artificial intelligence - potential game changer for medical technology companies
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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
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Agenda
AI: Overview of a potential game changer
AI: Healthcare applications & implications
AI: Key benefits for Medical Technology companies
References
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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
About CitiusTech
2,700+Healthcare IT professionals worldwide
1,200+Healthcare software engineering
700+HL7 certified professionals
30%+CAGR over last 5 years
80+ Healthcare customers Healthcare technology companies
Hospitals, IDNs & medical groups
Payers and health plans
ACO, MCO, HIE, HIX, NHIN and RHIO
Pharma & Life Sciences companies
Author 2:
Akash Jha
Healthcare Consultant