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August 21st, 2019
Masaya Watanabe
VP and Executive Officer, Hitachi, Ltd. Vice Chairman, JFMDA
Healthcare Innovation in Japan
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Masaya Watanabe
Vice President and Executive Officer, CISO & GM, Smart Transformation Hitachi, Ltd. Vice Chairman, The Japan Federation of Medical Devices Associations (JFMDA)
June 2017 Chairman, The Japan Federation of Medical Devices Associations
April 2015 Vice President and Executive Officer President and CEO, Healthcare Company
April 2014 President and CEO, Hitachi America April 2012 Vice President and Executive Officer CSO, IT Group April 1982 Entered Hitachi, Ltd
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National Cancer Centre Singapore (NCCS)
New NCCS Building 24 stories above ground and 4 stories
below ground Scheduled to open in 2021. Proton Therapy System installation
started from this July in the below
ground level
Installation of Gantry Parts 2
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3
● Introduction to Healthcare and Med-tech in Japan ● Digital/e-Health in Japan ● Foundation for New Digital Innovation ● Singapore-Japan Collaboration
Contents
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Introduction to Healthcare and Med-tech in Japan
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1868
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1920
1930
1940
1950
1960
1970
1980
1990
2000
2010
2020
2030
2040
2050
2060
2070
2080
2090
2100
2110
Present
<15 yo
15-49 yo
>65 yo
50-64 yo
Source: Ministry of Health, Labor and Welfare 8th Healthcare medical AI development acceleration consortium (Jun. 6, 2019),
“Current status of image-related database and common platform building research at AMED“
Population(%) over 65yo
in 2060
Japan: World top Aging Society & Decrease of Population Population over 65yo 26.0% (2015) 38.1% (2060)
Aging Society, Japan and Global
in 2015
Demographic Shift in Japan
5
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6
Critical Issues anticipated in 2040
Medical and Nursing Expenses
Source: Ministry of Health, Labor and Welfare Committee
● Lack of labor in medical and nursing service ● Increases of medical and nursing cost
● Shortage of medical and nursing service in big cities
Number of employees in Medical and Nursing
334
402
501
6.43 7.29
8.35 (M People)
12.5 14.7
18.8%
2018 2025 2040
334
402
501
49.9
62.9~63.3
92.9~94.7 (T Yen)
2018 2025 2040
8.8% 9.7~9.8%
11.7~12.0%
Medical Expense
Nursing Expense
Medical
Nursing
rate to total employees the rate to GDP
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Sustainable Social Growth
Solve Social Problems and Develop Values that supports the Sustainable Growth
Solve Social Problems •Establish Flexible Society Structure •Solve Social Infrastructural problem •Energy and Environmental Problem
・・・ •Globalization
•Create Market
• Industry Competitiveness
•Economic Growth
Sustainable Development Goals (SDGs)
Society 5.0 Society where people can
live vividly and comfortably
Realize Both
7
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Japanese Med-tech Market and JFMDA
●Founded in 1984 ●21 Association Members ●Total 4,280 Companies ●120,000 Employees
●3.0T Yen (8% of WW) ●CAGR approx. 4% ●Export:0.6T Yen vs Import:1.6T Yen ●300K types of Devices
8
Med-Tech. Market JFMDA (Japan Federation of Medical Device Association)
・JMIA: Japan Medical Industry Association ・JEITA: Japan Electronics and Information Technology
Industries Association ・JMOIA: Japan Medical-Optical Equipment Industrial
Association ・JAMDI: Japan Association of Medical Devices Industries ・MTJAPAN: Medical Technology Association of Japan ・JAHID: Japan Association of Health Industry Distributors ・JASS: Japanese Association of Surgical Sutures ・JHPIA: Japan Hygiene Products Industry Association ・JIRA: Japan Medical Imaging and Radiological Systems
Industries Association ・JOIA: Japan Ophthalmic Instruments Association ・JCLA: Japan Contact Lens Association ・Japan Condoms Industrial Association
・JHHC: Japan Home Health Care Association ・JDTA: Japan Dental Trade Association ・JAIMA: Japan Analytical Instruments
Manufacturers' Association ・JHIMA: Japan Hearing Instruments
Manufacturers Association ・JHIDA: Japan Hearing Instruments Dispensers
Association ・HAPI: The Japan Home-Health Apparatus
Industrial Association ・Japan Industries Association of Physical Therapy Device ・JACRI: Japan Association of Clinical Reagents
Industries ・@MD-Net: Association of Japan Medical devices
Network
AMDD: American Medical Devices and Diagnostics Manufacturers’ Association
EBC: European Business Council in Japan
JFMDA Association Members
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JFMDA Vision 2018
Transform to Lead “Society 5.0” ●Innovation including Digitalization ●from Single Device to System to to resolve Social Agenda ●Collaboration with Stakeholders For the Patients ●Safety, Supply, Traceability(UDI) Industry Growth ●Globalization ●Attract Young Talent
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AMED Working Group concluded Five Focuses:
① Early and Simple Exam. and Diagnosis ② Diagnosis and Treatment that maximize Outcome (eg. Cancer) ③ Prevention (eg. High Blood Pressure, Diabetes) ④ Human Function Enhancement to Aged People ⑤ Digital Health and Platform
Next R&D Investment of Med-tech
Growth Period Early Period Maturity
Mark
et
Com
petitiveness
⑤ ④
① ➂ ②
10
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Digital/e-Health in Japan
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AI Technology History
Describe Formula
Neural Network
3rd Generation
Describe Rules
Study from Data
Machine Learning
Describe Knowledge
Fuzzy Inference
Planning
1st Generation
2nd Generation
1960 1970 1980 1990 2000 2010 2020
Deep Learning
XAI
12
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Dawn Period
Revaluation
Expecte
d v
alu
e
Biometric
Authentication
Block Chain
Drop Period
AI/IoT Today
Big Data
AI/IoT in 2017
●Health, Medical, Nursing
Data Base Establishment
Medical AI Hub
Recurrent Training
to Medical Staff
●Smart Agriculture
●National Enforcement Infrastructure
Disaster Prevention
●Transportation and Logistics
●Local Development Smart City from Japan
AI for Society in Japan
AI Social Implementation Disruptive Tech. Adoption
13
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Medical / Image Data
Vital Data
14
Watching by IoT
Sensing Vital Data
Disease Prediction, Prevention and after care by AI
Remote Medical Care, Surgery
AI Diagnosis Support
Surgeons can conduct many surgery without moving, hence increase numbers
Watching aged or care required, sensing vital and communication robot
Sensing vial data such as work out load and pulse
AI conducts risk analysis and diagnosis based on data
Outing support
Outing support by auto-operated technology
Outpatients, Inpatient, Nursing
Healthy Person
Acute Phase
Ambulatory support by auto-operation
Human resource allocation effective and optimum
Connect Care Cycle by Digital Technology
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Surgery 4.0
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Surgery 4.0
Support immediate and accurate decision by summarizing patients information such as diagnostic data
Define “Optimal Surgery” by linking surgery data and clinical outcome. Accelerate Standardization
Support Surgeon training by AI
1800s Surgery 1.0
Surgery by inhalation anesthesia
1990s Surgery 2.0
Endoscopic Surgery
Today Surgery 3.0
Robotic Surgery
Future Surgery 4.0
Expectation to Surgery 4.0
16
CT MRI Similar Cases / Expert Cases Patient Data
Surgery Platform / Next Generation Surgery Support Robot
EMR/EHR including surgery outcome
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SCOT for Smart Surgery
医療機器
®
・Standardize surgery devices ・Improved safety ・46 medical devices connected
Intra-Operative MRI
・First in 2000 at Tokyo Women’s Medical University ・2,000 surgeries ・Brain tumor 5 year survival rate improved by 80% ・Introduced in 16 Hospitals
Medical Device
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AI-SCOT Data
Warehouse C
lean
sin
g
AI-SCOT
Conference before surgery - Search and display similar cases Navigation during surgery - Prediction after surgery
Medical Device
®
ORiN is a registered trademark of the Japan Robot Association
VR/AR
18
SCOT for Surgery 4.0
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Surgical Skills Assessment with Digital
Surgery Assistant System
Surgery Support Robot
Surgery Scoring System
Digital Solutions
accurate and objective surgical skills assessment system, designed to help health care professionals
continuously improve (source: http://www.csats.com/)
HyperSCOT
Surgery Work Flow Analysis with Digital Tech.
Staff and device status transition sensing and visualization
Ascertain Surgeon’s Skill Level Objectively
Clarify the Tasks during Surgery
Tokyo Women's Medical University
C-SATS
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AI Hospital
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AI Hospital Project in SIP
PD Yusuke Nakamura Director Cancer Precision Medicine Center
SIP(Strategic Innovation Promotion) Program funded by Japanese Gov.
Realize advanced “patient-centered medical care” through hospital digitalization and AI usage
Realize both quality of medical care and staff satisfaction through audio visual data at medical fields
10 model hospitals by 2022
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AI Hospital Implementation
●High Security DB ●Medical effective information extraction and analysis
●Diagnostic Conversation Doc. ●Summary Document for patient ●Bidirectional patient explanation
●Endoscopy with sensors and AI ●Comprehensive analysis system including vital and genome data ● Super precise analysis by AI
Advanced Diagnosis and Treatment
Improved Communication with Patient
Audio Visual
22
Knowledge DB Patient Info. DB
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Humanoid Robot EMIEW3
AI
Image Recognition
Voice Recognition
Cloud Base
Scenario Base
Japanese・English・Chinese
Speed 6km/Hr
23
Tablet Type
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Radiation Room
Reception
Waiting Room
Hospital Ward
Treatment Room Hospital Guidance
Relief of Anxiety
Explanation of Diag.
Guidance of Treatment
Patient Check during night
PET Examination with Humanoid Robot
24
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Diagnostics and Treatment × AI
Collaboration
Tokyo Medial and Dental University
and Hitachi
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10K(1K/Year) 500K(100K/Year) 10M(1M/Year)
Number of Patients in Japan
“Common Intractable Disease”
Source: Tokyo Medical and Dental University document
other Disease
Common Intractable Disease eg. IBD, Rheumatism
Rare Intractable
Disease
Improve Medical Care to “Common Intractable Disease” with AI and ICT
● Collaboration between High Level Medical Institute and Local Hospitals
Share clinical Specialists Knowledge
Share Patient information in timely manner
● Improved QOL for Patient
● Improve Medical Economics by Optimized Cost
26
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Local Hospitals/Clinics
Patient Referral
Lack of Resources ・Patients over concentration ・Remission and mild patients over stay
Uneasy to accept intractable patients
Specialized Medical Institution
Tokyo Medical and Dental University Intractable Disease Treatment
Highly Specialized Medical Institute and Local Hospitals/Clinics have cooperation mechanism but not effectively implemented and it causes problems to each sides.
Best care to patients may not provided that causes lack of satisfaction
IBD Patient Care: Today
Without Referral
Counter Referral
IBD: Inflammatory Bowel Disease
27
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Focus on severe and exasperation period patients
Secure feel by support from highly specialized
medical institutes
Information share and patient monitoring by
devices
Comfortable Self Management
Realize Specialist/Local/Patients linked medical care by digital tech
● Specialists patient watch using smart devices
● AI diagnosis based on structuralized specialists clinical knowledge and clinical information
Clinical Research started in July 2019 at Tokyo Medical and Dental University
IBD Patient Care: Future with AI and ICT
Local Hospitals/Clinics
Patient Referral
Specialized Medical Institution
Tokyo Medical and Dental University Intractable Disease Treatment
Counter Referral Supervise
28
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Diagnostic Imaging with AI
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Image Diagnosis × AI
+ US Breast Cancer CAD
Dementia CAD
Lung Cancer CAD
Muscle Analysis
Cerebral aneurysm
White Matter Analysis
Brain Shrink Evaluation
Ion Deposition Evaluation
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AMED Sponsored Research Project(1)
Endoscopy 43,482
Pathology 12,934
Radiology 29,006,125 ⋆
University Hospital
Anonymized & Annotated
Image DB
GPU Server
+
● ●
Ophthalmology 213,671
Dermatology 6,136
● ● ●
AI Application
AI Core Technology
Collaboration of Medical & Engineering Researchers
Delivery to
Doctors
AI Application
AI Application
* No. of Data collected in 2018
AMED: Japan Agency of Medical Research and Development
Glaucoma positive/negative
Research by
Japanese
Ophthalmological
Society
Source: Ministry of Health, Labor and Welfare 8th Healthcare medical AI development acceleration consortium (Jun. 6, 2019),
“Current status of image-related database and common platform building research at AMED“
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Challenges to the Next Step: To Utilize DB collected by Academic Society in Boarder Community including Industry 1. to Create Teacher’s Data (1) Meta data Generation, Annotation (2) Anonymization 2. to Provide Data to the Third Parties (1) Personal Information, Ethic Examination (2) Data Security (3) Data Standardization (4) Intellectual Property Rights
AMED Sponsored Research Project(2)
32
Source: Ministry of Health, Labor and Welfare 8th Healthcare medical AI development acceleration consortium (Jun. 6, 2019),
“Current status of image-related database and common platform building research at AMED“
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MRI Quantitative Parameter Mapping --- Simultaneous measurement of multiple quantitative maps ---
Quantitative parameter maps 3D-GrE
Signal intensity function by Bloch simulator
FLAIR PDW T1WI T2*WI T2WI QSM MRA
T1 PD T2*
Derived maps & weighted images
B1
Taniguchi Y, et al. ISMRM 2010; 3113.
Parameter fitting
Works in Progress
Acknowledge Risk and optimization
Easy Imaging
Rapid Imaging
High Quality Image
Image Quantification
Support Interpretation Detect/Analyze/Report
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QSM High Resolution Imaging(1)
34
Healthy Example Hokkaido University and Hitachi Joint Research
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35 35
Pronucleus Inner Nucleus
Thalamus Pillow
Caudate Nucleus
Pale Ryukyu
Putamen
Hokkaido University and Hitachi Joint Study
Line of Sight
Gennari Linear
QSM High Resolution Imaging(2)
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Foundation for Digital Innovation
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New Innovation driven by “Data”
Innovation Data Medical
Care
Data extracted from medical fields drives Innovation
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What are required as foundation of Digital Innovation? ● National Data Platform ● Personal Data Protection ● AI Ethics ● Cybersecurity ● New Talent etc.
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National Data Platform in Japan
CIN: Clinical Innovation Network, PeOPLe: Person centered Open Platform for wellbeing
Data Users Data Supplier
Individual
Hospital Clinic
Nursing Operator
Government
Medical Society
etc.
Hospital
Univ. Research Lab
Pharma.
Insurance
Government
etc. Registered Operator
Opt out Anonymized
Medical Information
CIN
AI Deep Learning
Cancer Genome
Treatment Information
+Genome
Diagnostic Images
Patient
Registry
PeOPLe PHR
Opt in
● Investment to National Healthcare Data Platform ● ”Next Generation Medical Platform Law“ (May 2018)
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AI Ethics in Global
USA ・Presidential decree signed “American AI Initiative“
(Feb. 2019)
China ・Establish AI ethics committee
(Jul. 2019)
Singapore ・AI governance and Ethics initiative (Jun. 2018)
UNESCO ・Forum on artificial intelligence in Africa (Dec. 2018) ・Principle for AI
(Mar. 2019)
OECD ・AIGO(Expert Group on AI)
Final meeting
(Feb. 2019)
EC
・Ethics guideline for trustworthy AI (Apr. 2019)
Social principle and AI ethics needed for the implementation of AI to the society
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Social Principle “Human-centric AI”
Human
Dignity
Diversity
Inclusive
-ness
Sustainability
Unjust data collection and infringement of sovereignty using dominant position
Education Literacy Principle
Provide education environment equally to all people
Fair Competition Principle
Fairness, accountability and transparency Principle
No unjust discrimination
Adequate explanation
Open dialogue on AI usage
Innovation Principle
Establish data use environment
Renovate prohibiting regulations
Security Principle
Convenience and Risk Balance
Secure social safety and sustainability
Privacy Principle
Individual’s Freedom, Dignity and Equality shall not violated by personal data use
Human Center Society
Human Center Principle
AI expands human ability
Final decision by human on the AI usage
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Healthcare Cyber Security
● Guideline to Secure Medical Device Cyber Security (Apr 2015)
● Slow implementation in Japanese Healthcare
● Digitization and Networking in Medical Field ● Increase to be vulnerable to Cyber attack in medical data ● Information leak by cyber Attacks ● Information leak by the increased chance of carrying medical information outside of the facility by mobile devices ● Malicious use of IoT equipment that has vulnerability
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Increasing Risks Japan Actions
ICT-ISAC JAPAN ICT Information Sharing And Analysis Center Japan
JE-ISAC Japan Electricity Information Sharing and Analysis Center
Financials ISAC Japan Information Sharing and Analysis Center
Telecom-ISAC JAPAN Telecom Information Sharing And Analysis Center Japan
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Cyber Security in Healthcare
Eco-System
Vendors(Med-tech, IT), Hospital, Professional Service, Government
Product Level Security “Security by Design”
Security Governance
Further improvement “Life Cycle Management”
・Event, Incident, Monitoring
・External Communication
・Education, Training
・Security Policy
・Governance Model
・Security Program Frame Work
Time
Matu
rity
・3rd Party Evaluation ・Risk Assessment ・Security Requirements (Regulation)
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Professional Education
Japan Biodesign TWIns(TWMU-Waseda)
RIKEN AIP
NICT AISTAIRC
応 用 基
Medical Engineering Cooperation
Government actions on education and R&D in “AI Strategy”
AI R&D Network Idea AI Staff training target (2025)
2,000/year
1.5 M/year All Elementary to College Level
250K/year
Experts
Application Basic
Literacy
Advanced Companies
Abroad National Lab
University
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Singapore-Japan Collaboration
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© Hitachi, Ltd. 2019. All rights reserved. © Hitachi, Ltd. 2019. All rights reserved. 49
● Efforts to Solve Common Social Agenda ● Co-create Innovation ● Harmonization in Medical Regulations
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Hitachi Young Leaders Initiative 2019
Long Term Commitment
July 9-12,2019 Singapore
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