1. Artificial intelligence in healthcare
Dr Yuanrui Margaret Shi
7 July 2017

2. Contents Healthcare Technology innovation in healthcare
Healthcare Artificial Intelligence market
Artificial Intelligence and machine learning
Examples
Challenges
QuintileIMS

3. Healthcare
Patient care is a complex, sensitive and high risk activity and process
Complex
Patients see doctors with symptoms
Doctors spot the patterns and identify the variations from the norm
Doctors manage the diseases with different treatment options
Sensitive and high risk
Huge variation in disease presentation, and treatment options
Dealing with human beings
Healthcare is the improvement and maintenance of health
Encompasses disease prevention, diagnoses, treatment and rehabilitation with many different professionals involved
Patient care is a complex, sensitive and high risk activity and process
Doctors undergo extremely rigorous training in honing on the clinical knowledge, skills and experience
Complex
Patients come to see doctors and present symptoms
Doctors spot the patterns and identify the variations from the norm
Doctors manage the diseases with different treatment option
Sensitive and high risk
Huge variation in disease presentation, and treatment options
Dealing with human beings
Challenging

4. Technology innovation in healthcare
Successful health care delivery requires effective medical technology
Combination of technology and health = boon!
Break barriers in the future and change the healthcare scenario, creating lasting impact
Robotics
Nanotechnology
Artificial Intelligence
Successful health care delivery requires effective medical technology
Combination of technology and health is providing to be a boon to many
Revolution is set to break barriers in the future
Innovation in biotech, pharma, IT, medical devices and equipment e.g. wearables will change the HC scenario
Create lasting impact on patient interaction, diagnosis and treatment delivery and population health management 
Recommend in investing into technology growing sector 
Robotics
Robotic surgery with surgeons work at home, patients can be managed locally
Minimially invasive surgery with quicker healing and less scarring
Transform from human to cyborg (biohacker), improve mental performance (nootropics) 
Nanotechnology
Nanobots unclog arteries, nanoparticles cross BBB to manage neurological disorders
Grow Fit – Indian based company developing personalised and targeted healthcare with data science, ML and medical science  AI
Humans have limitation in the volume of information that brain can process

5. Healthcare Artificial Intelligence market
Lucrative market – expected to reach $7.98 billion in 2022
Lucrative market – expected to reach $7.98 billion in 2022
15% of all global deals made by healthcare AI companies
In 2015, healthcare artificial intelligence companies comprised 15 percent of all global AI deals across sectors.
Increasingly high volume of deals made by startup companies – receive millions in venture capital investments
“Deals to healthcare-related AI companies have been increasing year-or-year since 2011, with deals more than doubling in 2014,” the report says. “Funding jumped by nearly 460 percent in 2014, to $358M from $64M in 2013.”
“New startups are clearly venturing into this space, with more than 20 AI-based healthcare-focused companies raising seed/angel funds in 2015, compared to less than 5 in Overall, seed/angel deals dominated with a 46 percent share of deals during the last 5 years, followed by Series A deals at 23 percent.”
Driven factors – growing use of big data, growing importance on personalised medicine, cross industry partnerships etc.
Hindering factors – reluctance among clinicians in adopting AI-based technologies, ambiguous regulatory guidelines

6. AI and machine learning. How does AI work in healthcare?
Artificial Intelligence is the field of study where we teach computers how to learn
Machine learning is a type of AI that allows computers to make predictions without being explicitly
Provides formal conceptual framework for input processing and decision making in diagnosis and management
Objective decision making with less variance
High speed and efficiency
All result in real benefits to patients
Artificial Intelligence is the field of study where we teach computers how to learn
Machine learning
Type of AI that allows computers to make predictions without being explicitly
Provides formal conceptual framework for input processing
The machines do not have inherent biases and are more likely to make objective decisions unclouded by pre-conceived SE notions about patients
The machines are able to process vast amount of information rapidly, recognise shifts patterns in data and decision making and high efficiency
(Benefits from healthcare:- Formal conceptual framework for input processing- Objective decision making and less variance between practitioners- Faster diagnostics)
All result in real benefits to patients
Ref:
Artificial Intelligence in Healthcare – It’s about Time | Casey Bennett | TEDxNashville
Better Medicine Through Machine Learning | Suchi Saria | TEDxBoston
HealthIT Analytics

7. What are the examples of AI in healthcare?
Almost every aspect of healthcare could, theoretically, benefit from an AI approach
Microsoft
Predictive analytics in vision care
Google
Clinical decision support in breast cancer diagnosis
IBM Watson
Precision medicine in population health management
Microsoft
Vision care – Microsoft forms partnership between Institutes in India, US, Brazil and Australia in using machine learning techniques to develop predictive analytics models for visual impairment and blindness
Google
Pathology and breast cancer diagnosis – ML algorithms with deep learning and neural networks techniques (convolutional neural network CNN) are applied to cancer tissue diagnosis to enhance diagnostic accuracy, which eliminates the variability of skill, training, and experience of pathologists
IBM Watson
Launch ML initiatives digs into the potential of unstructured data to focus on advanced imaging analytics and population health management in supporting the delivery of value based healthcare
Imaging analytics is the main area of interest with recent $1 billion acquisition of Merge Healthcare in 2015

8. Challenges True AI has yet to come Clinical decision support tools
Companion diagnostics
Although technology is advancing at a remarkable pace, the true AI have yet to come.
Computers have not adequately captured many subconscious decision making processes based on minute sensory inputs, past memories and experiences of physicians
ML is on its own is never going to replace the deeply and uniquely human capacity in disease diagnosis – it can fill in the natural knowledge gap clinicians may have, through a wide range of clinical decision support tools
Companion diagnostics – clinical decision support tools with ML techniques

9. QuintileIMS
Leading worldwide integrated information and technology enabled healthcare service provider
>50,000 employees operating >100 countries with leading scientific, analytical and commercial experts
Formed through the merger of Quintile and IMS in October 2016
Possess global health data assets with Real World Evidence (RWE) insights generated by advanced analytics leveraged by its technology infrastructure
Leading worldwide integrated information and technology enabled healthcare service provider
Driving clinical development – efficient trial design and faster market access
Adding value to clients and shareholders
>50,000 employees operating >100 countries with leading scientific, analytical and commercial experts
Formed through the merger of Quintile and IMS in 2016
Establishment of a new management team and operational framework
3 reportable segments – commercial solution, R&D solution, integrated engagement services
Possess global health data assets with Real World Evidence (RWE) insights generated by advanced analytics leveraged by its technology infrastructure
One of the largest and most comprehensive collections of healthcare information in the world
Apply advanced analytics and leverage technology infrastructure to improve efficiency and decision making
Research & Development – clinical operation, clinical trial support service, Q2 solution
Commercial – real world insights, consulting, information, technology solutions, workflow analysis, integrated engagement services

10. QuintileIMS
Clients include top 100 global pharmaceutical and biotechnology companies
Market opportunity for R&D services and commercial solutions
Growth strategy by continuing to innovate
Clients include top 100 global pharmaceutical and biotechnology companies
Payers, government and regulatory agencies, providers, pharmaceutical distributors, pharmacies
Market opportunity for R&D services and commercial solutions
growth and innovation in life science industry
growth in R&D, increased complexity in R&D
financial pressure driving the need for increased efficiency
need to demonstrate value
need to integrate and structure expanding source of data
Growth strategy by continuing to innovate
leverage information, technology and service capabilities
build upon extensive client relationships
expand portfolio through strategic acquisition
expand penetration of service offerings to broader healthcare marketplace

11. QuintileIMS $7.8 billion revenue with 7.8% growth
$24 billion enterprise value
$18 billion market capitalisation
11% shareholder return
Revenue by segment – $3.5B in commercial solution, $3.5B in R&D solutions, $0.8B in integrated engagement services
Revenue by region – $3.5B in Americas, $2.8B in EMEA, $1.5B in Asia Pacific
Read more: Revenue  Read more: Enterprise Value (EV) 
Read more: Market Capitalization 

12. Conclusion
‘Cognitive computing could usher in a golden age – if we shape it wisely.’
‘There’s a land rush around AI right now… the competitive advantage is going to come from being cognitive.’
IBM President and CEO Ginni Rometty

13. Thank you