Artificial intelligence in healthcare Dr Yuanrui Margaret Shi 7 July 2017

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

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

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

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 2011. 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

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

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 http://healthitanalytics.com/news/microsoft-takes-on-blindness-eye-care-with-ai-machine-learning 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 http://healthitanalytics.com/news/googles-machine-learning-imaging-analytics-flag-breast-cancer 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 http://healthitanalytics.com/news/ibm-watson-expands-role-in-imaging-analytics-population-health

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

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

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

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 http://www.investopedia.com/terms/r/revenue.asp#ixzz4lzThECXs  Read more: Enterprise Value (EV) http://www.investopedia.com/terms/e/enterprisevalue.asp#ixzz4lzTBuE7f  Read more: Market Capitalization http://www.investopedia.com/terms/m/marketcapitalization.asp#ixzz4lzU3Crc3  http://www.investopedia.com/articles/basics/10/efficiently-read-annual-report.asp

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

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