This document emphasizes on the 3 pillars of Artificial Intelligence in Healthcare viz. Machine Learning, Natural Language Processing and Vision

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Artificial Intelligence: Potential Game
Changer for Medical Technology Companies
2 November, 2017 | Author: Poulami Chatterjee: Healthcare Business Analyst,
Akash Jha: Healthcare Consultant, CitiusTech
CitiusTech Thought
Leadership

2. 2
Agenda
AI: Overview of a potential game changer
AI: Healthcare applications & implications
AI: Key benefits for Medical Technology companies
References

3. 3
A set of statistical techniques for identifying
patterns and enable intelligent decision support.
Deep Learning
Definition: Deep
learning is a sector of
machine learning that
uses neural networks
to mimic the cognitive
capability of the human
brain.
Technology: IBM
Watson, Python, R and
Tensor flow etc.
Predictive Analytics
Definition: Predictive
analytics has a variety
of techniques such as
predictive modeling,
machine learning and
data mining to analyze
current and historical
facts to predict future
outcomes.
Technology: SPSS, SAP
HANA RapidMiner, etc.
Natural language
processing (NLP) is a field in
AI and Linguistics which is
concerned with the
interactions between
computers and human
(natural) languages
Artificial Intelligence
Applications and services are designed to simulate human senses and its derived intelligence without any
external input to perform cognitive functions such as learning and problem solving.
Machine Translation
Natural Language
understanding
Natural Language
Generation
Corpus Linguistics
Computer vision is the
capturing, processing and
understanding of visual
information similar to the
combination of the eye
and the brain.
Image Extraction
Derived Insights &
Reports
Image Processing &
Analytics
Image Sensors
Natural Language Processing Machine Learning Vision
AI: Driven By Cognitive Tools and Next-Gen Analytics

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Deep Learning
Predictive Analytics
Classification
& Clustering
Translation
Image
Recognition
Computer
Vision
Commonly used software
R Python Cortana IBM Watson
Tensorflow Apache CTAKES Rapid Miner SPSS
Natural
Language
Processing
Machine
Learning
Vision
Artificial
Intelligence
3 Pillars of AI

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 Use of NLP to scan precedents and research
material for cases
 Lawyer chatbots to diagnose a case and
automatically prepare an appeal
 Use of complex AI systems and NLP based
research to make trading and investment
decisions
 “Robo-advisers” offer portfolio management
and AI apps help in personal finance
 AI based Self-driving cars under test from
Uber, Google and Tesla
 Intelligent cars self-diagnosing technical
problems, locating gas stations etc.
 Use of AI to analyze browsing, spending
patterns and push ads to users
 Smart posters sensing people’s presence
and changing ads based on reactions
 Analyzing customer emails for meaning and
sentiment and feeding data to CRM systems
 Deep learning techniques to replace human
customer services with bots
 Use of AI to prepare completely autonomous
reports on major events, i.e., sports,
elections
 Analyzing consumer pattern and pushing
relevant content, i.e., Netflix, Facebook etc.
Today AI has influence over many aspects of our lives
Others uses of AI are Law enforcement, games, smartphones etc.
Marketing Law
Retail Finance
AutomobileMedia
AI: Significant Adoption Across Industries

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AI: Overview of a potential gamechanger
AI: Healthcare applications & implications
AI: Key benefits for Medical Technology companies
Agenda

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AI: Significant Opportunities in Healthcare (1/2)
Patient Engagement
Chatbots
Roles Market
 Personalized advice
 Diagnosis and Referrals
 Medication reminders
 Analyze mental health
 Lifestyle suggestions
 Dr. AI by HealthTap
 Buoy app
 Microsoft HealthBot
 BabylonHealth App
 Your.MD app
Population Health
Advanced dashboards
Roles Market
 Imaging analytics
 Risk Stratification
 Survivability prediction
 Early diagnosis
 Gene sequence analysis
 Customized care plans5.3
 IBM Watson
 Deep Genomics
 Google Deepmind
 Intel Lumiata
 Sophia Genetics
 CareSkore

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AI: Significant Opportunities in Healthcare (2/2)
Medicine
Diseases, drugs & trials
Roles Market
 Drug efficacy prediction
 Medication Management
 New Drug discovery
 Outbreak prediction
 NLP based outcomes
 IBM Watson with J&J
 Microsoft Hanover
 AtomWise
 AiCure app
 Insilico medicine
 PathAI
User Experience
Operational efficiency
Roles Market
 NLP Text mining
 Natural voice search
 Virtual assistants
 Internet of Things (IoT)
 Linguamatics
 Dolbey Fusion Speech
 Nuance Nina
 Microsoft Cortana
Medical technology companies and application vendors are already developing and prototyping AI applications
across various healthcare use cases.

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AI in Healthcare: Industry Adoption (1/2)
Google DeepMind – NHS
Deep learning algorithms interpret visual information in the form of de-personalized scans (head and neck scans
at University College London Hospitals NHS Foundation Trust, eye scans at Moorfields Eye Hospital NHS
Foundation Trust) to identify potential issues.
IBM – Cleveland Clinic Lerner College of Medicine research
IBM WatsonPaths parses the medical records for facts and test results, then knits them together into competing
theories that might explain the patient's symptoms and communicate with Physicians in a natural way.
IBM – Memorial Sloan Kettering Cancer Center
IBM Watson ingests and analyzes tens of thousands of the renowned cancer center patient records and clinical
research providing treatment options with degrees of confidence for each, along with the supporting evidence.
Deep Genomics
Leverage deep learning algorithms to decode the meaning of the Genome trying to predict the effects of a
particular mutation based on the analysis of other mutations. Developed a database for prediction on how 300
million genetic variations could affect a genetic code.
Enlitic
Interprets a medical image and classify malignant tumors, patient risk and provides decision support. Also does
regressive retrospective analysis to judge clinical performance. Detection rate is 50% more accurate and 10,000
times faster than a Radiologist.
Turbine
Models cell biology on the molecular level to identify the best drug for a specific tumor, complex biomarkers and
design combination therapies by performing millions of simulated experiments guided by an AI identifying
biomarkers signaling sensitivity to treatment.
Research ProductPrimary Care
ResearchOphthalmology
Research ProductOncology
ResearchGenomics
ProductOncology
ProductPharmaceutical

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AI in Healthcare: Industry Adoption (2/2)
Your.MD – NHS
AI-powered chatbot asks users about their symptoms and provides easy-to-understand information about their
medical conditions. Uses machine learning, natural language processing and generation. App’s diagnosis has
been approved by UK NHS.
Babylon Health
Physician trained AI chatbot provides diagnosis of symptoms, continuously analyzes data and cross-references
with other patients, sets up video consultations with selected Providers and manages prescriptions and
histories.
Stanford University
In-house AI algorithm, trained with over 130,000 images of moles, rashes, and lesions, diagnoses skin cancer
rivalling professional doctors.
AiCure
AI algorithms set up medication reminders, offers facial recognition and visual confirmation of medication
ingestion, adapts to patient lifestyle patterns and set up automated interventions in case of deviant behavior.
Arterys – GE Healthcare
AI driven cloud-based medical imaging platform uses MR images to draw up the contours of the heart’s four
chambers, measuring how much blood they move with each contraction, usually hand-drawn by Cardiologists.
GE plans inclusion in its MRI machines soon.
23andMe – Rthm
23andMe have genetic diagnostic tools to help individuals understand their genetic makeup while Rthm allows
users to leverage the insights produced from their genetic test to implement changes to their everyday routine
ProductPrimary Care
Primary care
ResearchOncology
Medication
ProductCardiology
ProductGenomics
Product
Product
Research

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Key AI Focus Areas for Medical Technology Companies
Data Mining and Regression Care Plans
Reduce Redundancy
Population Health
Patient Coordination
Decision Support
Pharmacy OptimizationGenomics/Precision Medicine
 AI can predict unseen & intelligent outcomes
with attached risk using historical data
 Use cases may include ESRD prediction rates
and co-morbidity risk & survival
 AI uses guided learning to identify repetitive,
redundant processes like utilization of scanners
 Streamline processes, i.e., auto-booking provider
appointments in making a diagnosis
 AI based web crawlers can find latest research
and treatment for Oncology and Cardiology
 Analyze long-term effectiveness of treatment
and have alternate care workflows
 AI enables Genome sequence mining, identifying
potential incompatibilities in medication
 Helps save provider time by making educated
predictions on risks and prescriptions
 AI uses wearables & smartphone to analyze
behavioral patterns, risk and medication adherence
 Create intelligent tailored care plans keeping in
mind payor, risks and past incompatibilities
 AI chat bots converse with patients in English and
diagnose basic illnesses using databases
 Intelligent notifications to patients on change in
schedule, i.e., medication adherence
 AI can identify usage patters for EHR like frequently
used tabs, i.e., open demographics on log-in
 Use past analysis to suggest ideal next-steps for a
particular patient, i.e., follow-up on BMI etc.
 Streamline Pharmacy operations by reordering
stock of most used medications
 AI can predict new drug behavior based on
regression data from its chemical constituents
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Agenda
AI: Overview of a potential gamechanger
AI: Healthcare applications & implications
AI: Key benefits for Medical Technology companies

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Leveraging AI in Medical Technology: Quick Wins
Making Search More
Efficient & Accurate
 Providers belonging to varied specialties may be using multiple EHRs from
different vendors
 Natural Language (NLP) search service integrated into the EHR can reduce time
spent in searching historical records
 It also helps Providers do a more focused searched as compared to manual
filters
Improving Care-
Coordination
 Providers often go through a repetitive process of collecting and interpreting
information at multiple points in the care co-ordination process, i.e.,
appointment, reception, diagnosis and treatment
 AI chat bots integrated into Patient Portals can mostly diagnose the illness and
book appointments even before the patient reaches the clinic
Enhancing Patient
Engagement
 Improved turnaround time through use of chatbots and automatic
appointments reduce frustration
 Patient behavioral analytics and focused notifications, i.e., sleep time, eating
habits etc. improve wellness and builds trust
 Intelligent notifications and alerts to providers help long-term patient health
and improves adherence

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Leveraging AI in Medical Technology: Long-Term Impact
Significant Gains in
Population Health
 AI initiatives such as chat bots help reduce probability of miscommunication and
time to treatment
 Predictive analytics on patient behavioral patterns and vitals helps customize
better care plans
 Advanced risk scoring helps prioritize patients for priority care
 AI mining of health records and scanning of new research content helps find new
treatment avenues
Reduction in
Cost of Care
 Long-term population health improvements result in less average treatment
costs
 Predictive analytics, i.e., tumor analysis, imaging nodule, risk scorings help
identify critical illnesses at an early stage
 Regression based AI systems can identify under and over utilization of medical
resources, i.e., scanners etc.

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AI in Medical Technology: Critical Success Factors
 Big Data Challenge: Unsupervised learning requires training samples from a huge volume
of data to be successful. Healthcare data in itself in very size intensive
 Defining Powerful Use Cases: Use cases will vary significantly, i.e., Patient Engagement
may be a use case for a EHR vendor but not for a PACS vendor
 Building a Cognitive Data Ecosystem: For AI to succeed, total healthcare data needs to
behave as a single entity and completely accessible to the AI subsystems
 Customer IT Readiness: Requires investments in Data Scientists, Predictive analytics tools
and big-data technologies or partnerships with 3rd party entities
 Managing Variability: Advanced AI chatbots may sometimes have unpredictable behavior
(MS Tay bot) and needs to be supervised
 Supervised Learning: Most AI applications today are supervised learning requiring
unbiased data and fast processing for optimal result
 Human Challenges: Collection of data for AI implementations will raise security and ethical
challenges

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AI in Medical Technology: Critical Success Factors (1/2)
Big Data Challenge Unsupervised learning requires training samples from a huge volume of data
to be successful. Healthcare data in itself is very size intensive.
Defining Powerful
Use Cases
Use cases will vary significantly, i.e., Patient Engagement may be a use
case for a EHR vendor but not for a PACS vendor.
Building a Cognitive
Data Ecosystem
For AI to succeed, total healthcare data needs to behave as a single entity and
completely accessible to the AI subsystems.
Customer IT
Readiness
Requires investments in Data Scientists, Predictive analytics tools and big-
data technologies or partnerships with 3rd party entities.
Managing Variability Advanced AI chatbots may sometimes have unpredictable behavior (MS Tay
bot) and needs to be supervised.

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AI in Medical Technology: Critical Success Factors (2/2)
Supervised Learning Most AI applications today are supervised learning requiring unbiased data
and fast processing for optimal result.
Human Challenges Collection of data for AI implementations will raise security and ethical
challenges.

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AI in Medical Technology: Key Takeaways
 AI can have huge implications in the Healthcare market as it has been having in other
spheres
 Most of the innovation in AI is happening under startups with few big players, i.e., IBM,
Google
 Most popular use cases are Risk Prediction, Medical Image Analytics, Patient Engagement
and Medication Adherence
 We recommend Medical Technology companies to first identify very strong use cases, i.e.,
for a Pop Health vendor, Risk Prediction using AI can have huge long-term benefits
 Companies will also need to define their strategy for skillsets, i.e., either develop in-house
or outsources key skills such as Data Science, Big-Data, NLP etc.
 Companies should take a holistic view of the future and not be bogged down by short-
term issues in terms of cost, technology and training

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References
 https://blogs.thomsonreuters.com/answerson/artificial-intelligence-legal-practice/
 http://medicalfuturist.com/artificial-intelligence-will-redesign-healthcare/
 http://medicalfuturist.com/top-artificial-intelligence-companies-in-healthcare/
 https://www.engineersgarage.com/blogs/top-10-industrial-applications-artificial-
intelligence
 https://phys.org/news/2017-06-artificial-intelligence-health-revolution.html
 https://www.wired.com/2017/01/look-x-rays-moles-living-ai-coming-job/
 https://tincture.io/how-ai-will-keep-you-healthy-7140a78e18aa

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Thank You
Author 1:
Poulami Chatterjee
Healthcare Business Analyst
thoughtleaders@citiustech.com
About CitiusTech
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 Payers and health plans
 ACO, MCO, HIE, HIX, NHIN and RHIO
 Pharma & Life Sciences companies
Author 2:
Akash Jha
Healthcare Consultant
thoughtleaders@citiustech.com