It is no secret that artificial intelligence is shaping new business landscapes in every industries. As one of emerging convergence technologies, Artificial Intelligence (AI) creates new products and services, finally innovating business models. Especially, it has been noted by industry experts and researchers that healthcare sector has the biggest potential of AI convergence. In fact, major technology companies including Google, Microsoft and IBM have invested in AI in healthcare sector. Thousands of AI startups are active launching innovative services related to healthcare.

1. Artificial Intelligence Service in Healthcare
Business Consulting Center (BCC) Korea
June, 2018
Sung-Bin Yoon & Art Choi
Global Startup Case Study

2. 2
Background
It is no secret that artificial intelligence is shaping new business landscapes in every industries. As
one of emerging convergence technologies, Artificial Intelligence (AI) creates new products and
services, finally innovating business models. Especially, it has been noted by industry experts and
researchers that healthcare sector has the biggest potential of AI convergence. In fact, major technology
companies including Google, Microsoft and IBM have invested in AI in healthcare sector. Thousands of
AI startups are active launching innovative services related to healthcare.
The main objective of this study is to enhance understanding of AI based service innovation. The
study analyzes AI convergence services created by startups as multi-dimensional modes of service
innovation. Accordingly, the main research questions are:
How AI innovates service model in healthcare sector?
This study will provide a framework for analyzing the role of AI in convergence service innovation
and how it can lead to business model innovation.
This study monitored more than 100 AI startups in healthcare sector (sourced from CB Insights,
Crunchbase and etc). With the literature review about AI applications, our research team categorized AI
convergence healthcare service into four segments and thirteen sub-segments.

3. 3
Conceptual framework for case study
This study will adopt the theoretic framework about convergence service innovation process argued by
former research (Yoon, 2017). It assumed service innovation typologies are applicable to the analysis of
convergence service. In this regard, the study proposed four dimensional service innovation model
including convergence technology, service product innovation, service process innovation and business
model innovation.

4. 4
AI healthcare service segmentation
Medical Research
Clinical Care
Hospital Management
Personal Healthcare
AI
Patient data analytics
Re-engineer human genome
Drug discovery
Medical imaging/diagnostics
Screening neurology
Robot assisted surgery
Virtual nursing assistant
Patient feedback manage
Patient experience manage
In-patient clinical monitor
Healthcare provider
Chatbot & applications
Recognizing deteriorating patients
Wearable applications

5. 5
Patient Data Analytics
Segment 1

6. 6
AI startups in patient data analytics
Company Service
Ayasdi
Use AI to automate business processes, enables intelligent applications that augment and even surpass human
capabilities, make new discoveries in big data and high dimensional data.
Apixio AI Coding Solution for massive health records.
Jvion
AI software to predict and prevent patient-level diseases and financial losses. Big-data enabled predictive analytic
software solutions that combine clinical intelligence with machine learning.
Lumiata
Real-time AI predictive analytics that help hospital networks and insurance carriers to provide care to more
patients in less time.
Flatiron Health
Develops AI software that connects community oncologists, academics, hospitals, life science researchers, and
regulators on a shared technology web platform.
Wellframe
AI personalized care protocols for every patient and communicates them via their mobile app. Delivers a set of to-
do lists and allows patients to directly interact with the caregiver.
Welltok
A reliable health optimization software that leverages personalized, data-driven approaches to connect all
consumers with the relevant benefits, rewards, and resources.
A.I Med
AI Software platform to help practices to manage billing, electronic records, electronic claims, document imaging,
speech dictation, and paperless scheduling.
Healint
Deep analytics and machine learning to uncover insights from wealth of data to improve patient outcomes and
expedite clinical trials.
CloudMedX Clinical AI to improve patient records and use of machine learning organize patient data.
H2O.AI
Use of AI platforms for patient care, preventative care, and patient outcomes.
Use of AI to automate machine learning and deep learning solution for massive medical records
Health Catalyst AI-Powered Healthcare Benchmarking and Performance Improvement Solution

7. 7
Case study - Apixio
Company Overview
AI Tech Service Innovation Business model Innovation
Year founded HQ location Key service Website
2009 San Mateo, CA, USA
AI coding solution for
massive health records
www.apixio.com
 Apixio uses AI to analyze
massive sets of medical
documents and coded data
 It uses machine learning, natural
language processing (NLP), and
neural networks
 It gathers PDF’s, wellness data,
claims, flat files, and other
documents to compile and
organize it into its data acquisition
platform
 Apixio solution help code more
accurately in less time- up to four
times faster than traditional chart
review methods
 For Hierarchical Condition
Category (HCC) coding, on
average, a person can do 24
charts per day with 48 HCCs, but
with Apixio, one would be able
to code 160 HCCs opportunities
per day
 Apixio has implemented its
business model to offer its
systems to healthcare providers,
and insurers to be subscription
based, pay as you go, and
performance based.
 Apixio now boasts 33 customers,
including 5 national health plans
and 9 Blue Cross Blue Shield
Association plans

8. 8
Case study - Apixio
“Health plans are striving for greater accuracy in their chart review and risk adjustment
processes than ever before. However, traditional coding processes are no match for tod
ay’s increasing workloads and strict industry standards,”
James P. (Jim) Bradley, Board Chairman, Apixio

9. 9
Case study - Ayasdi
Company Overview
AI Tech Service Innovation Business model Innovation
Year founded HQ location Key service Website
2008 Palo Alto, CA, USA
Clinical variation
management
www.ayasdi.com
 With EMR system, hospitals get
tremendous amount of patient data
 Typically, managing variation is
highly manual and labor intensive
 Ayasdi’s solution using machine
learning simplified clinical
variation management. It helps
hospitals to identify areas of
unwarranted variation and surfaces
new best practices
 Ayasdi’s clinical variation
management solution can offer
below benefits;
1) Discover what’s going on in the
hospital
2) Identify best care practices
3) Build new care paths for different
patient groups
4) Implement best practices into
care coordination systems
5) Provide continuous improvement
care
 Ayasdi’s software is licensed on
an annual subscription basis,
 It can be deployed via centralized
cloud service, or via an on-
premise, private cloud installation

10. 10
Re-Engineering in Human Genome
Segment 2

11. 11
AI startups in genomics
Company Service
Deep Genomics
 Uses AI and machine deep learning to trace potential genetic causes for disease.
 Apply AI techniques to medicine and drug development driven by the emergence of powerful
new algorithms, but also by cost-effective new ways of sequencing whole genomes, the entire
readout of a person’s DNA.
Desktop Genetics
 AI that aids in experimental design and in data interpretation.
 DESKGEN AI powers DESKGEN CRISPR Library product range, which enables the work of
pharma, biotech and academic customers working in drug discovery, functional genomics, and
cell therapy
Pathway
Genomics
 Genetic Screening for patient care
 The program is developing a smartphone app that merges artificial intelligence and deep
learning with personal genetic information. The app provides users with personalized health and
wellness information based on the individual’s health history
Freenome
 AI genomics company seeking to empower everyone with the tools they need to detect, treat,
and ultimately prevent their diseases.

12. 12
Case study - Deep Genomics
Company Overview
AI Tech Service Innovation Business model Innovation
Year founded HQ location Key service Website
2015 Toronto, Canada Develop genetic medicines www.deepgenomics.com
 It uses deep learning, or very large
neural networks, to analyze
genomic data.
 Identifying one or more genes
responsible for a disease can help
researchers develop a drug that
addresses the behavior of the faulty
genes.
Deep Genomics provide therapy
and medicine discovery platform
which combines advanced
biological knowledge and data
with AI system
 It enables to efficiently find drug
candidates targeting the genetic
determinants of disease at the level
of RNA or DNA.
 Deep Genomics provides the
platform with pharma companies
on drug development
 On September 25, 2017, it
received a USD $13 million
equity investment led by Khosla
Ventures, accompanied by early
stage investment firm True
Ventures

13. 13
Case study - Freenome
Company Overview
AI Tech Service Innovation Business model Innovation
Year founded HQ location Key service Website
2015 South San Francisco, CA, USA Early cancer detection www.freenome.com
 It uses AI for decoding the vast
complexity of the cell-free genome
 By training on thousands of
cancer-positive blood samples,
AI genomics platform learns which
biomarker patterns signify a
cancer’s stage, type, and most
effective treatment pathways
Freenome’s artificial intelligence
(AI) platform is poised to detect
cancer at its earliest stages and
help clinicians optimize the next
generation of precision therapies
 Freenome’s AI allows it to process
all of the cfDNA in the blood
 Freenome is conducting the first
clinical validation study of an AI-
Genomics Blood Test
 It plans to bring a blood-based
cancer test to market in 2018
 The product will make early-
stage detection and treatment of
colorectal cancer, a reality for
millions of patients

14. 14
Drug Discovery
Segment 3

15. 15
AI startups in drug discovery
Company Service
Atomwise
 Atomwise created its own neural network called AtomNet, which uses deep learning to simulate
the creation of new drug molecules. The AI also makes predictions about the compounds,
including potential side effects, toxicity, effectiveness and so on.
MedAware
 Using AI to eliminate prescription errors. MedAware’s machine-learning algorithms mine data
gathered from millions of electronic medical records to detect outliers in prescription behavior
and flag them in real-time to healthcare providers.
NuMedii
 NuMedii has built AIDD(Artificial Intelligence for Drug Discovery) that harnesses Big Data and
AI to rapidly discover connections between drugs and diseases at a systems level.
Numerate  Using the power of cutting-edge AI to revolutionize small molecule drug design
twoXar
 The convergence of big data, cloud computing, and artificial intelligence has allowed twoXAR
to build a drug discovery platform that is order of magnitudes faster, cheaper, and more accurate
than traditional wet-lab based approaches.
Recursion
Pharmaceuticals
 It combines AI with automation to conduct experimental biology at scale — testing thousands of
compounds on hundreds of cellular disease models in parallel.
BenevolentAI
 BenevolentTech is developing an advanced AI platform that helps scientists make new
discoveries and redefines how scientists gain access to, and use, all the data available to them to
drive innovation. It is built upon a deep judgement system that learns and reasons from the
interaction between human judgement and data.
Cyclica
 Cyclica harnesses biophysics, bioinformatics and AI to help pharmaceutical companies navigate
the drug discovery pipeline by assessing the safety and efficacy of drugs.

16. For more information and on demand research, please contact
U.S.A. Office
Mark Liu
Tel:+1-6262952442
E-mail: liujunjie@qyresearch.com
China Office
Simon Lee(Zhang Dong)
Tel : +86-1082945717
E-mail : zhangdong@qyresearch.com
South Korea Office
Sung-Bin Yoon, Ph. D
Tel : +82-10-7551-1278
E-mail : yoon@qyresearch.com
Japan office
Tang Xin
Tel:+81-9038009273
E-mail: tangxin@qyresearch.com