the Firebase version to the standalone SDK. To give them a hand, Google created a code lab that combines the new ML Kit with its CameraX app in order to translate text in real-time without connecting to the Internet.

This can definitely help boost confidence in AI-based apps if the user no longer has to worry about privacy or network problems. Of course, Google would probably prefer that developers keep using the Firebase connection which it even describes as getting the best of both products.

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Googles new ML Kit SDK keeps all machine learning on the device - SlashGear