Delivering Better Healthcare Services with Edge AI

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Delivering Better Healthcare Services with Edge
AI
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• Medicine has been one of the most renowned
  success stories of modern science and technology.
  However, the MIT Technology review observes that
  until 2020 the pace of digital transformation in
  this sector has been frustratingly low owing to
  its risk-averse nature and spiraling costs. The
  mainstreaming of digital tools for enabling the
  treatment outcomes was emerging, but slowly.
• But the disruptions in the wake of the COVID-19
  upended the scenario, stretching the global
  healthcare workforce to its limits. At the height
  of the pandemic, Mental Health America reported
  that stress and burnout affected 93 of the
  healthcare workers. It compelled the
  decision-makers to reconsider operational
  practices and find ways to build, manage and
  scale smarter hospital applications that
  intuitively assist the healthcare providers.
  Nevertheless, for such applications to deliver,
  the vast operational data streams need to be
  processed near their point of ingestion to reduce
  lags and enable real-time decisions.

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• Solving Real-World Problems
• For instance, consider this use case. For
  patients undergoing treatment or residents of old
  age homes, falling from bed can be a significant
  issue, severely delaying recovery. In fact,
  research by Cleveland Clinic reports that 30 of
  such falls may result in serious injuries.
  However, continuous watch out across hospital
  wards can be extremely tasking for the medical
  staff. Here, can round-the-clock manual
  surveillance be replaced by bringing machine
  intelligence closer to the hospital floor? A
  smart application running object detection
  algorithms for face landmarks and body pose
  detection can predict a fall and trigger an alarm
  for the responders.
• Such runtime feedback loops needed for remotely
  monitoring the patients body posture and vital
  signs and arriving at instant decisions based on
  situations are only possible by running AI
  algorithms on Edge.

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• What is Edge AI?
• Currently, AI-powered solutions are implemented
  using powerful data centers on-premise or in the
  cloud. However, healthcares inherent challenges
  and peculiarities make this architecture
  difficult to be used across healthcare use cases.
  Running AI algorithms in the cloud comes with
  limitations like
• - Unreliable Internet Connectivity While
  developed nations are way ahead in internet
  penetration and robust connectivity, it can be a
  challenge across the Global South. Further
  internet connectivity can be limited in rural
  areas and field hospitals.
• - High Operational Costs Sending data to the
  cloud and back to the device involves costs. Also
  transferring medical imaging in no-loss formats
  may push up operational expenses.
• - High Latency Putting massive data traffic
  through the internet can cause delays which is
  unacceptable in life and death situations. For
  instance, in the above illustration, the images
  of a patients position on the bed must be
  processed in real-time by the AI engine for
  optimized response.

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• In respite, Edge computing AI or TinyML brings
  the power into the device installed in the field.
  Instead of the cloud, the concept focuses on
  implementing neural networks at the endpoints or
  the networks Edge. The AI-enabled edge device
  can thus process the data loads locally without
  relying on the cloud processing backend.
• The Edge AI concept is based on the fact that the
  training and deployment of Machine Learning (ML)
  models can be done separately. Therefore it is
  possible to embed pre-trained ML models into
  medical devices with limited memory and
  computing, converting them into smart systems.
  However, challenges persist in efficiently
  handling AI workloads on the Edge owing to
  limited computational bandwidth, and the
  predominantly vendor/platform-specific nature of
  the available solutions.

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• Nevertheless, in the current digital economy
  characterized by the proliferation of the
  Industry 4.0 constructs and 5G, there is much
  optimism about Edge AI, with robust estimates
  from every corner. Research and Markets predict
  the global Edge AI software market will
  demonstrate a handsome CAGR of 19, reaching
  nearly 2 billion by 2026. On the other hand,
  Edge AI Hardware Market Outlook 2030 by Allied
  Market Research forecasts the market size for
  processors, memory, and sensors to reach 38
  billion by the end of this decade.
• Data-driven Healthcare
• But what has led to the increased mainstreaming
  of the Edge-operated AI in healthcare in recent
  years? Apparently, an explosion of data in the
  sector due to the adoption of IoT and an
  increasing demand to harness it sustainably to
  deliver more personalized and intuitive clinical
  journeys. Dell Technologies reports that
  healthcare and life sciences presently account
  for 30 of all data stored globally, and about 3
  million data points are generated on average
  across various clinical trials.

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• The data volumes are expected only to go up in
  the coming days due to the increased usage of
  connected devices and IoT sensors in healthcare.
  For instance, right now, at least 1015 connected
  devices are in use per hospital bed in the US.
  Based on this trend, experts predict that 75 of
  the healthcare data will be generated at the Edge
  of the networks by 2025.
• Benefits of Edge AI
• Here Edge AI provides the tool to process the
  data near the source and bringing transformative
  benefits for healthcare like
• - Improved Security Instead of data centers
  maintaining data within the Edge devices,
  confidential information on patient health
  remains secured from intrusions and less exposed
  to mass data breaches.

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• - Faster Triaging Accurate diagnosis of health
  issues is key to delivering clinical outcomes and
  proper patient care. Here, operating alongside
  human healthcare professionals, AI solutions on
  the Edge can rapidly perform multiple tests at
  scale, giving better insights into the patients
  health. For instance, Google is leveraging AI to
  help doctors screen patients for diabetes-induced
  retinopathy and prevent early blindness.
• - Lean Healthcare IT Processing healthcare data
  using Edge AI allows healthcare institutions to
  adopt a leaner IT infrastructure. While
  operational and tactical aspects are pushed to
  the Edge, the cloud and data center bandwidths
  are focused on more strategic roles. Also, it
  ensures that vital healthcare processes are still
  available even in an outage.

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• - Process Automation AI-enabled Edge devices can
  take on the repetitive tasks of the clinical
  environment and help healthcare workers to focus
  on more strategic tasks, saving time and money.
  For instance, in the US, on average, nurses spend
  up to 25 of their work hours on administrative
  works like patient onboarding and documentation.
  Instead, robotic process automation and Edge AI
  at the front desk can use tech like Natural
  Language Processing for initial patient
  interviews and capture and make the relevant
  information readily available for the healthcare
  professionals to review.
• Edge AI Use Cases in Healthcare
• While the benefits of inducting Edge AI in
  healthcare are apparent, what are some of the use
  cases where the technology is currently operating
  or may be adopted in the days ahead? Experts
  believe that the agility of Edge AI makes it
  highly contextual along the entire healthcare
  journey. Interventions include

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• - First Response Ambulances that ferry patients
  and accident victims to hospitals are no longer
  just transportations but slowly evolving into
  mobile Edge platforms that can deliver the
  necessary care within the golden hour. For
  instance, in Spain, EMS members use tablet PCs to
  capture patients vital signs and send them over
  a 5G network for analysis by the emergency
  personnel back at the hospital. In the days
  ahead, such information can be processed on the
  go using AI, directing the EMS professionals on
  the necessary steps to prevent the loss of life.
• - In The Hospital Within clinical environments,
  Edge computing and AI are pacing up the quality
  of diagnosis and automating medicine delivery.
  Instead of repeatedly transporting patients into
  various facilities for checks, Edge AI brings
  such services right to the patient. For instance,
  healthcare establishments like UCLA Health,
  Massachusetts General Hospital, or Kings College
  Hospital in London have inducted AI-powered MRI
  scanners that can operate at the patients
  bedside, identifying anomalies and helping
  radiologists analyze the situation in real-time.
  Further, instead of depending on nurses to
  administer the insulin shots to diabetics, an
  smart insulin pump can ingest data from
  artificial pancreas sensors under the patients
  skin to determine the blood sugar levels,
  automating delivery.

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• - At Home One of Edge AIs most prominent use
  cases in healthcare is telemedicine, delivering
  treatment directly to patients where they live.
  The American Medical Association and Wellness
  Council of America believe that upto 75 of the
  clinical workloads can be handled safely through
  telemedicine. For instance, using smart Edge
  sensors to monitor patient conditions at home can
  trigger alerts for the caregivers if the
  situation deteriorates. Also, Edge AI can help
  bring optimum healthcare to remote areas where
  quality medical expertise may not be available.
  The embedded intelligence can help process data
  locally and guide low-skilled medical
  professionals to make informed decisions.
• Final Thoughts
• While the benefits of Edge Ai in healthcare are
  multifaceted and compelling, much depends on
  skilled execution. In fact, in high-risk
  environments like healthcare, the imperative to
  get first-time-right outcomes can hardly be
  overemphasized! Therefore alongside investment in
  technology, it becomes a strategic necessity to
  find an experienced Edge and AI implementation
  partner who can get the job done and deliver the
  desired objectives.

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• Like other businesses, if you too are looking for
  low code development platforms Mindfire Solutions
  can be your partner of choice. We have a team of
  highly skilled and certified software
  professionals, who have developed many custom
  solutions for our global clients over the years.
• Here are a few interesting projects we have done.
  Click here to know more
• Video consulting web application
• Automated testing solution for IoT project

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Thanks You

• Content Source Medium
• Contented By Mindfire Solutions