Daily Archives: January 7, 2022

SEATTLE--(BUSINESS WIRE)--The Cloud Security Alliance (CSA), the worlds leading organization dedicated to defining standards, certifications, and best practices to help ensure a secure cloud computing environment, today released Artificial Intelligence (AI) in Healthcare. Drafted by the Health Information Management Working Group, the report provides an overview of the ways in which AI and machine learning (ML) can be used to bring about major transformations in healthcare while addressing the challenges their use presents, and offering guidance for how to best incorporate them into healthcare systems now and in the future.

The document shares examples, use cases, and treatment methods for how AI, machine learning, and data mining can be effectively utilized throughout a healthcare system, including in research, diagnosis, and treatment. It also addresses ethical and legal challenges, bias in AI, and how it relates to telehealth, big data, and cloud computing in healthcare.

This is the time when healthcare leaders should be accelerating their use of AI, which when used with cloud computing has the potential for drastically improving patient outcomes. But, as with any new technology entering the healthcare arena, there are several challenges, among them a lack of data exchange, regulatory compliance requirements, and patient and provider adoption. This paper offers a summary of the areas in which healthcare can benefit, while providing healthcare delivery organizations guidance on how to best address the challenges their use brings, said Dr. James Angle, the papers lead author and co-chair of the Health Information Management Working Group.

The emergence of AI as a tool for better healthcare offers opportunities to improve patient and clinical outcomes and reduce costs. The ever-increasing volume and complexity of healthcare data provide an ideal environment for the application of both AI and ML, and there are several applications where these technologies can deliver an incredible value. Even so, healthcare delivery organizations must evaluate each to determine if and how they can be adopted, said Michael Roza, a contributor to the paper.

The CSA Health Information Management Working Group aims to provide a direct influence on how health information service providers deliver secure cloud solutions (services, transport, applications, and storage) to their clients, and to foster cloud awareness within all aspects of healthcare and related industries. Individuals interested in becoming involved in Health Information Management future research and initiatives are invited to join the working group.

Download Artificial Intelligence in Healthcare.

About Cloud Security Alliance

The Cloud Security Alliance (CSA) is the worlds leading organization dedicated to defining and raising awareness of best practices to help ensure a secure cloud computing environment. CSA harnesses the subject matter expertise of industry practitioners, associations, governments, and its corporate and individual members to offer cloud security-specific research, education, training, certification, events, and products. CSA's activities, knowledge, and extensive network benefit the entire community impacted by cloud from providers and customers to governments, entrepreneurs, and the assurance industry and provide a forum through which different parties can work together to create and maintain a trusted cloud ecosystem. For further information, visit us at http://www.cloudsecurityalliance.org, and follow us on Twitter @cloudsa.

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Cloud Security Alliance Releases Guidance on Use of Artificial Intelligence (AI) in Healthcare - Business Wire

An Ubtech Walker X Robot plays Chinese chess during 2021 World Artificial Intelligence Conference (WAIC) at Shanghai World Expo Center on July 8, 2021 in Shanghai, China.

VCG | VCG via Getty Images

Machines are getting smarter and smarter every year, but artificial intelligence is yet to live up to the hype that's been generated by some of the world's largest technology companies.

AI can excel at specific narrow tasks such as playing chess but it struggles to do more than one thing well. A seven-year-old has far broader intelligence than any of today's AI systems, for example.

"AI algorithms are good at approaching individual tasks, or tasks that include a small degree of variability," Edward Grefenstette, a research scientist at Meta AI, formerly Facebook AI Research, told CNBC.

"However, the real world encompasses significant potential for change, a dynamic which we are bad at capturing within our training algorithms, yielding brittle intelligence," he added.

AI researchers have started to show that there are ways to efficiently adapt AI training methods to changing environments or tasks, resulting in more robust agents, Grefenstette said. He believes there will be more industrial and scientific applications of such methods this year that will produce "noticeable leaps."

While AI still has a long way to go before anything like human-level intelligence is achieved, it hasn't stopped the likes of Google, Facebook (Meta) and Amazon investing billions of dollars into hiring talented AI researchers who can potentially improve everything from search engines and voice assistants to aspects of the so-called "metaverse."

Anthropologist Beth Singler, who studies AI and robots at the University of Cambridge, told CNBC that claims about the effectiveness and reality of AI in spaces that are now being labeled as the metaverse will become more commonplace in 2022 as more money is invested in the area and the public start to recognize the "metaverse" as a term and a concept.

Singler also warned that there could be "too little discussion" in 2022 of the effect of the metaverse on people's "identities, communities, and rights."

Gary Marcus, a scientist who sold an AI start-up to Uber and is currently executive chairman of another firm called Robust AI, told CNBC that the most important AI breakthrough in 2022 will likely be one that the world doesn't immediately see.

"The cycle from lab discovery to practicality can take years," he said, adding that the field of deep learning still has a long way to go. Deep learning is an area of AI that attempts to mimic the activity in layers of neurons in the brain to learn how to recognize complex patterns in data.

Marcus believes the most important challenge for AI right now is to "find a good way of combining all the world's immense knowledge of science and technology" with deep learning. At the moment "deep learning can't leverage all that knowledge and instead is stuck again and again trying to learn everything from scratch," he said.

"I predict there will be progress on this problem this year that will ultimately be transformational, towards what I called hybrid systems, but that it'll be another few years before we see major dividends," Marcus added. "The thing that we probably will see this year or next is the first medicine in which AI played a substantial role in the discovery process."

One of the biggest AI breakthroughs in the last couple of years has come from London-headquartered research lab DeepMind, which is owned by Alphabet.

The company has successfully created AI software that can accurately predict the structure that proteins will fold into in a matter of days, solving a 50-year-old "grand challenge" that could pave the way for better understanding of diseases and drug discovery.

Neil Lawrence, a professor of machine learning at the University of Cambridge, told CNBC that he expects to see DeepMind target more big science questions in 2022.

Language models AI systems that can generate convincing text, converse with humans, respond to questions, and more are also set to improve in 2022.

The best-known language model is OpenAI's GPT-3 but DeepMind said in December that its new "RETRO" language model can beat others 25 times its size.

Catherine Breslin, a machine learning scientist who used to work on Amazon Alexa, thinks Big Tech will race toward larger and larger language models next year.

Breslin, who now runs AI consultancy firm Kingfisher Labs, told CNBC that there will also be a move toward models that combine vision, speech and language capability, rather than treat them as separate tasks.

Nathan Benaich, a venture capitalist with Air Street Capital and the co-author of the annual State of AI report, told CNBC that a new breed of companies will likely use language models to predict the most effective RNA (ribonucleic acid) sequences.

"Last year we witnessed the impact of RNA technologies as novel covid vaccines, many of them built on this technology, brought an end to nation-wide lockdowns," he said. "This year, I believe we will see a new crop of AI-first RNA therapeutic companies. Using language models to predict the most effective RNA sequences to target a disease of interest, these new companies could dramatically speed up the time it takes to discover new drugs and vaccines."

While a number of advancements could be around the corner, there are major concerns around the ethics of AI, which can be highly discriminative and biased when trained on certain datasets. AI systems are also being used to power autonomous weapons and to generate fake porn.

Verena Rieser, a professor of conversational AI at Heriot-Watt University in Edinburgh, told CNBC that there will be a stronger focus on ethical questions around AI in 2022.

"I don't know whether AI will be able to do much 'new' stuff by the end of 2022 but hopefully it will do it better," she said, adding that this means it would be fairer, less biased and more inclusive.

Samim Winiger, an independent AI researcher who used to work for a Big Tech firm, added that he believes there will be revelations around the use of machine learning models in financial markets, spying, and health care.

"It will raise major questions about privacy, legality, ethics and economics," he told CNBC.

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How A.I. is set to evolve in 2022 - CNBC

Higher education already employs artificial intelligence in a number of effective wayscourse and facilities scheduling, student recruitment campaign development, endowment investments and support, and many other operational activities are guided by AI at large institutions. The programs that run AIalgorithmscan use big data to project or predict outcomes based on machine learning, in which the computer learns to adapt to a myriad of changing elements, conditions and trends.

Adaptive learning is one of the early applications of AI to the actual teaching and learning process. In this case AI is employed to orchestrate the interaction between the learner and instructional material. This enables the program to most efficiently guide the learner to meet desired outcomes based upon the unique needs and preferences of the learner. Using a series of assessments, the algorithm presents a customized selection of instructional materials adapted to what the learner has demonstrated mastery over and what the learner has yet to learn. This method efficiently eliminates needless repetition of material already learned while advancing through the content at the pace of the learner ensuring that learning outcomes are accomplished.

There is great room for further growth of AI in higher ed, as Susan Fourtan writes in Fierce Education:

The potential and impact of AI on teaching have prompted some colleges and universities to take a closer look at it, accelerating its adoption across campuses. For perspective, the global AI market is projected to reach almost $170billion by 2025. By 2028, the AI market size is expected to gain momentum by reaching over $360billion, registering a growth rate of 33.6percent between 2021 and 2028, according to a research firm Fortune Business Insights report. The market is mostly segmented into Machine Learning, Natural Language Processing (NLP), image processing, and speech recognition.

One of the pioneers in applying AI to supporting learning at the university level, Ashok Goel of Georgia Tech, famously developed Jill Watson, an AI program to serve as a virtual graduate assistant. Since Jills first semester in 2016, Goel has repeatedly and incrementally improved the program, expanding the potential to create additional AI assistants. The program is becoming increasingly affordable and replicable:

The first iteration of Jill Watson took between 1,000 and 1,500 person hours to complete. While thats understandable for a groundbreaking research project, its not a feasible time investment for a middle school teacher. So Goel and his team set about reducing the time it took to create a customized version of Jill Watson. Now we can build a Jill Watson in less than ten hours, Goel says. That reduction in build time is thanks to Agent Smith, a new creation by Goel and his team. All the Agent Smith system needs to create a personalized Jill Watson is a course syllabus and a one-on-one Q&A session with the person teaching it In a sense, its using AI to create AI, Goel says, which is what you want in the long term, because if humans keep on creating AI, its going to take a long time.

Increasingly, many students are accustomed to interacting with AI-driven chat bots. Serving in a wide range of capacities at colleges, the chat bots commonly converse in text or computer-generated speech using natural language processing. These algorithms may even create a virtual relationship with the students. Such is the case with a chat bot named Oli tested by Common App. For 12 months this chat bot communicated with half a million students of the high school Class of 2021 twice a week to guide them through the college application process. In addition to the pro forma steps in the application process, Oli would offer friendly reminders to students to look after themselves in these COVID times, including suggestions to remind them to keep in touch with friends, listen to favorite music or take deep breaths. When the process was complete, Oli texted.

Hey pal, Oli said one week before officially signing off, I wanted to let you know that I have to say goodbye soon. Remember, even without me, youre never alone. Dont hesitate to reach out to your advisor or close ones if you need help or someone to talk to. College isnt easy, but its exciting and youre so ready! The relationship might have ended there. But some of Olis human correspondents had more to say. Hundreds of them texted back, effusive in their praise for the support the chatbot had offered as they pursued college. Research about social robots shows that children view them as sort of alive and make an attempt to build a mutual relationship, writes MIT professor Sherry Turkle. Its a type of connection, a degree of friendship, that excites some researchers and worries others.

Just last month, Google announced a new AI tutor platform to give students personalized feedback, assignments and guidance. Brandon Paykamian writes in GovTech,

[Google Head of Education] Steven Butschi described the product as an expansion of Student Success Services, Googles software suite released last year that includes virtual assistants, analytics, enrollment algorithms and other applications for higher ed. He said the new AI tutor platform collects competency skills graphs made by educators, then uses AI to generate learning activities, such as short-answer or multiple-choice questions, which students can access on an app. The platform also includes applications that can chat with students, provide coaching for reading comprehension and writing, and advise them on academic course plans based on their prior knowledge, career goals and interests.

With all of these AI applications in development and early release phases, questions have arisen as to how we can best ensure that biases are avoided in AI algorithms used in education. At the same time concerns have been raised that we make sure that learners recognize these are computer programs rather than direct communication with live instructors, that privacy of learners is maintained, and related concerns about the use of AI. The federal Office of Technology and Science Policy is gathering information with the intention of creating an AI Bill of Rights. Generally, the AI bill of rights is meant to clarify the rights and freedoms of persons using, or who are subject to, data-driven biometric technologies.

How is your institution preparing to integrate reliable, cost-effective and efficient AI tools for instruction, assessment, advising and deeper engagement with learners? Are the stakeholdersincluding faculty, staff, students and the broader communityincluded in the process to facilitate the broadest input and ensure the advantages and intended outcomes from the use of AI?

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Artificial Intelligence to Assist, Tutor, Teach and Assess in Higher Ed - Inside Higher Ed

Detroit, Michigan, Jan. 06, 2022 (GLOBE NEWSWIRE) -- Artificial Intelligence Technology Solutions, Inc., (OTCPK:AITX), today filed a Form 8-K with the Securities and Exchange Commission that provides details on the companys corporate charter amendment that has been filed with the Nevada Secretary of State. The charter amendment formalizes the companys commitment not to engage in a reverse stock split of its Common Stock before January 1, 2024, unless the Company is uplisting the Company to NASDAQ or the NYSE.

The AITX investor communitys support of our growth, progress and success is remarkable and much appreciated. Although Ive shared this exact statement about not planning any reverse stock splits on Twitter, weekly videos, and other interactions, Im happy to prove this beyond a doubt by locking it in with a corporate charter amendment and associated Form 8-K filing, said Steve Reinharz, CEO of AITX. I will continue to balance the considerations of our clients, our retail investors, our team members, our dealers and our institutional investors to ensure we remain focused on our mission of building AITX into a powerhouse of human-aiding AI-enabled machines.

CAUTIONARY DISCLOSURE ABOUT FORWARD-LOOKING STATEMENTSThis release contains "forward-looking statements" within the meaning of Section 27A of the Securities Act of 1933, as amended, and Section 21E the Securities Exchange Act of 1934, as amended and such forward-looking statements are made pursuant to the safe harbor provisions of the Private Securities Litigation Reform Act of 1995. Statements in this news release other than statements of historical fact are "forward-looking statements" that are based on current expectations and assumptions. Forward-looking statements involve risks and uncertainties that could cause actual results to differ materially from those expressed or implied by the statements, including, but not limited to, the following: the ability of Artificial Intelligence Technology Solutions to provide for its obligations, to provide working capital needs from operating revenues, to obtain additional financing needed for any future acquisitions, to meet competitive challenges and technological changes, to meet business and financial goals including projections and forecasts, and other risks. Artificial Intelligence Technology Solutions undertakes no duty to update any forward-looking statement(s) and/or to confirm the statement(s) to actual results or changes in Artificial Intelligence Technology Solutions expectations.

About Artificial Intelligence Technology Solutions (AITX)AITX is an innovator in the delivery of artificial intelligence-based solutions that empower organizations to gain new insight, solve complex challenges and fuel new business ideas. Through its next-generation robotic product offerings, AITXs RAD, RAD-M and RAD-G companies help organizations streamline operations, increase ROI, and strengthen business. AITX technology improves the simplicity and economics of patrolling and guard services and allows experienced personnel to focus on more strategic tasks. Customers augment the capabilities of existing staffs and gain higher levels of situational awareness, all at drastically reduced cost. AITX solutions are well suited for use in multiple industries such as enterprises, government, transportation, critical infrastructure, education, and healthcare. To learn more, visit http://www.aitx.ai, http://www.radsecurity.com and http://www.radlightmyway.com, or follow Steve Reinharz on Twitter @SteveReinharz.

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Steve Reinharz949-636-7060@SteveReinharz

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Artificial Intelligence Technology Solutions Files 8-K Detailing Commitment Not to Engage in a Reverse Stock Split of its Common Stock - GlobeNewswire

Most scientific areas now use big data analysis to extract knowledge from complicated and massive databases. This method is now utilized in medicine to investigate big groups of individuals. This review helped to understand that the employed artificial intelligence and sophisticated machine learning approaches to investigate physio pathogenesis-based therapy in pSS. The procedure also estimated the evolution of trends in statistical techniques, cohort sizes, and the number of publications throughout this time span. In all, 44,077 abstracts and 1,017 publications were reviewed. The mean number of chosen articles each year was 101.0 (S.D. 19.16), but it climbed dramatically with time (from 74 articles in 2008 to 138 in 2017). Only 12 of them focused on pSS, but none on the topic of pathogenesis-based therapy. A thorough assessment of the literature over the last decade collected all papers reporting on the application of sophisticated statistical analysis in the study of systemic autoimmune disorders (SADs). To accomplish this job, an automatic bibliography screening approach has been devised.To summarize, whereas medicine is gradually entering the era of big data analysis and artificial intelligence, these techniques are not yet being utilized to characterize pSS-specific pathogenesis-based treatment. Nonetheless, big multicenter studies using advanced algorithmic methods on large cohorts of SADs patients are studying this feature.

Reference:www.tandfonline.com/doi/full/10.1080/21645515.2018.1475872

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Artificial Intelligence and Sophisticated Machine Learning Techniques are Being Used to Develop Pathogenesi... - Physician's Weekly

To make fusion energy a viable resource for the worlds energy grid, researchers need to understand the turbulent motion of plasmas: a mix of ions and electrons swirling around in reactor vessels. The plasma particles, following magnetic field lines in toroidal chambers known as tokamaks, must be confined long enough for fusion devices to produce significant gains in net energy, a challenge when the hot edge of the plasma (over 1 million degrees Celsius) is just centimeters away from the much cooler solid walls of the vessel.

Abhilash Mathews, a PhD candidate in the Department of Nuclear Science and Engineering working at MITs Plasma Science and Fusion Center (PSFC), believes this plasma edge to be a particularly rich source of unanswered questions. A turbulent boundary, it is central to understanding plasma confinement, fueling, and the potentially damaging heat fluxes that can strike material surfaces factors that impact fusion reactor designs.

To better understand edge conditions, scientistsfocus on modeling turbulence at this boundary using numerical simulations that will help predict the plasma's behavior. However, first principles simulations of this region are among the most challenging and time-consuming computations in fusion research. Progress could be accelerated if researchers could develop reduced computer models that run much faster, but with quantified levels of accuracy.

For decades, tokamak physicists have regularly used a reduced two-fluid theory rather than higher-fidelity models to simulate boundary plasmas in experiment, despite uncertainty about accuracy. In a pair of recent publications, Mathews begins directly testing the accuracy of this reduced plasma turbulence model in a new way: he combines physics with machine learning.

A successful theory is supposed to predict what you're going to observe, explains Mathews, for example, the temperature, the density, the electric potential, the flows. And its the relationships between these variables that fundamentally define a turbulence theory. What our work essentially examines is the dynamic relationship between two of these variables: the turbulent electric field and the electron pressure.

In the first paper, published in Physical Review E, Mathews employs a novel deep-learning technique that uses artificial neural networks to build representations of the equations governing the reduced fluid theory. With this framework, he demonstrates a way to compute the turbulent electric field from an electron pressure fluctuation in the plasma consistent with the reduced fluid theory. Models commonly used to relate the electric field to pressure break down when applied to turbulent plasmas, but this one is robust even to noisy pressure measurements.

In the second paper, published in Physics of Plasmas, Mathews further investigates this connection, contrasting it against higher-fidelity turbulence simulations. This first-of-its-kind comparison of turbulence across models has previously been difficult if not impossible to evaluate precisely. Mathews finds that in plasmas relevant to existing fusion devices, the reduced fluid model's predicted turbulent fields are consistent with high-fidelity calculations. In this sense, the reduced turbulence theory works. But to fully validate it, one should check every connection between every variable, says Mathews.

Mathews advisor, Principal Research Scientist Jerry Hughes, notes that plasma turbulence is notoriously difficult to simulate, more so than the familiar turbulence seen in air and water. This work shows that, under the right set of conditions, physics-informed machine-learning techniques can paint a very full picture of the rapidly fluctuating edge plasma, beginning from a limited set of observations. Im excited to see how we can apply this to new experiments, in which we essentially never observe every quantity we want.

These physics-informed deep-learning methods pave new ways in testing old theories and expanding what can be observed from new experiments. David Hatch, a research scientist at the Institute for Fusion Studies at the University of Texas at Austin, believes these applications are the start of a promising new technique.

Abhis work is a majorachievement with the potential for broad application, he says. For example, given limited diagnostic measurements of a specific plasma quantity, physics-informed machine learning could infer additional plasma quantities in a nearby domain, thereby augmenting the information provided by a given diagnostic. The technique also opens new strategies for model validation.

Mathews sees exciting research ahead.

Translating these techniques into fusion experiments for real edge plasmas is one goal we have in sight, and work is currently underway, he says. But this is just the beginning.

Mathews wassupported in this workby theManson Benedict Fellowship,Natural Sciences and Engineering Research Council of Canada,andU.S. Department of Energy Office of Science under the Fusion Energy Sciences program.

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Seeing the plasma edge of fusion experiments in new ways with artificial intelligence - MIT News

In this podcast, Julianne Pepitone sits down with Rob Pulciani, Executive Vice President of AI and Machine Learning Product at Capital One to talk about the AI algorithms already embedded in the customer experience today and what deeper personalization of those tools will look like in the future.

JULIANNE PEPITONE

On the company side of things, Im curious what kind of guardrails have to be in place, to ensure data security and privacy, as you said it needs to be handled responsibly. So are those conversations happening now, and what do you think is required to keep this safe, and beneficial for everyone?

ROB PULCIANI

So, first off its worth noting that machine learning can never come to life without data, and access to that data, and being able to understand that data. So data transformation in terms of our data ecosystem at Capital One has been a journey that weve been on for a while now, in order to, you know, give those personalized experiences to our millions of customers. And as we think about guardrails for privacy, its really important that we keep track of the real value that were delivering to our customers and maintaining high standards for our user privacy, transparency, and security. We need to make sure that were getting the proper permissions from consumers, and theyre fully aware of the value trade off. I think thats the most important aspect: making sure that the value is there, and making sure that customers understand how their datas being used, and that theyre in control of it.

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My AI: Why personalized artificial intelligence could be the next big - Fast Company

JUPITER, Fla., Jan. 6, 2022 /PRNewswire-PRWeb/ -- Scheduled to broadcast spring/2022, the award-winning series, Advancements with Ted Danson, will discover how innovations in AI are helping employees to access, understand, and utilize their health benefits.

In this segment, Advancements will explore why so many Americans lack an understanding about their healthcare benefits -- from complex rules to complicated, verbose verbiage. Viewers will learn about the many ways these complexities can negatively impact employees' health and well-being, productivity in the workplace, and ultimately, the U.S. workforce as a whole.

Audiences will hear from experts at Insurights, an AI-powered startup on a mission to improve human health by giving people better access to their health benefits. The show will discover how developments in AI and technology present a solution for the industry as the Insurights team introduces Zoe, its digital healthcare navigator.

"The way U.S. workers engage with their health benefits is broken. Too many employees forgo vital treatment or waste precious time trying to understand exactly what is covered under their health plan," said Guy Benjamin, co-CEO and co-founder of Insurights. "Providing employees with helpful, accurate and immediate answers to questions is essential to their wellbeing. Zoe's ability to analyze any health plan and provide invaluable information will have a profound impact on the workforce and lead to healthier, happier employees."

Viewers will learn firsthand how Zoe simplifies the healthcare process and provides employees with much-needed transparency about their benefits. Spectators will see how Zoe answers employees' questions about health coverage and benefits on-the-spot, helps to find lower-cost providers, and informs about relevant preventive care benefits to ensure optimized healthcare.

"Throughout the country, roughly 70 percent of Americans do not understand their healthcare plan," said Senior Producer, DJ Metzer. "We look forward to exploring how technology is helping to break down these barriers and how it's giving people the knowledge they need to know and understand their rights and benefits."

About Insurights:

Insurights is an AI-powered startup on a mission to improve human health by giving people better access to their health benefits. The digital platform provides employees with on-the-spot answers to health benefits questions, helping them find lower-cost providers and informing about relevant preventive care benefits. Insurights is dedicated to helping employees be healthier by making health benefits simple and accessible to everyone, everywhere.

Insurights is based in Israel and New York, and investors include Group 11, Cresson Management, Good Company, and Insurtech Israel. For more information, visit: http://www.insurights.com/

About Advancements and DMG Productions:

The Advancements series is an information-based educational show targeting recent advances across a number of industries and economies. Featuring state-of-the-art solutions and important issues facing today's consumers and business professionals, Advancements focuses on cutting-edge developments, and brings this information to the public with the vision to enlighten about how technology and innovation continue to transform our world.

Backed by experts in various fields, DMG Productions is dedicated to education and advancement, and to consistently producing commercial-free, educational programming on which both viewers and networks depend.

For more info, please visit: AdvancementsTV.com or call DJ Metzer at 866-496-4065.

Media Contact

Sarah McBrayer, DMG Productions, 866-496-4065, info@advancementstv.com

SOURCE Advancements with Ted Danson

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Learn how Artificial Intelligence is Improving the Healthcare Experience - WFMZ Allentown