IBM Watson, Google Health, SyncThink, and Aprecia are emerging healthcare technology companies developing applications of artificial intelligence, virtual reality, and 3D printing. IBM Watson uses natural language processing to match cancer patients to clinical trials. Google Health developed an AI algorithm to detect diabetic retinopathy from retinal scans. SyncThink uses virtual reality eye-tracking to identify brain impairment from concussions. Aprecia 3D prints personalized drugs with customized dosages and release mechanisms. These companies are poised for growth in the next five years as their technologies improve healthcare delivery and outcomes.

1. Health Technology Forecasting
Introduction
The use of technology in healthcare can be traced as early as the 10th century BC with
the finding of Egyptian wooden and leather toe prosthesis1
. Over time technology has been
used increasingly to improve the efficiency and delivery of healthcare. The primitive inventions
of technology have now led to massive innovations that constitute our present healthcare
systems. From stethoscopes to complex machines such as surgical robots, technology is now
used in all aspects of medicine and healthcare. Presently, we are seeing immense growth in the
research and development for specific applications of artificial intelligence, virtual reality, and
3D printing. In this article, we will explore some of these emerging technologies and their near-
term applications in healthcare.
Artificial Intelligence
Artificial Intelligence (AI) is a broad branch of computer science with the goal of creating
systems that can perform human-like functions intelligently and independently. Subdomains of
artificial intelligence include symbolic learning, machine learning, natural language processing,
neural networks, and deep learning. Each of these subdomains is used to create systems that
can exceed human activities, for example, natural language processing reflects how we read
and write in a language. AI is used in healthcare to improve decision making, disease diagnosis
and management, early detection of diseases, drug discovery and research2
.
AI Use Case #1
One application of AI is with the use of natural language processing for matching
participants to clinical trials. There are about 33,000 cancer trials registered in the US and
currently, less than 5% of US adults with cancer take part in clinical trials. The problem is in
identifying the right patients with clinical attributes that meet the highly specific eligibility
requirements of clinical trials. Most oncologists require time in narrowing down specific trials to
manually find matches that may be beneficial to their patients. This process requires reviewing
vast amounts of electronic medical record (EMR) information, 80% of which is unstructured
data, to find appropriate matches3,4,5
. As a result, this process of matching is slow, lengthy and
challenging.
With natural language processing, the machine can read, understand and derive
important information from patient data and clinical trial eligibility criteria. This technology
eliminates the need to manually screen through trial data and as a result saves clinician time. It
also allows faster patient enrollment which ultimately reduces the length of the study.
Improved matching means that patients have better access to clinical trials and the trial sites
are able to meet their enrollment targets4,5
.
Company #1
In order to tackle this ineffective manual method, IBM Watson developed an Oncology
Clinical Trial Matching (Watson CTM) program that uses natural language processing. It allows
clinicians to easily get a list of clinical trials that a patient is eligible for. On the other hand, it

2. can also be used to find all the patients that meet the criteria for a specific clinical trial. The
program is able to analyze, without any manual intervention, large amounts of structured and
unstructured data in EMR and find matches based on the inclusion and exclusion trial criteria
from clinicaltrials.gov. Watson is able to interpret important pieces of patient information by
analyzing lab results, mutation charts, tumor status, etc. This significantly lowers the time spent
on daily screening and extensive amounts of paperwork.
IBM has conducted customer case studies to see the practical application of its
technology in a real setting. A study published in the Journal of Clinical Oncology found that
Watson CTM was able to assign patients to breast cancer trials with an accuracy of 87.6% and
to lung cancer trials with an accuracy of 74.9%3
. Another IBM Watson study found that the use
of the AI system decreased the time required to screen patients by 78%6
. Currently, this
technology is being used by the Highlands Oncology Group and Nuvance Health, both of whom
have seen positive results after incorporating the Watson CTM into their processes4
. Other
healthcare and research organizations have also shown interest in implementing this
technology.
Other companies such as Deep 6 AI and Oracle could be potential competitors in the
upcoming years. IBM Watson has been able to provide more scientific support for the
functionality of its technology as compared to others in this space6
.
In the next 5 years, there is a good scope for IBM Watson Oncology Clinical Trial
Matching technology and other similar AI programs to be integrated into more healthcare and
research practices. IBM Watson Health has predicted the number of cancer patients to increase
to 23 million people in the next 10 years. Therefore, the value of the product will only increase
as manually screening patients is an extremely inefficient method.
AI Use Case #2
Another application of AI is with the use of deep learning and image recognition for
disease diagnosis. Diabetic retinopathy is a medical condition that can cause blindness and
vision loss in people with diabetes mellitus7,8
. High blood sugar in the blood vessels of the retina
causes them to leak fluid or bleed which can result in permanent damage. With 422 million
people with diabetes in 2014, there are a large number of people at risk for blindness
worldwide. Optical coherence tomography (OCT), an imaging technique that uses light waves to
create 3D images of the retina, is currently used by eye care professionals to diagnose eye
conditions7
. The challenge lies in the sheer number of retinal scans that need to be analyzed by
trained clinicians. For example, at Moorfields Eye Hospital in London, England, healthcare
professionals need to analyze over 1000 retinal scans a day. The time required to collect and
analyze all these scans can lead to delays in patient care, which at times could cost them their
sight9,10
.
With the use of deep learning, a model can be trained, using retinal scans, to identify
markers of diabetic retinopathy or other eye diseases. This model will have to be used in
concert with an OCT or another similar imaging device. AI can considerably reduce the
workload of ophthalmologists for screening patients and allow them to focus more on treating
patients with eye diseases. Also, it can analyze and grade a retinal scan much faster than

3. manual analysis, and as a result, it minimizes the disease exacerbation due to delays in the
analysis10,11
.
Company #2
Google Health has conducted research on the use of artificial intelligence in predicting
and diagnosing cancer, preventing blindness and predicting patient outcomes. In order to
address the problem of Diabetes Retinopathy, Google Health developed an AI algorithm that
can detect retinopathy from OCT scans10,11
. The model was trained using more than one million
retina scans which were manually reviewed and rated by ophthalmologists. The graded images,
once fed into the AI algorithm, allow it to understand signs of the disease such as swelling,
hemorrhaging and nerve tissue damage. This application is called the Automated Retinal
Disease Assessment (ARDA) and it provides instant analysis of diabetic retinopathy8,9
.
A 2016 study published in the Journal of the American Medical Association found that
the AI algorithm had high sensitivity and specificity for detecting diabetic retinopathy.
Subsequent clinical trials conducted in India, which has over 60 million diabetics and a shortfall
of ophthalmologists, confirmed the results in the JAMA study showing that the model
performed on par with screening done by ophthalmologists12,13,14
. Since then, further research
has been conducted to test the application of ARDA for detecting and predicting other health
conditions.
Other companies, such as PathAI, Freenome, and Enlitic, that use AI for radiology and
cancer diagnoses are direct competitors to Google Health. Google’s previous success in breast
cancer detection, the ability to conduct large scale research internationally and partnerships
with healthcare-focused companies have allowed it to grow and develop it's technology rapidly.
In the next 5 years, with more clinical testing on the way, Google Health has a good
likelihood of getting FDA approval. In addition, DeepMind Health (which is now part of Google
Health) has also developed and tested an AI system that can be adaptable to different types of
eye scanners and be used even when the scanners are updated or replaced over time. This
system also has the possibility of being used in the detection of over 50 other eye diseases in
the future.
Virtual Reality
Virtual reality (VR) is the use of computer technology to create a simulated experience
that can be interacted with. Virtual reality is being used in the healthcare industry in several
ways including medical training, for coping with mental health, pain management, physical and
cognitive rehabilitation.
VR Use Case #1
An application for VR is in identifying impaired function or synchronization and
informing timely intervention for individuals with a concussion. A concussion is a brain injury
that is caused by impact to the head or body that leads the head and brain to rapidly move back
and forth. Such sudden movement can potentially induce symptoms such as headache, nausea,
dizziness, balance and concentration problems. Generally, baseline testing is conducted by a
trained healthcare professional at the start of the athlete’s season. This testing includes balance

4. and cognitive assessments as well as neuropsychological tests which to assess the athlete’s
concentration, memory and reaction time. The problem for making a ‘return to play’ decision
arises when some symptoms of concussion are not easily identifiable and when the
neuropsychological tests are not interpreted appropriately15,16
.
Company #1
The SyncThink platform offers six assessments that can be used to detect visual
impairment. Eye-Sync is an FDA cleared non-invasive mobile eye-tracking platform that can
objectively identify impairment related to the predictive oculomotor and vestibulo-ocular
function. Eye-Sync can be used to perform baseline screenings and measure the impact of brain
performance throughout the season. It utilizes a virtual reality goggle that contains high fidelity
research-grade infrared cameras and emitters. These components work together with novel
software to provide a light-based stimulus to capture and record the movement of each eye
while the patient performs a series of 60-second assessments. This technology uses proprietary
algorithms for each assessment to quantify the variance of predictive eye movements. This can
then be interpreted by a trained clinician to support medical diagnosis and to develop a
treatment plan. Eye-Sync can then also be used to target and train specific deficiencies to
improve dynamic vision and resolve impaired function15
.
SyncThink has conducted, in collaboration with the Brain Trauma Foundation and the US
Department of Defense, extensive scientific research over 15 years demonstrating the safety
and accuracy of the device. The Eye-Sync technology got FDA approval in 2016 for visual
impairment identification through eye movement analysis15
.
A competitor of this company is InSight by Saccade Analytics which has a similar
application of VR technology. SyncThink’s rigorous scientific research gives it a leg up on other
similar products that are either already available or newly entering the market.
The future outlook for SyncThink over the next five years looks promising as the
company expanding and attracting large organizations. With its application in healthcare,
military, and sports, SyncThink has a lot of room for growth.
3D Printing
3D printing is a manufacturing method that uses additive processes for making 3-
dimensional objects from computer models. There are several medical uses for 3D printing
including organ/tissue printing, creation of prosthetics, anatomical models, drugs, and
implants.
3D Printing Use Case
One application of 3D printing in the biotech industry is the 3D printing of medicines.
Oral tablets are normally made through the processes of mixing, milling, and granulation of
powdered ingredients that are compressed into tablets. At each of these steps, there is a risk of
drug form change or degradation which can lead to batch failures. This traditional
manufacturing practice is not suitable for creating drugs with customized dosages, new drug
release mechanisms or prolonged stability. 3D printing of drugs can allow the medication to be
personalized to the patient’s pharmacogenetic profile. Drugs can be made to have higher dose

5. loads, be fast-dissolving and have taste masking. The layer by layer printing allows for the
addition of barriers between active ingredients which can facilitate controlled drug release17
.
An online survey of 1002 American adults found that 50% of them reported difficulty
swallowing tablets or capsules; an issue that can be addressed by the printing of easy-to-
swallow tablets18
.
Company
Aprecia is the first and only pharmaceutical company that has developed an FDA-
approved formula that enables oral drugs to be 3D printed on a commercial scale. The ZipDose
3D Printed (3DP) technology manufactures high dosage medications that can contain over 1000
mg of active pharmaceutical ingredients17
. The highlight of this technology is that it allows rapid
dispersal of the medication once it comes in contact with an aqueous solution. This makes
ingesting oral medications an easier process because it allows for pre-gastric absorption. The
compounds used to 3D print also allows manufacturers to mask a wide range of tastes that are
produced by pharmaceutical ingredients. The science behind the formulation of ZipDose was
based on the simple fact that all drugs are produced through the process of layering. The 3D
printer first prints the base layer of the powered pharmaceutical blend. Then the printer uses
an aqueous fluid solution to bind the powders and prepare it to adhere to the next added layer.
This process occurs multiple times to form a solidified yet porous, orodispersible medication.
This technology redefines pharmaceutical drugs as it can accommodate a wide range of new
patient populations. It is also beneficial to a wide range of adaptive clinical trials as the printing
of the product occurs in real-time17,19,20
.
At the present time, Aprecia pharmaceuticals is the only company utilizing 3D printing in
the production of medications. The development of this technology required more than 8 years
of research and testing; therefore, this company won't have any immediate competition in the
near future. In the next 5 years, this company can leverage its 3DP technology to attract
pharmaceutical companies and research organizations.
In summary, emerging technologies in domains of artificial intelligence, virtual reality,
and 3D printing are redefining the future of many health care streams and we will be seeing
much more of them in the near future.
Investment Split
IBM Watson Clinical Trial Matching Google Health SyncThink Aprecia

6. References
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sheets/detail/diabetes
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7. 17. Aprecia: 3D Printing in Medicine. (n.d.). Retrieved from https://www.aprecia.com/
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technology/what-is-zipdose-technology
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Projected Uses. Retrieved from
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