AI Impact on Healthcare

www.wjpps.com Vol 7, Issue 3, 2018. 379
Amit. World Journal of Pharmacy and Pharmaceutical Sciences
WILL ARTIFICIAL INTELLIGENCE OUST MEDICAL
PRACTIONAIRS & DRUG DISCOVERY THINK TANKS?
Dr. Amit Gangwal*
Director, Business Intelligence, Arogya Retail, Indore, MP, India.
2017 was the year when artificial intelligence (AI) took small steps
towards becoming a big part of our lives even as critics warned about
its long term effects. Experts envisage a closer integration of robotics
(hardware with AI) in coming year along with further march in deep
learning. Deep learning is a subset of machine learning that has
networks which has capacity of unsupervised learning from data that is
unstructured or unlabeled. Also called deep neural learning/deep neural
network. AL has already entered homes in the form of digital
assignments and is also, inter alia, at heart of the driverless cars that
are tipped to transform how people travel & commute. This is high
time to become champions of change by incorporating AI chapters inschool and college
curriculum. This is vital because almost all type of jobs will require some basic knowledge
about AI and data analytics/science. Career or business mapping should be done keeping in
mind onslaught and/or boons of AI. The title of this article is apposite because AI has the
power to snatch or finish expertise required in particular domain. This will perhaps end
monopoly game. It has been published in Times of India news paper before few days that
because of monopoly technology & competency trap; Xerox had to re-plan its business
strategy. Imagine now owing to advent of AI what will happen in coming years. Here I am
sharing one example from automobile industry. How technologies are changing basics of
automobile industries. Fuel run cars then electric cars and then (not finally) driver less cars.
Interesting thing to note here is that driverless car was not the idea of classical automobile
industries. We may also say traditional car making organizations were not as fast as non car
making organization like Google, Uber, Tesla etc. This was AI who lent an edge to Google
and other companies. Similarly AI will change the way clinical trials are conducted, doctors
diagnose and treat the patients and lot many routine and highly specialized things will be
changed and will be controlled by AI devices. I am not encroaching into the technical
WORLD JOURNAL OF PHARMACY AND PHARMACEUTICAL SCIENCES
SJIF Impact Factor 7.421
Volume 7, Issue 3, 379-384 Review Article ISSN 2278 – 4357
Article Received on
04 Jan. 2018,
Revised on 25 Jan. 2018,
Accepted on 15 Feb. 2018
DOI: 10.20959/wjpps20183-11104
*Corresponding Author
Dr. Amit Gangwal
Director, Business
Intelligence, Arogya Retail,
Indore, MP, India.

terminologies (of AI) of which I am not expert of, as various top notch technology giants are
engaged in it. Here I am sharing few most commonly used/involved technical jargons and
these are: random forests (or random decision forests), support vector machine, regression
analysis, classification, linear algebra (vectors, matrices, derivatives), calculus, basic
probability theory, python programming etc.
AI will change the fashion in which prevention and treatment of various human diseases are
undertaken. Initially AI will assist directly to medical fraternity based on latter’s inputs (at
present these inputs are being investigated/planned by non medical experts with assistance of
medical fraternity). Gradually role of humans (engaged in healthcare related all operations
like counseling, diagnosis, analysis, prescribing) will go on diminishing and AI guided tools
will start taking over. I am confident (but not happy while writing this) that in next few
decades patients will be interacting (though this word is meant for humans) with machines
and there won’t be any coming across of him with human medical staff. In this article I am
sharing example of diabetes mellitus. How AI and related technological advances will change
the way we perceive prevention and treatment of disorders. In few decades or not in so
distant future, most of the research based pharmaceutical giants have to upgrade their set up
from purely pharmacology based approach to AI approach, if they want to address diseases. I
am refraining from using the phrase drug discovery (perhaps over the years AI, machine
learning, biomechanics, 3D printing will wipe out all drug discovery and clinical trial
approach). Country like ours needs more infrastructures like labs, hospitals, qualified doctors
(experienced doctors and doctors with high success rate) & diagnostic technicians. This can
be addressed by artificial intelligence. This all will be possible by much higher versions of
classical/regular computers; quantum computers. They will be used for quantum supremacy.
Google is a leader in this race. These next generation computing devices will outperform
classical computers by many notches. AI will prescribe treatment based on inputs by patient.
This will minimize bias by doctor for a particular brand, may it be diagnosis or treatment.
Doctors may mistakenly be biased in their observation of symptoms; they may miss catching
the early symptoms of diseases. AI has the potential to ward off these human factors. Another
example can be of eye disorders. Ophthalmologists diagnose the diabetic eye disease by
analyzing the pictures of the back of the eye for lesions. The severity is identified by the
appearance of lesions that indicate bleeding and leakage in the eye. Only experienced doctors
and specialists can read and interpret these pictures. There is an acute shortage of qualified
ophthalmologists that can diagnose eyes of diabetics. AI may empower doctors here by

reading diagnostic reports properly. Same holds true for other specific diagnostic tests like
MRI, CT scan and others.
AI and disease treatment
Diabetes Mellitus, due to its many-sided, dynamics and data-intensive nature, is a exemplary
disease for the application of AI-based approaches (rule-based, case-based and model-based
reasoning, machine learning and visual analytics. The complication of diabetes diagnosis and
management has led AI to become a leading technology to provide solutions that empower
patients, caregivers in their everyday life and scientific fraternity engaged in finding solution
against it. Several publicly-funded projects have been carried out, such as: EMPOWER,
MOBIGUIDE, COMMODITY12 EU, DIADVISOR, DIABEO, and the recently launched
PEPPER project. sual analytics).

AI and drug discovery
Drug discovery is not everyone’s cup of tea/coffee. • Developing a new drug costs an average
of nearly $2.6 billion and may take as long as 1.5 decades. Artificial intelligence will play
crucial role in curtailing this sum and time frame. What AI can do? Artificial intelligence is
showing the potential to be a faster, more efficient way to find and develop new drugs. A
growing number of organizations & universities are focusing to minimize the complexities
involved in classical way of drug discovery by using AI computing to envisage which drug
candidates are most likely to be effective treatments. What if; a software generates new
molecular structures by combining properties of existing drugs, automatic chemical design
helps drug discovery team jump to conclusion in a faster & accurate manner, a treatment
method from the ground up using a deep learning neural network, a system to use historical,
biological & chemical data to imagine novel molecules with the potential to fight major
diseases. Leading pharma companies already started working on AI enabled drug discovery
models or collaborating with tech/IT organizations. Established tech/IT companies have also
started diverting/expanding their portfolio towards drug discovery. AI techniques in use for
drug discovery are deep learning technique known as a generative adversarial network
(GAN) by Baltimore based company, insilico medicine, GPU (graphics processing unit)-
accelerated deep learning to target cancer and age-related illnesses by above organization.
BenevolentBio’s deep learning software, powered by the NVIDIA DGX-1 AI supercomputer
(it ingests & analyzes the information to find connections and propose drug candidates).
BenevolentBio aims to reinvent drug discovery by using deep learning and natural language
processing to understand and analyze large pool of data originated from patents, genomic
data and the more than 10,000 publications uploaded daily across all biomedical journals and
databases. Artificial intelligence enabled techniques (AIET) assist drug discovery team in
finding perfect or most optimum lead among great number of tentative drug candidates. AIET
can generate large pool of data based on input and customized solution one needs. AIET can
wrangle tens of crores of data to present you with the most optimum set of data, by filtering
unwanted one. AIET’s most important job/tool is to extract only those relevant data (from
huge data pool, from patent data base, journal data base and other data base source) which is
most consistent, significant and result oriented from drug discovery point of view for targeted
disease. No doubt here input giving rights will be resting with humans only. Continue • But
once after feeding raw intelligence to machines, AIET will not be in human control for that
particular shot. Later you may again change codes as per your expected outcome. Benefits of

AI are getting rid of data mining by humans, saving of time, ater initial set up, lesser
expenses.
CONCLUSION
It will be interesting to see who will conquer; gene therapy or AI. Let us wait to see what
happens when interface connecting human brain and computer will be at his full youth.
Algorithm, spectrogram (visual representation) will change the way we perceive & respond
to various stuff. Algorithm will become an interpreter sort of tool for users. This will be
wonderful if think from perspective of paralysis marred people. Computational
neuroscientists have to face challenges from brain functioning. Signals/firings from neurons
are not a well defined or recorded data. Functions of neurons overlap. On one incident motor
cortex can generate 100 signals/second, but on another occasion it may give 115 signals.
What will happen when humans will succeed in making a full set of algorithms for all
activities of brain? Artificial intelligence, visual data processing and face recognition
technology are the forefronts. All these newer technologies are in infancy, but at its full youth
this will solve some of the oldest problem of humans. Perhaps gene therapy and
immunotherapy were incomplete without high level of computing devices. Now with the
advent of AI, these two domains of medical treatment will get the booster dose. Gene
therapy, immunotherapy will provide one shot-long term solution for diseases with major
computing and analyzing inputs from AI. AL will assist/guide in targeting individual person,
rather to provide common immunotherapy for all cancer victims, for instance. One thing is
sure; AI will end domain specific working. The algorithms can identify
incongruity/strangeness faster. More and more wearable devices will be launched in coming
years; which will further empower the users/patients. Dosage forms and doctors will witness
biggest changes in coming years. These changes will decrease their impotence or direct role
in addressing disease; as devices or technologist will take control of medical stuff. Similarly
classical dosage form will lose their utility. Will advent of AI reduce the unbearable financial
burden (An analysis by Times of India reveals that medicines, diagnostics & consumables
like syringes, dressing, catheter etc typically constitute 30-50 % of the sum in a hospital bill)
on patients and others? AI will also play crucial roles in performing diagnostic tests
(including compatibility test for blood and donors’ organs), dialysis, precise and accurate
chemotherapy. Problem of antibiotic resistance may also be addressed by AI. Around 23000
people lose their lives owing to antibiotic resistant infections in America. Tom Reuner, SVP
of intelligent automation and IT services at IT consulting firm HfS, believes that employees

have to incessantly re-invent themselves as the journey toward digital transformation
necessitates new skill sets and continuous learning. "Many employees will struggle to make
that journey. But equally, many service providers will struggle to adapt to these new realities.
Skills will become more important than just scale," he said. Ray Wang, CEO of Constellation
Research, said cloud, artificial intelligence, and software platforms will lead to 20%- 30%
reduction of staffing by 2020. Good news is that Artificial intelligence will never eradicate
the jobs of scientists unlike it may do for other industries where danger of losing jobs is
looming like anything. This is so because, here health aspect of people is associated and
therefore even an iota of AI enabled stuff has to be ensured and validated by scientists.
REFERENCES
1. https://pdfs.semanticscholar.org/04c3/4b1621f15e238b1d72a1addbf6b2edbe3709.
2. http://timesofindia.indiatimes.com/business/india-business/technology-giants-prepare-for-
layoffs/articleshow/58585806.cms.

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