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.

1. DELIVERING BETTER HEALTHCARE
SERVICES WITH EDGE AI

2. Medicine has been one of the most renowned success stories of
modern science and technology. However, the MITTechnology
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.

3. 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
patient’s body posture and vital signs and arriving at instant
decisions based on situations are only possible by running AI
algorithms on Edge.

4. What is Edge AI?
Currently, AI-powered solutions are implemented using powerful
data centers on-premise or in the cloud. However, healthcare’s
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 patient’s
position on the bed must be processed in real-time by the AI engine
for optimized response.

5. In respite, Edge computing AI orTinyML 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
network’s 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. 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.

6. 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. DellTechnologies 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.
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 10–15 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.

7. 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.
- FasterTriaging: 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.

8. - 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:

9. - 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.
- - InThe 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 King’s College Hospital in London
have inducted AI-powered MRI scanners that can operate at the
patient’s 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
patient’s skin to determine the blood sugar levels, automating delivery.

10. - At Home: One of Edge AI’s 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.
FinalThoughts
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.

11. 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.

12. THANKS YOU
Content Source: Medium
Contented by: Mindfire Solutions