Artificial Intelligence (AI), machine learning, and deep learning are taking the healthcare industry by storm. They are not pie in the sky technologies any longer; they are practical tools that can help companies optimize their service provision, improve the standard of care, generate more revenue, and decrease risk. Nearly all major companies in the healthcare space have already begun to use the technology in practice; here I present some of the important highlights of the implementation, and what they mean for other companies in healthcare.

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MD vs MACHINE
AI IN HEALTHCARE
By.Dr.Mahboob ali khan Phd
Artificial Intelligence (AI), machine learning, and deep learning are
taking the healthcare industry by storm. They are not pie in the sky
technologies any longer; they are practical tools that can help companies optimize
their service provision, improve the standard of care, generate more revenue, and
decrease risk. Nearly all major companies in the healthcare space have already
begun to use the technology in practice; here I present some of the important
highlights of the implementation, and what they mean for other companies in
healthcare.
General gains for the industry
It seems that the question is not “if” but “when” AI will
revolutionize the healthcare.
It took some time for the medical community to accept the
stethoscope. It will also take a while to recognize A.I. as a full-

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fledged health tool – despite its vast potential to revolutionize
healthcare. Yet, it is so powerful that when it will finally take its
rightful place in healthcare, it will displace the stethoscope as its
symbol.
AI, machine learning, and deep learning are already increasing
profits in the healthcare industry. For example, according to
research firm Frost & Sullivan by 2021, AI systems will
generate $6.7 billion in global healthcare industry revenue. In
2014, they only generated $634 million—that’s a 40 percent
compound annual growth rate.
Investment into these technologies is booming. In 2014, the cross-
industry average revenues’ spending on IT was 3.3, but for
healthcare providers, the average spent was 4.2 percent. Around 35
percent of healthcare organizations will implement artificial
intelligence solutions within the next two years — and over half of
them plan to follow suit within the next five years. I’ve analyzed
the trend and recognized that an increase has been driven by a
variety of needs specific to running any business in healthcare,
including the need to create electronic healthcare records that are
secure enough to comply with privacy laws. Between 2012 and
2017, penetration of electronic health care records grew from 40 to
67. I find this statistic crucial for the future of machine learning in
healthcare because the availability of data is necessary for the
further advancement in this topic.
At the same time, healthcare AI deals also grew in significant
numbers, expanding from 20 such deals in 2012 to almost 70 by
mid-2016. Venture capital investment in AI-driven medical
technologies has also exploded, an open recognition of the area as
promising one that is likely to deliver, at least in the eyes of tech-
savvy money people. In 2012 AI-driven healthcare projects such as
robotics, machine learning (ML), and computer vision totaled $30
million; in 2016, that area of investment topped $892 million.
According to a report from Accenture, the greatest near-term value
for healthcare businesses right now exists in these top three
applications:
robot-assisted surgery ($40 billion)
virtual nursing assistants ($20 billion)

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administrative workflow assistance ($18 billion)
However, other areas I examined, such as medical imaging are also
very promising—particularly because they are meeting such
tremendous needs. For example, right now more than half of the
world’s population has no access to medical imaging because those
technologies that are not AI-assisted are expensive, unwieldy, and
demand impractical levels of training. In total, Accenture estimates
that AI will be able to address at least 20 percent of unmet clinician
demand by 2026.
The takeaway: more money is going into practical AI-driven
applications in the healthcare industry because those applications
are generating revenue. And that’s is just a beginning.
Specific applications
To get a better sense of how AI and machine learning are
transforming the healthcare industry now, it’s useful to consider
specific cases. That is why I have gathered some of the more
fascinating applications of the technologies in healthcare right
now, which also demonstrate the practical value of these cutting-
edge technologies.
Identifying tuberculosis in the developing world
Identifying patterns in images is in my opinion one of the strongest
points of existing AI systems, and researchers are now training
AI to review chest X-rays and identify tuberculosis. This
technology could bring effective screening and evaluation to TB-
prevalent regions that lack radiologists.
AI for treating war veterans with post-traumatic stress disorder (PTSD)

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The Tiatros Post Traumatic Growth for Veterans program
partnered with IBM Watson to use AI and analytics to ensure more
veterans with PTSD would complete psychotherapy. Using these
technologies, they achieved a 73 percent completion rate, up from
less than 10 percent. As many as 80 percent of veterans with PTSD
who finish a treatment program within a year of diagnosis can
recover, according to statistics from the Department of Veterans
Affairs. Approximately one in five of the 3 million veterans of
Afghanistan and Iraq wars suffer from PTSD.
Detecting brain bleeds
Israeli healthcare tech company MedyMatch and IBM Watson
Health are using AI to help doctors in hospital emergency rooms
treat stroke and head trauma patients more effectively by detecting
intracranial bleeding. The AI systems use clinical insights, deep
learning, patient data, and machine vision to automatically flag
potential cerebral bleeds for physician review.
Optimizing administrative workflow and eliminating waiting time
Administrative and assistant work is a prime area for
AI. According to Accenture, timesaving workflow capabilities
such as voice-to-text transcription have the potential to eliminate
tasks like ordering tests and prescriptions and writing notes in
charts for medical professionals—anything that concerns non-
patient care. This amounts to a savings of 17 percent of doctor
work time and a whopping 51 percent of registered nurse work
time.
AI could also prioritize doctor emails and assist patients in
resolving simple medical issues without the help of doctors,

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optimizing schedules from both sides. For example, the startup
Scanadu’s doc.ai natural language processing program allows
patients to get their lab results explained to them by an app, saving
both patient and doctor time and money. Nuance Communications
has unveiled a similar product in the form of a virtual assistant that
can explain test results and deal with basic patient concerns. The
healthcare organizations that implement these technologies first
will see the most benefit because they will have the most time
to build knowledge libraries.
Detecting Alzheimer’s disease
It now takes AI-enabled robots less than one minute to diagnose
Alzheimer’s disease with about 82 percent accuracy based on
speech patterns and voice—and that level of accuracy is only
growing. The AI systems can attend to the length of pauses between
words, any preference for pronouns over proper nouns, overly
simplistic descriptions, and variations in speech frequency and
amplitude. While all of these factors are very tough for human
listeners to note and detect with high levels of accuracy, AI systems
are objective and quantifiable in their analysis.
Cancer diagnosing
Traditional methods for detecting and diagnosing cancers include
computed tomography (CT), magnetic resonance imaging (MRI),
ultrasonography, and X-ray. Unfortunately, many cancers cannot
be diagnosed accurately enough to reliably save lives with these
techniques. Analysis of microarray gene profiles is an alternative
but relies on many hours of computation—unless that analysis is
AI-enabled. Stanford’s AI-enabled diagnostic algorithm has now
been proven just as effective at detecting potential skin cancers
from images as a team of 21 board-certified dermatologists. Startup
Enlitic is employing deep learning to detect lung cancer nodules in
CT images—and their algorithm is 50 percent more accurate than
an expert thoracic radiologists working as a team.
Other healthcare companies are going past diagnosis and on to
treatment and even cures with the help of AI. Insilico Medicine is
finding new drugs and treatments with deep learning algorithms,
including new immunotherapies. These gene therapies use the cells

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of each individual patient to model their own biology and immune
systems.
AI makes these cures work because it can design combination
therapies and identify incredibly complex biomarkers by
performing millions of experiments in simulated form at lightning
speed.
Robo-assisted surgery
When it comes to value potential, robot-assisted surgery is at the
head of the AI-enabled class. AI-enabled robotics can enhance and
guide the precision of the surgical instrument by integrating real-
time operating metrics, data from actual surgical experiences, and
information from pre-op medical records. In fact, Accenture
reports that these advances made possible by AI-enabled robotics
include a length of stay reduced by 21 percent.

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Studying various solutions, I find Mazor Robotics most promising.
It is using AI to minimize the invasiveness and maximize the
customization of surgical operations on areas with complex
anatomy—such as the spine. The AI system helps the surgeon plan
where implants will be placed using CT scans before the patient is
present, and Mazor’s robot arm for spinal surgery guides the
movement of the surgical instruments, ensuring a high degree of
precision.
Worse yet, it gets smarter!
We shouldn’t get too excited about how Machine Learning and AI
are going to change the healthcare as we know it. There is a lot of
work to be done by people if we want to see those changes
happening. In fact, the human factor is crucial for success in this
case. Moreover, pumping up the topic may create unnecessary
pressure on parties involved in the process.
Technology is great. But people and process improve care. The best
predictions are merely suggestions until they’re put into action. In
healthcare, that’s the hard part. Success requires talking to people
and spending time learning context and workflows — no matter
how badly vendors or investors would like to believe otherwise. It
would be fantastic if health care could be transformed by installing
software that assumed your workflows and priorities.
The bottom line
There have always been cases of over-hyped technologies
throughout history, but AI and machine learning are absolutely not
among of them.
Even as we stand at the nascent edge of the technology, with its
potential only barely understood, the healthcare industry is
experiencing an influx of productivity and revenue thanks to AI
and machine learning.
Most major healthcare players are already investing in AI,
recognizing major role of this technology in the future of the
industry. Where it takes us from here will be exciting to see, but a
well-informed, studied opinion with the right knowledge will have
the best chance of predicting its path.

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