AI and Robotics Join Forces to Revolutionize Health Care
Health care is one economic sector the second wave of the Fifth Techno-Economic revolution will make an impact on the lives of almost everyone.
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care is one sector of the economy where the second wave of the Fifth
Techno-Economic revolution will make an especially deep impact on the lives of
almost every human being.
Waterhouse Coopers, or PWC, recently identified five converging trends leading
to an era in which AI and robotics will dominate health care.
and foremost is the escalating health care value challenge that
all countries across the globe face. It involves demands created by
chronic disease, rising costs, an aging population and limited financial &
human resources. Ironically, our economies are continuing to invest in
facilities and equipment that were built to address a completely different set
of healthcare needs and are not designed to cope with the new demand profile. For instance, a "hospital-centric system" deals very well with serious health episodes that
require days or weeks of acute care for very ill people. But it was never
intended to deal with large numbers of people whose conditions are chronic,
complex and require treatment for the longer term.
the past decade has seen an explosion in the amount of
health data that is available to us. For example, for a skin
specialist there are 11,000 new dermatology articles published every
year. In 2013, it was estimated that the volume of health-related data
had reached over four zettabytes; that's four trillion gigabytes (or 1021 bytes). And there are those who project it will rise by a
factor of ten times by 2020, and, to beyond a yottabyte (or 1024 bytes) by 2040. Furthermore, fully
80% of this extraordinary amount of data is unstructured, meaning that it's not contained in a database or some other
type of data structure. Staying current with and being able to access
this data is simply beyond the scope of any human individual, no matter how capable
information technology development in healthcare has been rapidly
moving from products to platforms to solutions. Past decades have focused
on the innovation provided by medical products delivering evidence-based care. The present decade is one of
medical platforms focused on real-time, outcome-based care. And the next decade is moving
towards medical solutions - using AI and robotics, as well as virtual and
augmented reality - to deliver intelligent solutions for both evidence-based
and outcome-based health care and focusing on collaborative, preventative care. This confluence of
technology-based products, platforms and solutions is leading to the previously
impossible world of "precision medicine," personalized at the individual
level. This new paradigm may even enable doctors to predict and
thereby prevent disease from ever occurring.
information technology has obviously had an enormous impact far beyond the
developments in healthcare. The explosion of digitally enabled, wireless
connectivity across increasingly mobile devices has created an increasing democratization of access to healthcare.
Some of the most powerful AI tools are already embedded in Android or iOS
devices. Harnessing this technology is providing consumers with the data
and information they need to proactively manage their own health and wellness,
and to make better, more informed decisions in partnership with their
there's the trend toward the general-public becoming active participants in their own health and wellness. Significantly,
this participation level has now reached critical mass. The explosion of
technology and the increasing ubiquity of the Internet of Things is bringing
about breakthroughs that are erasing healthcare boundaries and enabling care
anywhere and everywhere. Furthermore, consumer acceptance is
increasingly extending into the areas of AI and robotics, which are the subject
of this segment.
to PWC, market opportunities for AI and Robotics in health care can be broken
down into eight functional areas: keeping well, early detection, diagnosis,
decision-making, treatment, end of life care, research, and
training. Just the AI component across these eight areas is
expected to grow to $6.6 billion worldwide by 2021.
start with keeping well. One of AI's
biggest potential benefits is to help people stay healthy so they don't need a
doctor, or at least not very often. The use of AI and the Internet of Medical
Things (or IoMT) in consumer health applications is already helping people to
manage their own healthcare and to keep themselves well through healthier
living. For example, the Smart Belt has a built-in mechanism that alerts the
person when they overeat. It relies on a magnetic sensor to track waste size
and tension to determine when the users may have over eaten and alerts the
wearer. Further, IBM has announced three new consumer-focused partnerships, one
of which is with Under Armour which will use Watson to power a cognitive
applications and others all encourage healthier behavior in individuals and
help with the proactive management of a healthy lifestyle. It puts consumers in
control of their health and well-being. Additionally, AI increases the ability
for healthcare professionals to better understand the day-to-day patterns and
needs of the people they care for, and with that understanding they are able to
provide better feedback, guidance and support for staying healthy, even as the
sheer volume of data becomes increasingly difficult for humans to deal with, or
make sense of.
detection is another enormous
area of opportunity. AI is already being used to more accurately detect
diseases, such as cancer, in their early stages. Increased accuracy is
key. Just consider one example: according to the American Cancer Society,
12.1 million mammograms are performed annually in the US, but a high proportion
of these mammograms yield false results, leading to 1 in 2 healthy women being
told they have cancer. The use of AI is enabling review and translation
of mammograms 30 times faster with 99% accuracy, reducing the need for
unnecessary biopsies as well as reducing the uncertainty and stress of a misdiagnosis.
proliferation of consumer wearables and other medical devices combined with AI
is also being applied to oversee early-stage heart disease, enabling doctors
and other caregivers to better monitor and detect potentially life-threatening
episodes at earlier, more treatable stages.
the horizon, Microsoft is developing computers programmed for use at a
molecular level to start fighting cancerous cells as soon as they are detected.
They are also doing research into using AI to interpret online search engine
behavior, for example, at the point where someone might research symptoms
online long before they approach their physician.
consider the critical area of diagnosis.
It's estimated that 80% of health data is invisible to current systems because
it's unstructured7. IBM's Watson for Health uses cognitive
technology to help healthcare organizations unlock vast amounts of health data
and use it to drive to a sound diagnosis. Watson can review and store far
more medical information, including every medical journal article, every case
study of treatment, and every symptom from around the world exponentially
faster than any human. And it doesn't just store that data, it's capable of
finding meaning in it. Unlike humans, its decisions are all evidence-based and
free of cognitive biases or overconfidence, enabling rapid analysis and vastly
reducing or even eliminating misdiagnosis.
Google's DeepMind Health is working in partnership with clinicians, researchers
and patients to solve real-world healthcare problems. The technology combines
machine learning and "systems neuroscience" to build powerful general-purpose
learning algorithms into neural networks that mimic the human brain.
diagnosis and treatment lies the crucial area of "decision-making" in
which the patient and physician decide which course of treatment to
follow. Improving care requires the alignment of broad-based data
analysis with appropriate and timely decisions, and predictive analytics that
can support clinical decision-making and actions as well as prioritize tasks.
Using "system-dynamics-driven pattern recognition" to identify patients at risk of
developing a condition - or seeing their health deteriorate due to lifestyle,
environmental, genomics, or other factors - is another area where AI is
beginning to take hold in healthcare. For example, in an extension and
application of AI, PwC's Bodylogical system captures learning through
mechanistic modeling to digitally represent the physiology of the human body.
This enables true-life simulations to predict the likely progression of chronic
diseases in the future, based on today's actions and interventions. These
simulations help pharmaceutical companies, providers, payers, employers,
researchers and consumers better understand how daily life choices and
therapeutics impact individual patients or population health outcomes and
the major decisions about the course of treatment are made, execution and
monitoring of the treatment are key.
Beyond scanning health records to help providers identify chronically ill
individuals who may be at risk of an adverse episode, AI can help clinicians
take a more comprehensive approach for disease management, better coordinate
care plans and help patients to better manage and comply with their
long-term treatment programs.
instance, AiCure has built an application to monitor patients with long-term
conditions and help them adhere to medication intake. The application uses a
visual recognition system to identify the patient's face, the medication
they're taking, and confirm ingestion. The data is then sent back to the
care provider or to a pharmaceutical company conducting a clinical trial.
here in the treatment phase that robots are likely to have the biggest impact.
Robots have been used in medicine for more than 30 years. From the first
programmable universal machine for assembly (or PUMA), used in urology surgery
in the 1980s, to the da Vinci robot, the most widely used robotic system in
clinical use today, robots have evolved to perform a wide range of tasks and
functions. They range from simple laboratory robots to highly complex surgical
robots that can either aid a human surgeon or execute operations by themselves.
In addition to surgery, they're used in hospitals and labs for repetitive
tasks, in rehabilitation, physical therapy and in support of those with
long-term conditions. RoBear is a nursing-care robot that is able to lift and
move patients in and out of bed into a wheelchair, helps those who need assistance
to stand, and even turns patients in bed to prevent bedsores.
some patients transition from normal treatment to end of life care. We are living much longer than
previous generations, and as we approach the end of life, we are dying in a
different and slower way, from conditions like dementia, heart failure and
osteoporosis. It is also a phase of life that is often plagued by loneliness.
have the potential to revolutionize end of life care, helping people to remain
independent for longer, reducing the need for hospitalization, human caregivers
and "care homes" by performing routine tasks such as taking vital signs and
prompting for medications. AI combined with the advancements in humanoid
design are enabling robots to go even further and have conversations and other
social interactions with the people; this keeps aging minds sharp and solves
helps solve the problems of loneliness and isolation. For instance, Kompai
robots talk, understand speech, remind people of meetings, keep track of
shopping lists and play music. Developed to assist the elderly in their own
homes, they are able to monitor them for falls and other health parameters,
provide alerts, and connect them via video-conference with healthcare
providers, friends and family.
foundation upon which modern medicine rests is, of course, research, and this is the aspect of health for which AI
is most well-suited. Every one of us has taken medications prescribed by
our doctors for symptoms or illnesses at some point in our lives. Those with
chronic diseases often depend upon medication to manage what might otherwise be
debilitating diseases. But the path from the research lab to patient is a
long and costly one.
to the California Biomedical Research Association, it takes an average of 12
years for a drug to travel from the research lab to the patient. Only five in
5,000, or 0.1%, of the drugs that begin pre-clinical testing ever make it to
human testing and just one of these five of those is ever approved for human
usage. Furthermore, on average, it will cost a company US $359 million to
develop a new drug from the research lab to the patient.
research and discovery is one of the more recent applications for AI in
healthcare. By directing the latest advances in AI to streamline the drug
discovery and drug repurposing processes there is the potential to
significantly cut both the time to market for new drugs and their costs, not
only for the labs who develop the drugs, but for those people whose health
depends upon them. One of the best examples is Pharma.AI, the Pharmaceutical
Artificial Intelligence division of Insilico Medicine, a bioinformatics
company. It's located at the Emerging Technology Centers at Johns Hopkins
University and was launched in March 2016. They focus on drug discovery
programs for cancer, Parkinson's, Alzheimer's, and other aging and age-related
health issues. However,
medical research is not just about finding new drugs to combat disease. It also
includes research into disease itself, with the ultimate goal being to
inoculate against or completely eliminate disease. A great example is
Meta, a Canadian start-up that uses AI to quickly read and comprehend
scientific papers and then provide insights to researchers. It was bought
by the Chan Zuckerberg Initiative in January 2017 as part of the charitable
foundation's mission to eradicate disease.
it's training that enables physicians and other medical
professionals to make optimal use of all of the available resources. AI allows
those in training to go through naturalistic simulations in a way that simple
computer-driven algorithms cannot. The advent of natural speech in technology
and the ability of an AI computer to draw instantly on a large database of
scenarios means AI can respond to questions, decisions or advice from a trainee
and can challenge them more effectively than a human can. And the training
program can learn from previous responses from the trainee, meaning that the
challenges can be continually adjusted to meet their learning needs.
training can be done anywhere with the power of AI embedded on a smartphone.
For example, AI can provide "quick catch-up sessions," in response to a tricky
case in a clinic or while traveling.
date, the main way new technologies have been used to augment training is
through virtual reality (VR). Combining VR with AI will offer boundless
opportunities for extending the skills of trainees in a targeted fashion.
and robotics are redrawing the healthcare landscape. The wave of innovation
being driven by these technologies is not only transforming clinical
decision-making, patient monitoring and care, and surgical support, but
fundamentally changing how we approach healthcare for our populations. We are
already experiencing this shift as we focus on integrating prevention and
wellness into our health systems and we are heading towards a time when people
work more proactively with their healthcare professionals across illness and
shift will inevitably alter many of the roles of healthcare professionals. As
these new technologies and perspectives become more integrated within and
across our healthcare systems and more ubiquitous among the population, the
skills that are required by our new health landscape may well be markedly
different than those that are needed today. We currently train our doctors and
nurses in the context of health systems that may no longer exist once they
graduate medical school. An understanding of technology will be imperative.
data analytics and human behavior may well be as much a part of the medical
curricula as anatomy and neurology.
and robotics technology will free up clinicians for other types of work that
enable them to spend more meaningful time with their patients. Rather than a
profession of 'healthcare providers', AI and robotics will open opportunities
for more holistic patient care, with a focus on keeping patients healthier
longer, instead of primarily treating illness.
will likely challenge the traditional role of the doctor. But, rather than
worrying if these technologies are going to replace doctors and other
healthcare professionals, we should be considering more deeply their wider role
in the entire healthcare continuum with a clear eye towards training our
healthcare workforce for the future.
short AI and robotics will dramatically enhance the cost-effectiveness of
health care. And the opportunities for patients, health care
professionals, and investors are enormous.
this trend, we offer the following forecasts for your consideration.
over the next decade there will be widespread acceptance of AI and robotics in
major health care roles by patients, worldwide. Exactly one year ago, in November
2016, PWC surveyed 12,000 people in 12 countries. The findings of the
survey revealed three key themes impacting consumers' willingness to engage
with AI and robotics in their health care:
as with most truly disruptive technologies AI and robotics in health care will
first achieve widespread adoption among less affluent consumers who have
fewer alternatives and subsequently come to dominate more sophisticated markets. Nearly 30 years ago, Harvard's Clayton
Christensen found that most truly disruptive technologies were adopted by
nontraditional consumers for whom the initial offerings were "good enough." The
PWC research clearly showed that in consumers in countries currently lacking
quality health care, like Nigeria, patients are eager to adopt AI and robotics
in every area from prevention and diagnosis to surgery. Patients in affluent
countries, with high quality medical care, like the UK and Germany, are far
- People are
increasingly willing to engage with AI and robots if it means better access to
healthcare, as seen with the UK Health System and Medicaid, free health care is
worthless if you can't get it when you need it;
and accuracy of diagnosis and treatment is a critical factor in this willingness; and
- Trust in the technology is vital for wider use
and adoption, even as the 'human touch' remains a key component patient want in
the healthcare experience.
several concerns will need to be resolved before governments, health care
professional, patients, technology developers, and institutional decision
makers embrace this new world of healthcare.
governments, the big challenge is
creating quality standards and a regulatory framework which are applicable to
and obligatory for the entire healthcare sector, as well as the appropriate
incentives for adopting new approaches. Linking regulations to facilities or
humans, will naturally inhibit adoption. Also, AI and robotics should be seen
as making healthcare more accessible and affordable. Otherwise there is a risk
that these technologies may become limited to the well-off.
healthcare professionals, the big challenge is understanding how AI and robotics have the
potential to work for and with them in a medical setting as well as throughout
the healthcare eco-system, and be open to change. If clinicians are not as good
as AI and robotics at monitoring, diagnosis, decision-making or surgery, then
what is the unique role for the human practitioner, and how can they prepare
patients and the general public, the big challenge is becoming more accustomed to artificial
intelligence and robots and discovering the benefits for themselves. Although,
we suspect just as they have already adopted AI in their everyday lives, health
technologies will similarly be taken up with alacrity.
the private sector developing AI and robotics solutions, the big challenge is ensuring that solutions
resolve the big issues of demand and resources that every health system faces.
In essence, by providing AI and robotic driven solutions, the private sector
has the opportunity to disrupt healthcare for the good.
decision-makers at healthcare institutions, the big challenge is developing an "evidence
base," measuring the success and the effectiveness of the new technology; and
implementing it in phases, while prioritizing and focusing on what consumers
want and need.
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