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REDESIGNING THE
HEALTHCARE WITH
ARTIFICIAL
INTELLIGENCE,
GENOMICS &
NEUROSCIENCE
HEALTHCARE WHITEPAPER BY ARTIVATIC
DATA LABS PRIVATE LIMITED
ABSTRACT
Healthcare in today’s world has not changed in
terms of method of diagnosis where the doctor
analyses the patient’s history along with historical
records of symptoms to their diagnosis, keeping
in mind the current practices involved in the
treatment. Usually going through multiple tests
and a process of elimination, the process is hectic
and more often than not prone to human error. It
is not possible for any doctor to analyse every bit
of data available in relation to a patient which
may include the genetic code etc. Nor is it
possible for them to keep track of all historical
cases where similar symptoms may have been
shown. This is where the application of AI and
ML are crucial. They streamline the process and
reduce human error while considering all the data
available. With the use of AI, the doctor could
automatically get recommendations on what kind
of diseases could be causing the symptoms
shown. Or the patients could be suggested the
correct doctor based on their personal preferences
and symptoms shown.
Keywords: Artificial Intelligence, Machine
Learning, Genomic, Neuroscience, Diseases

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Healthcare is Evolving
Ever since the Middle Ages healthcare has been an integral part of our society. In modern
times, it is one of the behemoth industries with a whole host of flaws. Even with limited
healthcare access for the majority of the world’s population and immense inefficiencies, the
Centres for Medicare and Medicaid Services (U.S.A.) have recorded a National Healthcare
Expenditure at $3.2 Trillion in 2015, a 5.8% growth from the previous year. Based on a research
published in the Journal of Rare Disorder, patients with rare diseases visit an average of 7.3
physicians before receiving an accurate diagnosis. With an immense amount of data being
recorded to add to the historical case data already available, it makes it almost impossible for
any doctor or team of doctors to have the knowledge an AI application can harvest with the
same amount of data.
THE TIME IS RIPE TO BRING THE AGE-OLD INDUSTRY INTO
THE WORLD OF ARTIFICIAL INTELLIGENCE. STREAMLINE THE
PROCESS AND GIVE THE WORLD THE HEALTHCARE
STANDARDS THEY DESERVE

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Problems in Healthcare
Too Much Information
Reports published by EMC and IDC show
that there is a volume of 153 exabytes of
health care data available. They also
projected an approximate year over year
increase of 48% to that data.
This amount of data is a daunting task for a
computer to process. Human Doctors are
not capable of even scraping the surface of
this amount of data and use it effectively to
diagnose patients.
Lack of Education
For a patient, the doctor they go to is often
a general practitioner who then forwards
them to a specialist. This is an extra step
which can easily be avoided if the patient
had a basic understanding of symptoms to
disease and or doctor specializations. This
again cannot be expected from common
people without a formal education in
medicine.
Patient Inflexibility
Patients have many personal preferences or
are bound by multiple factors such as
religion, capacity to travel, special needs
etc. These often cause patients to ignore
their medical care requirements.
Decentralised Untracked Data
Even the digitalised data that exists in the
healthcare ecosystem is decentralised
where the medical reports from tests,
prescriptions, diagnosis details, case studies
are all stored separately. Nor is this data
used to extrapolate or correlated to current
patient information to find impending
problems which could otherwise be easily
treated.
Excessive Tests
Current approach to diagnosing patients for
symptoms is through a process of
elimination from the initial set of possible
causes of symptoms the doctor may
shortlist. More than 7 billion clinical lab
tests are performed in the U.S. each year.
These are needed to cover all the bases and
increasing accuracy of a correct diagnosis
and reducing malpractice.
Increasing Costs and Lack of Insurance
WHO has calculated the average per capita
expenditure on healthcare to be $1058.518.
This is growing year over year with
countries such as the U.S. spending $9,403
per capita on healthcare. Even with UN’s
Sustainable Development Goals that all UN
Member States have agreed to try to
achieve Universal Health Coverage by
2030. It is still going to leave a majority of
the world’s population unable to afford
quality medical care

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Current Use of AI in Healthcare Industry
Healthcare Bots
The healthcare industry was one of the first to avail
the help of robots for complex and delicate surgical
procedures. This is a small step in the correct
direction. The robots contain some Artificial
Intelligence allowing them to avoid mistakes and
learn from the mistakes that do happen.
Smart Wearable Devices
Patients often use smart watches or other wearable
devices allowing their basic health parameters and
sometimes specific parameters to be tracked and
their healthcare advisors to be updated with the data
collected allowing a quick response to anomalous
health parameters.
Personal Health Virtual Assistant
Apps like Siri, Cortana and a whole host of similar
apps are based on high performance AI engines
capable of helping
their users with the general questions they
have about possible symptoms and learn to
track attributes of the user and alert them in
real time if a health risk is detected.

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Case: AI in Healthcare
With the use of classification techniques, neural networks, and other ML techniques it is
possible to use the Exabytes of data to learn patterns and create intelligent models. This would
allow us to analyse a patient and be able to detect diseases and other anomalies with minimal
effort. Also with negative and positive feedback over time, the algorithms perfect the models
and increase accuracy as well.
Using machine learning algorithms, we can streamline the process with a more holistic
approach. The processing capacity of the processors available today allows us to churn all the
possible data available along with the historical data to find patterns and match new patients to
historical trends seen. This allows us to find the most likely health issues.
The recommended list of diseases is personalised to each patient based on the data available
which is used to match to the historical data models which are created using machine learning
algorithms and patient tendencies etc. Over time as the historical models are perfected, the
accuracy of the recommendation will increase to an optimal level. Till that happens the doctors
would have to give feedback to the algorithm to let it learn correct recommendation from wrong
recommendations. Eventually the time taken to find a cure can be reduced drastically and
malpractice or incorrect diagnosis eradicated.
As the information about patients are brought online, along with all the historical data mapping
symptoms to possible cures and specialisations of doctors best fit for diagnosing the them, the
patient can get recommendations for the correct doctor. These recommendations would be
based not only on the symptoms that the patient is displaying, but also the previous patient’s
choices for doctor’s location, gender, age and experience level etc.
Using Artivatic, the patients and healthcare providers were added as Users and Products
respectively via AVDataQuartz. Interaction levels were defined as given below: -
All interactions are saved and used to find the affinity of a patient to a particular attribute of a
provider.
1. Personalized text search for providers with unstructured queries
Interaction Name Interaction Level
Search 1
Check Doctor Availability 2
View Doctor Rating 3
Check Doctor Cost Estimate 4
Compare Doctor 5

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a. Personalized auto suggestions for Doctors matching search string
Solved using elastic search implementation through Artivatic suggestion API which
also provides the search result in personalized order.
2. Categorized listing of doctors in order of personalized preference with an option to
change it to order of rating etc.
a. Includes automated personalization without manual filters
Recommendation API with specialization string as a filter provides a personalized
response with doctors with the requested specialization will be returned.
b. Location based filters based on radius
Recommendation API allows the usage of location filter where the radius and current
latitude and longitude are provided. The response will contain the list of providers
which are within the defined radius from the location provided.
3. Guest user access with minimal input for filters.
a. Parameters to be input by the user should be auto detected for maximum
efficiency with minimal input.
Artivatic detects the weight of attributes which best define the patient. The guest user
is requested to enter only the top 4 attributes so Artivatic can classify the guest within
the current personal models developed.
4. Cost estimator
Doctor estimated cost is tracked and compared to the insurance plan the patient has and
an approximate payment required out of pocket are calculated.
5. Compare Doctors
Up to 3 doctors can be added to the comparison list which also provides a level of
interaction for Artivatic to use for building its models.
6. Manual flow to pick disease or other symptoms based on a body map and find
corresponding specialist doctors
Doctor specialization to symptoms and diseases are stored in Artivatic and available for
search through elastic search APIs.

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Few Key Areas of Artivatic in Healthcare
Doctors & Patients Engagement
Notify of prospective patients
Find patients who are in need of appointments from their doctors. May be people who are
current patients or not.
Notify anomaly in patient reports
Parse patient reports and find the likelihood of problems. Notify doctors about possible health
risks.
Track health
Receive updates about the patient health including medical reports and prescriptions and also
test results. Also, data from health devices showing activity details will also be of importance.
Provide Intelligent Help
Parse patient reports and find the likelihood of problems. Notify patient and doctors about
possible health risks.
Suggest better lifestyle choices
Parse patient reports and find the likelihood of problems. Lifestyle choices with their benefits.
Predict diagnosis
Patient data, specific diagnosis data & accurate case studies to predict the critical aspects of the
diagnosis that will help in better & right diagnosis.
Look out for genetic issues
Patient data, specific diagnosis data, past /historical data & accurate case studies are needed to
identify the genetic issues to predict and recommend the diseases or lifestyle change that may
happen over time.
Provide doctor suggestions
Find patients who are in need of appointments from their doctors.
Suggest articles
Patient conditions and diseases, article tags with conditions and diseases information will help
in improving the doctor’s learning and enriching information for better understanding and up
to date to the medical related information.

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Doctor and Pharma Companies
Suggest content for doctors to read (Articles etc.)
Article tags with conditions and diseases, which company endorsed the article etc. up to pharma
company loyalty levels.
Topic suggestions for articles based on patients and trends of diseases
Based on the diagnosis of the patient, articles related to the disease will be suggested to the
user.
Supply chain discrepancies
Artivatic model can help in identifying the problems in the supply-chain system for pharma,
doctors, MR focused environment and improvise the entire supply & demand need with high
accuracy.
Predict in other cities
The demand of drugs in various cities based on the diseases, population and environment
change, Artivatic can help in predicting the demand based on the up and down scenarios of the
health-related problems.
Weather and season based drug demand prediction
Artivatic can enable the season based drug demand predication with AI but this is long term
process with large amount of data sets.

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Upcoming Implementations in Healthcare
The future implementations of AI in Healthcare include the following: -
• Use of NLP to read medical reports allowing for digitalisation of reports
• Usage of image recognition along with deep learning to finding fractures or other
anomalies in scans.
• Usage of genetic mapping to detect hereditary complications early helping in
limiting/delaying/preventing the onset of issues related to the genes.
• Usage of genetic mapping to detect mutations in genes, helping in
limiting/delaying/preventing any negative effects of the mutation.

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Disclaimer: This document has been released solely for educational and informational purposes. Artivatic does not make any
representations or warranties whatsoever regarding quality, reliability, functionality, or compatibility of products and
solutions, services and technologies mentioned herewith. Depending on specific situations, products and solutions may need
customization, and performance and results may vary.