Today, the demand for preventative intervention is skyrocketing. But limited growth in clinical ranks indicates an ever-widening talent gap with cascading implications. WHO assessed a projected shortfall of 18 million healthcare workers by 2030. This gap is likely to be manifested primarily in low and lower-middle-income geographies. Medvocation, in one of its recent studies, found that nearly 44% of doctors worldwide are already breaking under the immense workload and are unable to live happy and healthy lives. The existing state of affairs can portend a crippling impediment as the global population ages. Reactive healthcare becoming more costly adds to the worries of the patients and clinical professionals.

1. Mindfire Solutions is a 20+ years old, 650+ people software
development and testing services company with a global
clientele. We offer custom web and mobile solutions for
companies across all major industries.

2. The COVID-19 pandemic overwhelmed the existing healthcare
infrastructure. It has been a rude reality check for clinical
administrators worldwide. Now, as the contagion subsumes, the
persisting rise in the global burden for non-communicable ailments
like lifestyle disorders is likely to keep medical practitioners on their
toes in the days ahead. With this, the demand for preventive
measures is increasing. By using AI for healthcare, we may step ahead
in this crisis.
A Looming Crisis In Healthcare
Today, the demand for preventative intervention is skyrocketing. But
limited growth in clinical ranks indicates an ever-widening talent gap
with cascading implications. WHO assessed a projected shortfall of
18 million healthcare workers by 2030. This gap is likely to be
manifested primarily in low and lower-middle-income geographies.
Medvocation, in one of its recent studies, found that nearly 44% of
doctors worldwide are already breaking under the immense workload
and are unable to live happy and healthy lives.
The existing state of affairs can portend a crippling impediment as
the global population ages. Reactive healthcare becoming more
costly adds to the worries of the patients and clinical professionals.

3. AI For Healthcare: Reimagining Wellbeing
Fortunately, advancements in data-driven technologies like AI and machine
learning have brought preventive medicine much closer to high-risk and
healthy individuals. It has improved the possibilities of self-care and
wellness like never before. For instance, today, AI applications can monitor
every heartbeat and predict congestive heart failure (CHF) with remarkable
accuracy. It permits the prospective patient a significant head start in
seeking expert advice long before ending up on a gurney in an ICU.
Due to this the global market for intelligent self-care medical devices ($13
billion in 2020) is expected to reach a valuation of more than $30 billion by
2027, with a CAGR of over 8%.
AI in Medicine: A Comprehensive Approach
For some years, AI-powered applications have made significant inroads
across various clinical procedures and treatments, from automating medical
front desk services, drug repurposing, vaccine development, building Smart
EHR systems, and improving pathology to successfully predicting drug
reactions. Today, at least 90% of healthcare establishments have an AI
strategy. Indeed the benefits are substantial as cognitive computing allows
practitioners to delegate repetitive tasks like clinical data extraction,
assimilation, and report creation to the machines. Effectively AI for
healthcare professionals can help with better decisions and focus on what
they do best: care for their patients.

4. However, the role of AI on the demand side of the story is equally
spectacular! Proper self-care, yet elusive, is now feasible as wearable
devices embedded with machine intelligence enable individuals to
listen to their vital signs better. As algorithms operating on the edge
get more intelligent and more emphatic, they will only expand the
chances of proactively and precisely diagnosing physiological
parameters and assessing the likelihood of acute events for
individuals with chronic conditions. It, in turn, will preempt health
risks, transform clinical deliveries and ease the pressure on the
existing medical infrastructure worldwide.
Factors Fueling The Influx Of AI For Healthcare
The trend is in no way isolated and wholly synced with the cognitive
technology maturity curve. Several factors advocate making personal
devices like Blood Glucose monitors, Insulin Pumps, Sleep Apnea
Devices, Blood Pressure Monitors, and Smart Watches intelligent
enough to improve self-care and wellness for their owners. None is
more telling than the dichotomy that although there has been an
explosive growth of health data collected institutionally in recent
years, it may still fall short in enabling patient outcomes.

5. For instance, an asthma patient typically visits the physician every
three months and spends over 2,100 hours in between when the
symptoms are not actively monitored, undermining realistic
assessment. Now, data continuously ingested through smart devices
can bridge this gap. Other factors include:
– Advancement in cloud computing:
The computational power available for training AI models and
algorithms has grown exponentially in recent years with the GPU
revolution. Today, with easy access to bare metal servers from cloud
infrastructure providers, it is easy to configure systems for running
high-performance healthcare workloads.
– Development of Deep Neural Networks:
The development of Artificial Neural Networks today is
supplementing ML capabilities, providing for much better and more
precise modeling. ML procedures like Capsule Neural Networks and
Transfer Learning can transform how ML models are built and
deployed, leading to far more accurate predictions even when trained
with limited datasets. It will indeed make self-care medical devices
smarter and more cost-effective.

6. – Shift in the healthcare delivery philosophy:
As AI systems become more readily available, institutions worldwide are
unmistakably considering how care is delivered and how precious
healthcare resources and infrastructure are utilized. For instance, the
National Institute of Health in the United Kingdom has launched an
initiative to encourage the use of AI for healthcare of individuals to self-
diagnose the onset of chronic conditions. The broader objective is to
eliminate unnecessary outpatient visits and save operating costs, optimizing
the resources available to the frontline care workers.
AI In Self-care: Use Cases
This research paper published by Nature.com manifests the overall
apprehensions of patients around the role of AI in healthcare. However,
advancements in cognitive technologies can bring in early and reliable
insights and even formulate an effective response to some of the most
prevalent chronic health conditions worldwide. These includes:
– Diabetes:
While the retroactive insights on blood sugar levels are currently derived
from lab tests like A1C and self-service glucometer readings, AI-enabled
devices can completely upend the diabetes treatment pathway. Intelligent
insulin pumps can monitor blood glucose levels and other health metrics in
real-time and auto-administer appropriate insulin doses based on the
patient’s health condition and symptoms.

7. – Hypertension:
Collecting blood pressure readings periodically through cuff-based
devices is only half the game, pending further diagnosis. However,
with Smart wearable devices connected to the cloud, blood pressure
data can be assimilated with multimodal data sets like genomics and
behavioral to pinpoint anomalies and preempt acute instances.
– Asthma:
Asthma patients must regularly visit clinics for pulmonary function
tests. They are monitored for environmental variables like air quality
and moisture profile that can adversely impact their health. AI
algorithms can extrapolate heart rate and blood oxygen level data
from pulse oxymeters. Utilizing other pointers like
pathophysiological analysis, natural history, seasonality, phenotypes,
genetics, environmental monitoring, disease biomarkers, etc., AI
predicts the possibilities of an asthma attack.
– Congestive Heart Failure (CHF):
Conventional detection for CHF is done through a clinical diagnosis
like ECG and studying factors like hereditary prevalence and lifestyle
choices. Nevertheless, AI algorithms can accurately predict heart
health through raw electrocardiograms to predict the possibilities of
CHF, almost with 100% accuracy.

8. – Depression:
Screening for mental health conditions depends on subjective
evaluations to detect the root cause and respond to symptoms.
However, AI algorithms can eliminate this subjectivity and clinical
bias from the equation by evaluating symptoms through facial
expressions, voice patterns, and online habits. Moreover, they can
objectively assess treatment progress by interpreting brainwave
profiles unique to patients with depression.
Final Thoughts
Bringing AI to improve the state of self-care and wellness is an idea
whose time has come. Cognitive technologies can play a pivotal role
in preventing catastrophic health events and saving lives. However,
considering the wide range of variables and the risks involved, expert
implementation becomes as crucial as the technology to ensure the
first-time-right outcomes. Therefore alongside investment in
technology, it becomes a strategic necessity to find an experienced
technology partner who can aptly demonstrate the viability of self-
care through AI.
Mindfire Solutions is one such leader in AI/ML, well-acclaimed in the
global health tech market. Get in touch with one of their AI
consultants to discover how the company simplifies self-care and
wellness for millions worldwide.

9. US East Coast: +1 248.686.1424
US West Coast: +1 248.631.4210
sales@mindfiresolutions.com