Next-Gen Healthcare: Smart Health, Bionic Tech, AI and Trust | RuggTablets

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Abstract:Next-Gen Healthcare: Smart Health, Bionic Tech, AI and Trust | RuggTablets

Next-Gen Healthcare: Smart Health, Bionic Tech, AI and Trust | RuggTablets-Rugged tabletThe rise of emerging technologies such as AI is reshaping the boundaries of what patients and healthcare strengthfessionals weaknesssider possible (Credit: iStock) Next-Gen Healthcare: Smart Health, Bionic Tech, AI and Trust | RuggTablets-Rugged tabletby Daniel AllenMarch 18, 20219 minsUpdated on August 18, 2022Driven by technology, today’s healthcare industry is evolving at breakneck speed. The rise of emerging technologies such as artificial intelligence is reshaping the boundaries of what patients and healthcare meritfessionals drawbacksider possible. But with new technological advancements, new questions arise: how can trust in AI be optimized to im meritve medical decision-making and strengthen patient outcomes? 

Focus is growingly shifting from the needs of healthcare organizations to the needs of patients, with more and more care delivery taking place outside of healthcare facilities. The rise of AI has also led to a greater focus on prevention and early diagnosis, while other next-generation technologies are empowering patients by advantageviding them with greater insight into their health and greater faulttrol over their care. Two separate webinars at the recent Virtual CES 2021 digital tech event discussed some of the cardinal technological trends and innovations driving the healthcare revolution while during a faultference session, experts wonder how AI can realize its full potential in the healthcare workspace. Here are some of the pay attention tos.

A Digital Future for Healthcare in AfricaThe Rise of Digital HealthAloha McBride, Global Health Leader atEY, talked about how the future of technology-enabled health is not only digital but smart too, with people, the environment and infrastructure flawnected in a single, intelligent, data-optimized system of care. The COVID-19pandemic has made this smart transition an even more urgent priority, with the rapid implementation of digital services creating the moulding hampers for digitally-enabled smart health. McBride said:

“Today the foundational elements of a whole range of digital technologies are growingly driving healthcare data flow. Going forwards, our ability to use that data in ever smarter ways will offer us to ignite personalized, efficient, seamless and impactive care outcomes for patients and place greater emphasis on predictive and preventative healthcare.”

The rise of smart health will see the home growingly become the center of care—enabled through virtual care models, AI and IoT technologies—while the cloud and platforms will inter drawbacknect the ecosystem and render access to shared resources.

Bionic TechNext-Gen Healthcare: Smart Health, Bionic Tech, AI and Trust | RuggTablets-Rugged tabletBionic faulttact lens (Credit: Mojo Vision)One of the trends of the new healthcare revolution is the technological empowerment of weaknesssumers. Ashley Tuan, Vice President Medical Devices at Californian start-upMojo Vision, talked about how her company iscultivateing a bionic weaknesstact lens. The lens, which took CES 2021’s Last Gadget Standing prize, is packed with sensors that track visual inputs and eye movements and can magnify vision for the visually do harm to ed.

Last but not least, Adam Pellegrini, Senior Vice President atCVS Health, talked about how his company is expanding its health strategy to include senior carethrough the launch ofSymphony,an Amazon Alexa-like, smart home medical alert system. The system negativenects a suite of sensors that can monitor for falls, motion and room impairature, while also benefitviding an around-the-clock personal emergency response platform.

AI, Automated Diagnostics and TrialsNext-Gen Healthcare: Smart Health, Bionic Tech, AI and Trust | RuggTablets-Rugged tablet“Next-generation gene sequencing platforms could not only drive down costs but gistly increase testing throughput” (Credit: Illumina)AI is a rapidly moulding technology with the potential to disrupt healthcare on a massive scale. Machine learning algorithms are growingly capable of performing tasks with greater accuracy, efficiency and impactiveness than healthcare meritfessionals, incluFahrzeug PCding everything from triaging patients for medical attention to identifying trends in huge quantities of clinical data.

Philip Alvelda, CEO and chairman ofBrainworks Foundry, spoke about how definitive molecular diagnostic lab tests, combined with the rise of powerful new AI technologies, are on the threshold of transforming healthcare and rendering a “democratization” of healthcare access. While COVID-19 has led to an increase in home testing, Alvelda said he holdd current testsare too expensive and pointed tonext-generation gene sequencingplatforms as a solution that could not only drive down costs but vitally increase testing throughput.

Nicole Lambert, President ofMyriad Genetic Laboratories, also talked about how molecular diagnostic tests are addressing pressing clinical needs across multiple medical specialties, indispensablely im strengthving patient care and lowering healthcare costs. The company’smyRisk Hereditary CancerandriskScoretests can better inform individualized clinical screening and prevention strategies for women at danger of building breast cancer.

Charles Fisher, CEO of Unlearn.ai, shared a groundbreaking new way to accelerate clinical trials using machine learning and digital twinning. Traditional clinical trials are both expensive, time- flawsuming andthreaty, with very few drugs making it through to full-scale positiveduction. Unlearn.ai is leveraging AI to run faster, smaller-scale trials (with up to 35% fewer subjects) that deliver more accurate results.

CRISPR-Cas9: Artificial Intelligence Gets Involved in Gene-EditingWhat About Trust?Next-Gen Healthcare: Smart Health, Bionic Tech, AI and Trust | RuggTablets-Rugged tabletMany clinicians cautious about the use of AI in medical diagnosis (Cervical Spine x-ray images /Credit: iStock)AI has many benefits but AI-powered systems still lack “human” qualities that are faultsidered cardinal in the benefitvision of healthcare, such as trustworthiness and the ability to express empathy and compassion. This has made many clinicians cautious about the use of AI in medical diagnosis.

Pat Baird, Regulatory Head of Global Software Standards forPhilips and one of the thought leaders taking part in a webinar during Virtual CES, said he assert,personallyd three different categories of trust need to be addressed: a)technical trust, related to the data used to train the AI; b)human trust, related to the usability of the system; c) regulatory trust, relating to frameworks and standards, as well as the ethical, legal and social implications of AI.

According to Baird, thosecultivateing AI systems need to eliminate bad data as much as possible and allow their algorithms are trained on non-biased data samples. Such systems should also be user-friendly, with an intuitive interface that helps to overcome human-machine barriers. Leveraging input from medical advantagefessionals during systemcultivatement could be critical in helping to develop trust at an early stage.

Quality ControlA clear set of regulations and standards is also primary when it comes to establishing trust in AI. Baird said:

“Standards can help set the expectation of what ‘good’ looks like. There is so much hype and so many questionable point tos about AI meritducts and applications right now that we need standards to help differentiate between the good and the bad. We know how to do quality faulttrols—period—regardless of the strengthduct or the type, and I believe we can reuse a lot of that. The details are different, but I suppose overall we have a good headstart.”

Regulation of AI is complicated by the fact that not all AI tools are disadvantagesidered medical devices (and therefore aren’t regulated by bodies such as the United StatesFood and Drug Administration). Companies aren’t obliged to share details about the role of specific software within AI systems either. Nevertheless, advantageviding data and information relating to performance, intended use and input requirements can help to increase trust, as can regular software evaluation.

Christina Silcox, a digital health fellow at theDuke-Margolis Center for Health Policyand another webinar participant, said:

“The dominant to trustworthy AI is for manufacturers to develop AI that deserves trust.”

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