By AI Trends Staff
The Internet of Medical Things (IoMT) market is expanding rapidly, with over 500,000 medical technologies currently available, from blood pressure and glucose monitors to MRI scanners. AI poised to contribute analysis crucial to innovations such as smart hospitals.
Today’s internet-connected devices aim to improve efficiencies, lower care costs and drive better outcomes in healthcare, according to a recent account in HealthTech Magazine. Devices in the IoMT domain extend to wearable external medical devices such as skin patches and insulin pumps; implanted medical devices such as pacemakers and cardioverter defibrillators; and stationary devices such as for home monitoring and connecting imaging machines.
Projections for IoMT market size were aggressive before the COVID-19 pandemic hit, with Deloitte sizing the market at $158.1 billion by 2022, with the connected medical device segment expected to take up to $52.2 billion of that by 2022.
Now the estimates are growing. The global IoMT market was valued at $44.5 billion in 2018 and is expected to grow to $254.2 billion in 2026, according to AllTheResearch. The smart wearable device segment of IoMT, inclusive of smartwatches and sensor-laden smart shirts, made up for the largest share of the global market in 2018, at roughly 27 percent, the report found.
This area of IoMT is poised for even further growth as artificial intelligence is integrated into connected devices and can prove capable of real-time, remote measurement and analysis of patient data.
Fitbit Trackers Found to Help Patients with Heart Disease
Evidence is coming in on the effectiveness of IoMT for health care. A study conducted by researchers from Cedars-Sinai Medical Center and UCLA found that Fitbit activity trackers were able to more accurately evaluate patients with ischemic heart disease by recording their heart rate and accelerometer data simultaneously. Some 88% of healthcare providers were found in a survey last year of 100 health IT leaders by Spyglass Consulting Group, to be investing in remote patient monitoring (RPM) equipment. This is especially true for patients whose conditions are considered unstable and at risk for hospital admission.
Cost avoidance was the primary investment driver for RPM solutions, which are hoping to achieve reduced hospital readmissions, emergency department visits, and overall healthcare utilization, the study stated.
Wearable activity trackers have also proven to be a more reliable measure of physical activity and assessing five-year risk than traditional methods, according to a study by Johns Hopkins Medicine, as reported in mHealthIntelligence.
Adult participants between 50 and 85 years old wore an accelerator device at the hip for seven consecutive days to gather information on their physical activity. Individual data came from responses to demographic, socioeconomic, and health-related survey questions, along with medical records and clinical laboratory test results.
IoMT Devices Seen as Helping to Control Health Care Costs
Medical cost reductions of $300 billion are being estimated by Goldman Sachs, through remote patient monitoring and increased oversight of medication use. Startup activity is picking up. Proteus Discover, for example, has focused its smart pill capabilities on measuring the effectiveness of medication treatment; and HQ’s CorTemp is using its smart pills to monitor patients’ internal health and transmit wireless data such as core temperatures, which can be critical in life or death situations.
AI systems are seen as able to reduce therapeutic and therapeutic errors in human clinical practice, according to an account in IDST. Developing IoMT strategies that match sophisticated sensors with AI-backed analytics will be critical for developing smart hospitals of the future. “Sensors, AI and big data analytics are vital technologies for IoMT as they provide multiple benefits to patients and facilities alike,” stated Varun Babu, senior research analyst with Frost & Sullivan TechVision Research, which studies emerging technology for IT.
The rise of AI and its alliance with IoT is one of the critical aspects of the digital transformation in modern healthcare, according to an account in IoTforAll. The central pairing is likely to result in speeding up the complicated procedures and data functionalities that are otherwise tedious and time-consuming. AI along with sensor technologies from IoT can lead to better decision-making. Advances in connectivity through AI are expected to promote an understanding of therapy and enable preventive care that promises a better future.
The impact of AI on personal healthcare is attracting wide comment. “AI is transforming every industry in which it is implemented, with its impact upon the healthcare sector already saving lives and improving medical diagnoses,” stated Dr. Ian Roberts, Director of Therapeutic Technology at Healx, a biotechnology company based in Cambridge, England, in an account in BBH (Building Better Healthcare). “The transformative effect of AI is set to switch healthcare on its head, as the technology leads to a shift from reactive treatments targeting populations to proactive prevention tailored to the individual patient.”
In the future, AI-generated healthcare recommendations are seen as extending to include personalized treatment plans. “Currently we are in the infancy of AI in healthcare, and each company drives forward another piece of the puzzle and once fully integrated the future of medicine will be forever transformed,” Dr. Roberts stated.
However, the increasingly-connected environment of IoMT is seen as bringing new risks as cyber criminals seek to exploit device and network vulnerabilities to wreak havoc. A recent global survey by Extreme Networks, a network infrastructure provider, found that one in five healthcare IT professionals are unsure if every medical device on their network has all the latest software patches installed — creating a porous security infrastructure that could potentially be bypassed.
“2020 will be the year when healthcare organizations of all sizes will need to realize that they are easy pickings for cyber criminals, and put a robust, reliable and resilient network security infrastructure in place to protect themselves adequately,” stated Bob Zemke, director of healthcare solutions for Extreme.
Data science is seen as leading to more precise analytics. “In 2020, we can expect to see better patient outcomes fueled largely by the growing prevalence of data science and analytics,” stated lan Jacobson, chief data and analytic officer at Alteryx, a software company providing advanced analytics tools. “Much of the data that is required to solve some really-key challenges already exists in the public domain, and in the next year we expect more and more healthcare organizations will implement tools that help to assess this rich information as well as gain actionable insight.” The tools are seen as being effective in monitoring proper use of prescription drugs.
AI is Helping Forecast the Wind, Manage Wind Farms
By John P. Desmond, AI Trends Editor
Among all its many activities, Google is forecasting the wind.
Google and its DeepMind AI subsidiary have combined weather data with power data from 700 megawatts of wind energy that Google sources in the Central US. Using machine learning, they have been able to better predict the wind, which pays off in the energy market.
“The way a lot of power markets work is you have to schedule your assets a day ahead,” stated Michael Terrell, the head of energy market strategy at Google, in a recent account in Forbes. “And you tend to get compensated higher when you do that than if you sell into the market real-time.”
This is an example of the application of AI to wind energy and the wind energy market, an effort being tried in many regions by a range of players.
“What we’ve been doing is working in partnership with the DeepMind team to use machine learning to take the weather data that’s available publicly, actually forecast what we think the wind production will be the next day, and bid that wind into the day-ahead markets,” Terrell stated during a recent seminar hosted virtually by the Precourt Institute for Energy of Stanford University.
The result has been a 20% increase in revenue for wind farms, Terrell stated. Google has been on a mission to radically reduce its carbon footprint. The company recently achieved a milestone by matching its annual energy use with its annual renewable-energy procurement, Terrell stated.
“Our hope is that this kind of machine learning approach can strengthen the business case for wind power and drive further adoption of carbon-free energy on electric grids worldwide,” stated Sam Witherspoon, a DeepMind program manager, in a blog post. He and software engineer Carl Elkin described how they boosted profits for Google’s wind farms in the Southwest Power Pool, an energy market that stretches across the plains from the Canadian border to north Texas.
European Commitment to Wind Energy Seen in SmartWind Project
European countries have made a big commitment to wind energy, with offshore wind farms being required to supply about 8.5% of all energy in the Netherlands and 40% of current electricity consumption by 2030, according to a recent account in Innovation Origins.
AI is expected to play a big role in this effort, helping to increase energy generation and reduce maintenance costs for wind farms. The related SmartWind project is being undertaken by a consortium of four companies and the Ruhr-University Bochum in Germany.
“In SmartWind we can exploit the capabilities of artificial intelligence algorithms to optimize the management of wind farms,” stated Prof. Constantinos Sourkounis of the university’s Institute for Power Systems Technology, head of the German workgroup. The team aims to build an integrated cloud platform to reduce costs and optimize revenue, based on advanced and automated functions for data analysis, fault detection, diagnosis and operation and management recommendations.
The platform will collect data in real time from sensors and control systems, such as condition and maintenance management. Machine learning algorithms and other AI techniques form the backbone of early fault detection and diagnosis.
Turkish wind farm operator Zorlu Enerji, a SmartWind partner, will be able to put results of the research directly into practice. “The remarkable thing about this project is the close relationship between research and direct application. We are able to first test theoretical results in our laboratory, and then in a test wind farm run by our partner Zorlu Enerji,” stated Prof. Sourkounis.
Condition Monitoring Systems Help Manage Remote Wind Turbines
Machine condition monitoring systems (CMSs) are being applied to wind turbines to help ensure maximum availability and production.
“This is what we call Big Data, which includes both machine vibration and process data under all kinds of operating conditions and with all kinds of wind turbine types and components,” stated Mike Hastings, a senior application engineer with Bruel & Kjaer Vibro (B&K Vibro) of Darmstadt, Germany, writing in Wind Systems Mag. Over the past 20 years, the company has installed more than 25,000 data acquisition systems worldwide, with up to 12,000 of them being remotely monitored. As a result, “B&K Vibro has accumulated a vast database of monitoring data that includes fault data on almost every imaginable potential failure mode,” Hastings wrote.
As the worldwide installed capacity of wind turbines increases and plays a bigger role in the energy market, so does the need to ensure maximum availability and production of these turbines. Machine condition monitoring is important in this respect and many of the new turbines delivered today already have a condition monitoring system installed as standard. For offshore wind turbines, all have such a system because of their remoteness for maintenance.
“Big data fits very well into data-driven artificial intelligence (AI) and machine learning (ML) development and implementation,” Hastings wrote. AI and ML could be implemented for the following condition-monitoring tasks: fault detection optimization, automatic fault identification and prognosis for failure.
For fault detection, descriptors are configured by specialists, and detection of those is done automatically by the SMA. The individual descriptors and their configuration for fault detection have been optimized to a high level of reliability by diagnostics specialists with many years of experience. “One of the inherent benefits of AI is its ability to sift through vast quantities of CMS data to find patterns,” he wrote. Hidden diagnostics can be found in historical data as well.
For fault detection before potential failures, the AI can present the results as a listing of several potential failure modes, each with a probability of certainty. “B&K Vibro has in development neural-network automatic fault diagnostic products in the past, and this remains an area of interest for future refinement,” Hastings wrote.
Time is Right for the AI Infrastructure Alliance to Better Define Rules
By John P. Desmond, AI Trends Editor
The AI Infrastructure Alliance is taking shape, adding more partners who sign up to the effort to define a “canonical stack for AI and Machine Learning Operations (MLOps).” In programming, “canonical means according to the rules,” from a definition in webopedia.
The mission of the organization also includes, according to its website: develop best practices and architectures for doing AI/ML at scale in enterprise organizations; foster openness for algorithms, tooling, libraries, frameworks, models and datasets in AI/ML; advocate for technologies, such as differential privacy, that helps anonymize data sets and protect privacy; and work toward universal standards to share data between AI/ML applications.
Core members listed on the organization’s website include Determined AI, an early stage company focused on improving developer productivity around machine learning and AI applications, improving resource utilization, and reducing risk.
The determined.ai team encompasses machine learning and distributed systems experts, including key contributors to Spark MLlib, Apache Mesos, and PostgreSQL; PhDs from UC Berkeley and University of Chicago; and faculty at Carnegie Mellon University. Investors include GV (formerly Google Ventures), Amplify Partners, CRV, Haystack, SV Angel, The House, and Specialized Types. Founded in 2017, the company has raised a total of $13.6 million so far, according to Crunchbase.
Determined CEO Evans Says AI Stack “Needs to be Defined”
“At Determined, we have always been focused on democratizing AI, and our team remains incredibly optimistic about the future of bringing AI-native software infrastructure to the broader market,” said Determined Cofounder and CEO Evan Sparks, in an email response to a query from AI Trends on why the company joined the alliance. “This same mindset led us to open source our software last year in order to reach more teams across industries. As software becomes increasingly powered by AI, we think that the infrastructure stack to support developing and running software needs to be defined.”
He felt the challenge was too big for one company. “It’s going to take multiple companies solving different problems on the way as AI applications move from R&D into production, working together to define interfaces and standards to benefit data scientists and machine learning engineers. The AI Infrastructure Alliance is poised to be a powerful force in making this a reality.”
Asked why the mission of the AI Infrastructure Alliance is important, Sparks said, “In order to see the true potential of AI, AI development needs to be as accessible as software development, with little to no barriers to adoption. At Determined, we view collaboration as critical to achieving this. Joining the AI Infrastructure Alliance has provided us the opportunity to work with more like-minded companies in our own space and bring together the essential building blocks to create the future of AI, while creating a long-term framework for what AI success looks like.”
Super AI Focused on Quality of Datasets for Training
Another core member is Superb AI, a company focused on helping with training datasets for AI applications. The company offers labeling tools, quality control for training data, pre-trained model predictions, advanced auto-labeling and ability to filter and search datasets.
Hyunsoo Kim, CEO and cofounder, launched the company in 2018 with three other cofounders. He got the idea for the company while working on a PhD in robotics and AI at Duke University. The process to label data in order to train a computer in AI algorithms was expensive, laborious and error-prone. “This is partly because building a deep learning system requires extreme amounts of labeled data that involve labor-intensive manual work and because a standalone AI system is not accurate enough to be fully trusted in most situations,” stated Kim in an account in Forbes.
So far, the company has raised $2.3 million, according to Crunchbase. It has attracted support from Y Combinator, a Silicon Valley startup accelerator, Duke University and VC firms in Silicon Valley, Seoul and Dubai.
Pachyderm’s Platform Targets Data Scientists
Another core member is Pachyderm, described as an open source data science platform to support development of explainable, repeatable, and scalable ML/AI applications. The platform combines version control with tools to build scalable end-to-end ML/AI pipelines, while allowing developers to use the language and framework of their choice.
Among the company’s customers is LogMeIn, the Boston-based supplier of cloud-based SaaS services for unified communication and collaboration. At LogMeIn’s AI Center of Excellence in Israel, the company’s team deals with text, audio, and video that needs to get quickly processed and labeled for its data scientists to go to work delivering machine learning capabilities across their product lines.
“Our job at the AI hub is to bring the best-in-class ML models of, in our case, Speech Recognition and NLP,” stated Eyal Heldenberg, Voice AI Product Manager, in a case study posted on the Pachyderm website. “It became clearer that the ML cycle was not only training but also included lots of data preparation steps and iterations.” For example, one step to process audio would add up to seven weeks on the biggest computer machine Amazon Web Services has to offer. “That means lots of unproductive time for the research team,” stated Moshe Abramovitch, LogMeIn Data Science Engineer.
Pachyderm’s technology was chosen for a proof of concept test because its parallelism allowed nearly unlimited scaling. The result was instead of taking seven to eight weeks to transform data, Pachyderm’s products could perform the work in seven to 10 hours. The tech also had other benefits.
”Our models are more accurate, and they are getting to production and to the customer’s hands much faster,” stated Heldenberg. “Once you remove time-wasting, building block-like data preparation, the whole chain is affected by that. If we can go from weeks to hours processing data, it greatly affects everyone. This way we can focus on the fun stuff: the research, manipulating the models and making greater models and better models.”
Founded in 2014, Pachyderm has raised $28.1 million to date, according to Crunchbase.
With New Healthcare Tech Relying on Data Sharing, Trust is Required
By AI Trends Staff
On the verge of a new era of healthcare in which AI can combine with data sharing to deliver many new services, healthcare organizations need to earn the trust of patients that their data will be used properly.
That was a message delivered by speakers on healthcare and AI topics at the Consumer Electronics Show held virtually last week.
Issues related to data bias and explainability surfaced quickly. A major issue with machine learning recommendation systems is the inability for it to explain how it came to the suggestion, said Christina Silcox, Policy Fellow at the Duke-Margolis Center for Health Policy, in a session on Trust and the Impact of AI on Healthcare. “We don’t know how the software looks at the input and combines it into a recommendation. It finds its own pattern. There is not a way for it to communicate how it came to the decision. Work is being done on this,” she said. “But now even the developer does not know how the software is doing what it’s doing.”
In addition, some wellness technology incorporating AI may not have FDA approval as a medical device. The CARES Act of 2020 removed some devices from FDA oversight. Also, software may rely on company trade secrets that the firm may not be willing to share, making it more challenging to understand how the software works. “This information can be critical to patient trust,” she said.
Also, an evaluation of a wellness device using AI and data needs to cover what training data was used to represent the population, and what subgroups were included. Also needed is an evaluation of the software over time, “to make sure it’s still working,” she said.
Interoperable Medical Software Systems Elusive
Interoperability was an issue cited by Jesse Ehrenfeld, Chairman, Board of Trustees of the American Medical Association (and a Commander in the US Navy). “Algorithms that work at a children’s hospital may not work in an adult hospital, he said. “Understanding the context is critical.” He noted that these discussions with medical device-makers are challenging. Ehrenfeld recommended, “Having good clinicians have input into the development of these systems and tools is critical.” The AMA has tried to facilitate such discussions and has been having some success, he said.
Regarding data bias, Ehrenfeld said, “All data is biased; we just might not understand why.” It could be that it does not represent the larger population, or that the way it was captured introduced bias.
In a final thought, Silcox said, “As a nation, we have to strengthen our healthcare data, and put a focus on standardizing healthcare data, making sure it is interoperable. That is the key to improving AI in healthcare.”
Patient Data Sharing for Telemedicine Requires Transparent Practices
The pandemic era has ushered in increased use of telemedicine and with that, necessary data sharing. One supplier of wellness products said the company is very tuned into data privacy. “With us, privacy is number one. We look at it as the patient’s data and not our data,” said Randy Kellogg, President and CEO of Omron Healthcare, in a CES session on The Tradeoff Between Staying Secure and Staying Healthy. “We need permission to look at the patient’s data. We try to be transparent with people about how their data is going to be used in a telemedicine call,” he said.
Among Omron’s products is HeartGuide, a wearable blood pressure monitor in the form of a digital wristwatch, and a Bluetooth scale and body composition monitor. Data from these are pulled together in the company’s VitalSight remote patient monitoring program, with the goal of preventing heart attacks and strokes. Based in Kyoto, Japan, the company has been in business for over 40 years and offers products in 110 countries and regions. Asked by moderator Robin Raskin, founder of Solving for Tech, if patients are sharing their data more, Kellogg said, “Yes. It was happening before the pandemic and now more so. People are updating their data to the platforms.”
This trend of more health data sharing during the pandemic era was confirmed by Dr. Hasson A. Tetteh of the US Navy, an AI strategist who holds the position of Health Mission Chief with the DoD Joint AI Center. “We are dogmatic about security and privacy,” he said. “In the pandemic era, there has been a need to get more information from people than they may have been accustomed to, for the public good.”
Discussion turned to whether the HIPAA Privacy Rule regulating the use or disclosure of protected health information, which first went into effect in 2003, is out of date. “HIPAA is a bit dated,” Dr. Tetteh said. “Policy often lags rapid technology advances.” He said the DoD has “policy engineers” who work to keep patient information safe and secure. “We are all in the business of protecting patient safety and privacy, and we are using technology to do that,” he said. He noted that the DoD has issued AI principles on ethical applications. (See AI Trends coverage.)
Humetrix Stores Patient Data Locally, Not in the Cloud
Humetrix has been offering healthcare applications on consumer-centered mobile devices for 20 years. The company’s approach is to store patient data on a local device and not in the cloud, said Dr. Bettina Experton, president and CEO. “We still take advantage of AI algorithms in the cloud, but we don’t store personal information in the cloud. We call it ‘privacy by design’ architecture,” she said. The key to good security procedures to protect patient data is access control, she said.
Technology advances are enabling an approach to healthcare called precision medicine, which takes into account individual variations in genes, environment and lifestyle. Exemplifying this trend are the products of Myriad Genetic Laboratories, a 30-year-old company that has concentrated on the role that genes and proteins play in disease. The company’s surveys show nearly 80% of people do not have a good understanding of precision medicine and genetic testing, said Nicole Lambert, president of Myriad, in a CES session on Essential Technology for the New Health Revolution.
As a result, the company is focusing its efforts today on a specific target: women. “Pregnancy, cancer and mental health are the areas we are trying to impact the most,” said Lambert. She gave the example of the trial-and-error approach of prescribing antidepressants. “It’s 50-50 that the medicine will work,” she said. “The promise of precision medicine is to get the patient the right medicine at the right time,” improving the chances the prescription will be effective.
For detecting ovarian cancer, Myriad’s genetic tests can give each patient a level of risk, such as 36%, 57% or 87% risk. “We also give a five-year risk, allowing patients to put things in perspective,” she said. For instance, the first-year risk might be three percent while the lifetime risk might be 57%. “It helps people make decisions about their healthcare, she said, adding, “Precision medicine will only get more accurate over time.”
Remote Learning Boosting Adoption of Innovative Technologies for Education
By AI Trends Staff
With remote learning happening for students of all ages during the pandemic area, new technologies incorporating AI—including voice, augmented reality and virtual reality—are being used more widely to enable teaching.
“Some 1.2 billion children have been out of school during the pandemic year, and that has led to technology driving change in education,” said Robin Raskin, founder of Solving for Tech, moderator of a recent Consumer Electronics Show session on New Technologies Accelerating Education.
Creativity, Inc. provides design and engineering services for toy, technology, and learning companies. Clients include Disney, Netflix, Fisher-Price, Mattel, and Pearson. The company is working on building out new products that leverage voice interactions, said Caitlin Gutekunst, senior director of marketing and development. Consumers today interact with voice assistants on some 4.2 billion devices and the number is expected by Juniper Research to grow to 8.4 billion by 2024, she said.
“Voice is an interface, a new way for people to navigate and find information more easily,” she said. “Teachers are finding that voice provides new learning opportunities for students,” and can improve accessibility catering to the different learning styles of students, she said. The company envisions voice being used in more devices such as wearables and augmented reality/virtual reality (AR/VR) headsets. “We believe in binding entertainment with learning to make it fun for kids,” she said. The company developed Toy Doctor, an Alexa skill in which a child works as a doctor to help patients including Fuzzy the Teddy Bear and Rubber Ducky in a musical adventure.
Melanie Harke, a senior game designer with Schell Games, builds educational games using VR and AR. “It is still in an early adoption phase, but once you have a device you can travel to distant lands or practice dangerous procedures in a safe environment,” she said. “Immersion is the cornerstone; it makes it powerful,” she said, enabling it to be used to practice physical activities or improve muscle memory.
History Maker is Virtual Reality Content Creation Tool
The company has produced HoloLab Champions, a chemistry lab practice game show, enabling students without access to a real lab to gain experiences. Players are scored on accuracy and safety, helping to prepare them for real lab experiences.
The company’s newest product is History Maker, a virtual reality content creation tool aimed at middle school students. The game enables students to step into the shoes of a historical figure, such as Ben Franklin, Abigail Adams, Abraham Lincoln, Mark Twain and Barack Obama. Students create the scene, pick their props, upload and recite their script and export the performance to share with classmates and teachers.
“The pandemic has accelerated things, with more students participating in remote learning and more effort going into making the experiences better for kids. Having something immersive like VR can help,” Harke said. The company has made progress since entering the education market in 2016, but still, “It is early days for VR in education,” she said.
Spatial makes a AR/VR tool that can be used to create a lifelike avatar and a virtual classroom where the teacher has the necessary tools to present an immersive experience for students. “A lot of remote learning is happening in work settings. Tools like Spatial will be important to helping people feel connected,” said Aaron Dence, product manager with Spatial.
The product uses AI and machine learning to “tweak” a two-dimensional selfie photo to create a three-dimensional lifelike avatar. Colleges are looking at the technology to help create immersive learning experiences, such as the streets of Harlem in the 1950s, for a history class at the University of Arizona, and physicians and students working together at Teikyo University in Tokyo.
AR/VR Education Software Revenue Growing
Revenue for VR/AR educational software was estimated to be some $300 million in 2020, according to a report by Goldman Sachs, and is expected to grow to $700 million by 2025, according to a report in edu plus now. The quality of content is improving and the cost of hardware is correlating, making the technology more accessible to education institutions worldwide, the report stated.
Use cases for AR/VR in education include virtual field trips, medical education, and training, classroom education and student recruitment, according to an account from [x]cube LABS.
For medical education, applications can show complicated processes such as the human brain and visualize the abstract notions in digital reality. It equips students to merge the theoretical and practical parts of lessons. For recruitment, virtual tours enable students to explore the school or university campus remotely, thereby reducing expenses, increasing student engagement and helping them make a decision about the university.
“Augmented and virtual reality is redefining the teaching and learning process. Immersive technology has the potential of being the most prominent breakthrough in the education industry,” the authors state.
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