【レポートの概要（一部）】
TABLE OF CONTENTS
1 INTRODUCTION 20
1.1 STUDY OBJECTIVES 20
1.2 MARKET DEFINITION 20
1.3 STUDY SCOPE 21
1.3.1 MARKETS COVERED 21
1.3.2 YEARS CONSIDERED 22
1.4 CURRENCY 22
1.5 STAKEHOLDERS 22
2 RESEARCH METHODOLOGY 23
2.1 RESEARCH DATA 23
2.1.1 SECONDARY AND PRIMARY RESEARCH 25
2.1.1.1 Key industry insights 25
2.1.2 SECONDARY DATA 26
2.1.2.1 List of major secondary sources 26
2.1.2.2 Secondary sources 26
2.1.3 PRIMARY DATA 27
2.1.3.1 Primary interviews with experts 27
2.1.3.2 Breakdown of primaries 27
2.1.3.3 Primary sources 28
2.2 MARKET SIZE ESTIMATION 28
2.2.1 BOTTOM-UP APPROACH 28
2.2.1.1 Approach for capturing the market share by bottom-up analysis (demand side) 29
2.2.2 TOP-DOWN APPROACH 30
2.2.2.1 Approach for capturing the market share by top-down analysis (supply side) 30
2.3 MARKET BREAKDOWN AND DATA TRIANGULATION 31
2.4 RESEARCH ASSUMPTIONS 32
3 EXECUTIVE SUMMARY 33
4 PREMIUM INSIGHTS 40
4.1 ATTRACTIVE OPPORTUNITIES IN AI IN HEALTHCARE MARKET 40
4.2 AI IN HEALTHCARE MARKET, BY OFFERING 41
4.3 AI IN HEALTHCARE MARKET, BY TECHNOLOGY 41
4.4 EUROPE: AI IN HEALTHCARE MARKET, BY END USER AND COUNTRY 42
4.5 AI IN HEALTHCARE MARKET, BY COUNTRY 43
5 MARKET OVERVIEW 44
5.1 INTRODUCTION 44
5.2 MARKET DYNAMICS 45
5.2.1 DRIVERS 46
5.2.1.1 Increasingly large and complex data set 46
5.2.1.2 Growing demand to reduce healthcare costs 47
5.2.1.3 Improving computing power and declining hardware cost 48
5.2.1.4 Growing number of cross-industry partnerships and collaborations 49
5.2.1.5 Rising need for improvised healthcare services due to imbalance between health workforce and patients 50
5.2.2 RESTRAINTS 50
5.2.2.1 Reluctance among medical practitioners to adopt AI-based technologies 50
5.2.2.2 Lack of skilled AI workforce and ambiguous regulatory guidelines for medical software 51
5.2.3 OPPORTUNITIES 52
5.2.3.1 Growing potential of AI-based tools for elderly care 52
5.2.3.2 Increasing focus on developing human-aware AI systems 52
5.2.4 CHALLENGES 53
5.2.4.1 Lack of curated healthcare data 53
5.2.4.2 Concerns regarding data privacy 53
5.2.4.3 Lack of interoperability between AI solutions offered
by different vendors 54
5.3 VALUE CHAIN ANALYSIS 55
5.4 CASE STUDIES 56
5.4.1 MAYO CLINIC’S CENTRE FOR INDIVIDUALIZED MEDICINE COLLABORATED WITH TEMPUS TO PERSONALIZE CANCER TREATMENT 56
5.4.2 MICROSOFT COLLABORATED WITH CLEVELAND CLINIC TO IDENTIFY POTENTIAL AT-RISK PATIENTS UNDER ICU CARE 56
5.4.3 NVIDIA AND MASSACHUSETTS GENERAL HOSPITAL PARTNERED
TO USE ARTIFICIAL INTELLIGENCE FOR ADVANCED RADIOLOGY, PATHOLOGY, & GENOMICS 56
5.4.4 MICROSOFT PARTNERED WITH WEIL CORNELL MEDICINE TO DEVELOP
AI-POWERED CHATBOT 57
5.4.5 PARTNERS HEALTHCARE AND GE HEALTHCARE ENTERED INTO 10-YEAR COLLABORATION FOR INTEGRATING AI ACROSS CONTINUUM OF CARE 57
6 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY OFFERING 58
6.1 INTRODUCTION 59
6.2 HARDWARE 60
6.2.1 PROCESSOR 61
6.2.1.1 MPU 62
6.2.1.2 GPU 63
6.2.1.3 FPGA 63
6.2.1.4 ASIC 63
6.2.2 MEMORY 63
6.2.2.1 High-bandwidth memory is being developed and deployed for
AI applications, independent of its computing architecture 63
6.2.3 NETWORK 64
6.2.3.1 NVIDIA (US), Intel (US) and Mellanox Technologies (Israel) are the key providers of network interconnect adapters for AI applications 64
6.3 SOFTWARE 64
6.3.1 AI SOLUTIONS 66
6.3.1.1 On-premises 66
6.3.1.1.1 Data-Sensitive enterprises prefer on-premises’ advanced NLP and ML tools to be used in AI solutions 66
6.3.1.2 Cloud 67
6.3.1.2.1 The cloud provides additional flexibility for business operations and real-time deployment ease to companies that are implementing real-time analytics 67
6.3.2 AI PLATFORM 67
6.3.2.1 Machine learning framework 68
6.3.2.1.1 Major tech companies such as Google, IBM, Microsoft are developing and offering their own ML frameworks 68
6.3.2.2 Application program interface (API) 68
6.3.2.2.1 APIs are used when programming graphical user interface (GUI) components 68
6.4 SERVICES 68
6.4.1 DEPLOYMENT & INTEGRATION 69
6.4.1.1 Need for deployment and integration services for AI hardware and software solutions is supplementing the growth of services 69
6.4.2 SUPPORT & MAINTENANCE 70
6.4.2.1 The ultimate objective of maintenance services is to keep the system at an acceptable standard 70
7 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TECHNOLOGY 71
7.1 INTRODUCTION 72
7.2 MACHINE LEARNING 73
7.2.1 DEEP LEARNING 74
7.2.1.1 Deep learning enables a machine to build a hierarchical representation. 74
7.2.2 SUPERVISED LEARNING 74
7.2.2.1 Classification and regression are major segmentation
of Supervised learning 74
7.2.3 REINFORCEMENT LEARNING 75
7.2.3.1 Reinforcement learning allows systems and software to determine ideal behaviour for maximizing performance of the systems 75
7.2.4 UNSUPERVISED LEARNING 75
7.2.4.1 Unsupervised learning include clustering methods consisting of algorithms with unlabelled training data 75
7.2.5 OTHERS 75
7.3 NATURAL LANGUAGE PROCESSING 76
7.3.1 NLP IS WIDELY USED BY THE CLINICAL AND RESEARCH COMMUNITY
IN HEALTHCARE 76
7.4 CONTEXT-AWARE COMPUTING 77
7.4.1 DEVELOPMENT OF MORE SOPHISTICATED HARD AND SOFT SENSORS HAS ACCELERATED THE GROWTH OF CONTEXT-AWARE COMPUTING 77
7.5 COMPUTER VISION 78
7.5.1 COMPUTER VISION TECHNOLOGY HAS SHOWN SIGNIFICANT APPLICATIONS IN SURGERY AND THERAPY 78
8 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET,
BY END-USE APPLICATION 79
8.1 INTRODUCTION 80
8.2 PATIENT DATA AND RISK ANALYSIS 81
8.2.1 GROWTH IN HEALTHCARE DATA HAS ESCALATED PATIENT DATA AND RISK ANALYSIS APPLICATION 81
8.3 INPATIENT CARE & HOSPITAL MANAGEMENT 83
8.3.1 DEMAND TO REDUCE THE OPERATIONAL COST IN HOSPITALS TO GENERATE DEMAND FOR AI-BASED IN-PATIENT CARE AND HOSPITAL MANAGEMENT 83
8.4 MEDICAL IMAGING & DIAGNOSTICS 84
8.4.1 GROWTH IN MEDICAL IMAGING DATA HAS PROPELLED THE GROWTH OF MEDICAL IMAGING AND DIAGNOSTICS APPLICATION 84
8.5 LIFESTYLE MANAGEMENT & MONITORING 85
8.5.1 AI SOLUTIONS FOR LIFESTYLE MANAGEMENT AND MONITORING HELPS PATIENTS IN MAKING HEALTHIER LIFESTYLE CHANGES 85
8.6 VIRTUAL ASSISTANT 87
8.6.1 INCREASING DEMAND TO IMPROVE FOLLOW-UP CARE, ESPECIALLY
FOR PATIENTS WITH CHRONIC DISEASES, IS DRIVING THE GROWTH OF VIRTUAL ASSISTANTS 87
8.7 DRUG DISCOVERY 88
8.7.1 AI TO REDUCE THE TIME AND COST REQUIRED IN DRUG DISCOVERY 88
8.8 RESEARCH 90
8.8.1 GROWING ADOPTION OF DIFFERENT TYPES OF AI ALGORITHMS AMONG BIOINFORMATICS RESEARCHERS, ESPECIALLY FOR CLASSIFYING AND MINING THEIR DATABASES, IS THE KEY APPLICATION OF AI IN RESEARCH 90
8.9 HEALTHCARE ASSISTANCE ROBOTS 91
8.9.1 HEALTHCARE ASSISTANCE ROBOTS HAVE BEEN ADOPTED IN SEVERAL AREAS THAT DIRECTLY AFFECT PATIENT CARE 91
8.10 PRECISION MEDICINE 93
8.10.1 AI IS EXPECTED TO FULFILL THE DEMAND FOR PERSONALIZED TREATMENT PLANS FOR PATIENTS ADMINISTERED WITH PRECISION MEDICINE 93
8.11 EMERGENCY ROOM & SURGERY 94
8.11.1 LIMITED WORKFORCE IN EMERGENCY ROOMS AND DEMAND TO SUPPORT CLINICIANS WITH SURGICAL DATA TO DRIVE THE GROWTH OF AI IN EMERGENCY ROOM AND SURGERY 94
8.12 WEARABLES 96
8.12.1 WEARABLE DEVICES ARE CLINICALLY USEFUL FOR IMPROVING REAL-TIME MONITORING OF PATIENTS 96
8.13 MENTAL HEALTH 97
8.13.1 INCREASE IN MENTAL DISORDERS ACROSS THE WORLD IS THE KEY FACTOR SUPPORTING THE GROWTH OF MENTAL HEALTH APPLICATION 97
9 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER 99
9.1 INTRODUCTION 100
9.2 HOSPITALS AND PROVIDERS 101
9.2.1 AI CAN BE UTILIZED TO PREDICT AND PREVENT READMISSIONS, AND IMPROVE OPERATIONS AMONG HOSPITALS AND PROVIDERS 101
9.3 PATIENTS 103
9.3.1 SMARTPHONE APPLICATIONS AND WEARABLES TO DRIVE THE ADOPTION OF AI AMONG PATIENTS 103
9.4 PHARMACEUTICAL AND BIOTECHNOLOGY COMPANIES 105
9.4.1 APPLICATIONS SUCH AS DRUG DISCOVERY, PRECISION MEDICINE,
AND RESEARCH TO DRIVE AI IN PHARMACEUTICAL AND BIOTECHNOLOGY COMPANIES 105
9.5 HEALTHCARE PAYERS 107
9.5.1 HEALTHCARE PAYERS USE AI TOOLS MAINLY FOR MANAGING RISK, IDENTIFYING CLAIMS TRENDS, AND MAXIMIZING PAYMENT ACCURACY 107
9.6 OTHERS 109
9.6.1 PATIENT DATA AND RISK ANALYTICS AND HEALTHCARE ASSISTANCE ROBOTS TO DRIVE THE GROWTH OF AI IN ACOS AND MCOS 109
10 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION 111
10.1 INTRODUCTION 112
10.2 NORTH AMERICA 114
10.2.1 US 117
10.2.1.1 High healthcare spending to complement the growth of AI in the US 117
10.2.2 CANADA 118
10.2.2.1 Continuous research on NLP and ML across research institutions and universities in Canada propels the AI in healthcare market 118
10.2.3 MEXICO 118
10.2.3.1 AI-enabled devices for the healthcare sector have been gaining traction in Mexico 118
10.3 EUROPE 119
10.3.1 GERMANY 123
10.3.1.1 Rising healthcare data generation in the country will be a key driving factor for the growth of the market 123
10.3.2 UK 123
10.3.2.1 Large volume of data generated by NHS and digital health solutions have paved the way for AI in the UK 123
10.3.3 FRANCE 124
10.3.3.1 Healthcare IT in France has witnessed considerable implementation of AI over the last decade 124
10.3.4 ITALY 125
10.3.4.1 Development of electronic health records and aging population to drive the market in Italy 125
10.3.5 SPAIN 125
10.3.5.1 Growing awareness of AI in Spain to drive the AI in healthcare market 125
10.3.6 REST OF EUROPE 126
10.4 ASIA PACIFIC 127
10.4.1 CHINA 131
10.4.1.1 High spending by the government in healthcare and official plans to digitize medical records to drive the AI in healthcare market in China 131
10.4.2 JAPAN 131
10.4.2.1 Aging population to drive the growth of AI in Japan 131
10.4.3 SOUTH KOREA 132
10.4.3.1 Quality healthcare services and rapid expansion of medical insurance coverage are among the driving factors of AI in healthcare market in South Korea 132
10.4.4 INDIA 133
10.4.4.1 Developing IT infrastructure in the country and AI-friendly
initiatives by the government are the key factors supporting
the growth of AI in India 133
10.4.5 REST OF ASIA PACIFIC 133
10.5 REST OF THE WORLD 134
10.5.1 SOUTH AMERICA 136
10.5.1.1 Increasing awareness about potential applications of AI in healthcare is driving the growth of the market in South America 136
10.5.2 MIDDLE EAST AND AFRICA 136
10.5.2.1 Growing healthcare expenditure in the Middle East and North Africa is a key growth driver of AI in healthcare market 136
11 COMPETITIVE LANDSCAPE 138
11.1 OVERVIEW 138
11.2 RANKING OF PLAYERS, 2017 140
11.3 COMPETITIVE SCENARIO 142
11.3.1 PRODUCT DEVELOPMENTS AND LAUNCHES 143
11.3.2 COLLABORATIONS, PARTNERSHIPS, AND STRATEGIC ALLIANCES 144
11.3.3 ACQUISITIONS & JOINT VENTURES 145
12 COMPANY PROFILES 147
12.1 KEY PLAYERS 147
(Business Overview, Products Offered, Recent Developments, SWOT Analysis, and MnM View)*
12.1.1 NVIDIA 147
12.1.2 INTEL 152
12.1.3 IBM 157
12.1.4 GOOGLE 161
12.1.5 MICROSOFT 165
12.1.6 GENERAL ELECTRIC (GE) COMPANY 169
12.1.7 SIEMENS HEALTHINEERS (A STRATEGIC UNIT OF THE SIEMENS GROUP) 173
12.1.8 MEDTRONIC 176
12.1.9 MICRON TECHNOLOGY 179
12.1.10 AMAZON WEB SERVICES (AWS) 182
* Business Overview, Products Offered, Recent Developments, SWOT Analysis, and MnM View might not be captured in case of unlisted companies.
12.2 OTHER MAJOR COMPANIES 185
12.2.1 JOHNSON & JOHNSON SERVICES 185
12.2.2 KONINKLIJKE PHILIPS 185
12.2.3 GENERAL VISION 186
12.3 COMPANY PROFILES, BY APPLICATION 187
12.3.1 PATIENT DATA & RISK ANALYSIS 187
12.3.1.1 CLOUDMEDX 187
12.3.1.2 ONCORA MEDICAL 187
12.3.1.3 ZEPHYR HEALTH 188
12.3.1.4 SENTRIAN 188
12.3.1.5 CARESKORE 189
12.3.1.6 LINGUAMATICS 189
12.3.2 MEDICAL IMAGING & DIAGNOSTICS 190
12.3.2.1 ENLITIC 190
12.3.2.2 BAY LABS 190
12.3.2.3 BUTTERFLY NETWORKS 191
12.3.2.4 IMAGIA CYBERNETICS 191
12.3.3 PRECISION MEDICINE 192
12.3.3.1 PRECISION HEALTH AI 192
12.3.3.2 COTA 192
12.3.3.3 FDNA 193
12.3.4 DRUG DISCOVERY 193
12.3.4.1 RECURSION PHARMACEUTICALS 193
12.3.4.2 ATOMWISE 194
12.3.4.3 DEEP GENOMICS 194
12.3.4.4 CLOUD PHARMACEUTICALS 195
12.3.5 LIFESTYLE MANAGEMENT & MONITORING 195
12.3.5.1 WELLTOK 195
12.3.5.2 VITAGENE 196
12.3.5.3 LUCINA HEALTH 196
12.3.6 VIRTUAL ASSISTANTS 197
12.3.6.1 NEXT IT (A VERINT SYSTEMS COMPANY) 197
12.3.6.2 BABYLON 197
12.3.6.3 MDLIVE 198
12.3.7 WEARABLES 198
12.3.7.1 MAGNEA 198
12.3.7.2 PHYSIQ 199
12.3.7.3 CYRCADIA HEALTH 199
12.3.8 EMERGENCY ROOM & SURGERY 200
12.3.8.1 CARESYNTAX 200
12.3.8.2 GAUSS SURGICAL 200
12.3.8.3 PERCEIVE3D 201
12.3.8.4 MAXQ AI 201
12.3.9 IN-PATIENT CARE & HOSPITAL MANAGEMENT 202
12.3.9.1 QVENTUS 202
12.3.9.2 WORKFUSION 202
12.3.10 RESEARCH 203
12.3.10.1 ICARBONX 203
12.3.10.2 DESKTOP GENETICS 203
12.3.11 MENTAL HEALTH 204
12.3.11.1 GINGER.IO 204
12.3.11.2 X2AI 204
12.3.11.3 BIOBEATS 205
12.3.12 HEALTHCARE ASSISTANCE ROBOTS 205
12.3.12.1 PILLO 205
12.3.12.2 CATALIA HEALTH 206
13 APPENDIX 207
13.1 INSIGHTS FROM INDUSTRY EXPERTS 207
13.2 DISCUSSION GUIDE 208
13.3 KNOWLEDGE STORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL 212
13.4 AVAILABLE CUSTOMIZATIONS 214
13.5 RELATED REPORTS 214
13.6 AUTHOR DETAILS 216
【レポート販売概要】
■ タイトル：ヘルスケアにおけるAI（人工知能）の世界市場予測（～2025年）■ 英文：Artificial Intelligence in Healthcare Market by Offering (Hardware, Software, Services), Technology (Machine Learning, NLP, Context-Aware Computing, Computer Vision), End-Use Application, End User, and Geography – Global Forecast to 2025
■ 発行日：2018年12月18日
■ 調査会社：MarketsandMarkets
■ 商品コード：SE 5225
■ 調査対象地域：グローバル
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