出版：MarketsandMarkets（マーケッツアンドマーケッツ） 出版年月：2024年2月
Artificial Intelligence in Healthcare Market – Global Forecast to 2029
ヘルスケア市場における人工知能 (AI) : オファリング (ハードウェア、ソフトウェア、サービス)、テクノロジー (機械学習、自然言語処理)、用途 (医療画像と診断、患者データとリスク分析)、エンド ユーザーと地域別 – 2029年までの世界予測
Artificial Intelligence (AI) in Healthcare Market by Offering (Hardware, Software, Services), Technology (Machine Learning, Natural Language Processing), Application (Medical Imaging & Diagnostics, Patient Data & Risk Analysis), End User & Region – Global Forecast to 2029
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| 種別 | 英文調査報告書 |
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Report Overview
The AI in Healthcare market is projected to grow from USD 20.9 billion in 2024 and is projected to reach USD 148.4 billion by 2029; it is expected to grow at a CAGR of 48.1% from 2024 to 2029.
ヘルスケア市場における人工知能 (AI) は、2024 年の 209 億米ドルから成長すると予測されており、2029 年までに 1,484 億米ドルに達すると予測されています。 2024 年から 2029 年にかけて 48.1% の CAGR で成長すると予想されています。
Strong focus on AI in Healthcare rising potential of AI-based tools for elderly care, increasing trend towards developing human-aware AI systems, and acceleration of AI technology in drug discovery, genomics, and imaging & diagnostics to fuel the growth of AI in Healthcare market.
“Market for Software to hold the largest share during the forecast period.”
The software segment is categorized into AI Platform and AI Solution. Software is the foundational element driving the integration and functionality of AI in healthcare. Acting as the catalyst for the AI brain, it enables the implementation of intricate machine learning algorithms such as natural language processing and deep learning. These algorithms, supported by efficient data ingestion and management facilitated by software, empower AI systems to analyze extensive medical datasets and derive valuable insights. In practical application, software plays a pivotal role in diagnostic tools, treatment personalization, and virtual assistants, enhancing the accuracy in disease detection, treatment planning, and patient engagement. Additionally, the software optimizes healthcare operations through administrative automation and predictive analytics, contributing to improved efficiency and proactive patient care. As the backbone of AI in healthcare, software transforms the landscape by offering innovative solutions for enhanced patient care, early diagnosis, and personalized treatment.
“Market for Natural Language Processing segment is projected to hold for second-largest share during the forecast timeline.”
The clinical and research community widely uses NLP in healthcare for efficient managing and development of unstructured and semi-structured textual documents, including electronic health reports, clinical notes, and pathology reports. The algorithm extracts health problems from narrative text clinical documents and proposes inclusion in a patient’s electronic problem list to interpret accurately. NLP involves four steps: document pre-processing, health problem detection, negation detection, and document post-processing. Babylon Health (UK) has developed an app and NLP algorithms to help a chatbot ask the same questions a doctor would ask during in-person examination. The app does not outline an official diagnosis; rather it uses speech and language processing to extract symptoms and forward the profile information to a doctor. NLP is experiencing significant demand from healthcare institutions for structuring and interpretation of clinical data more accurately. Moreover, the rising usage of connected devices, along with the massive volume of patients’ data, accelerates the growth of this market.
“Market for patient data & risk analysis segment holds for major market share during the forecast period.”
The convergence of machine learning (ML) and natural language processing (NLP) in healthcare offers significant advancements in predictive insights for patient health. Utilizing diverse data sources, ML models analyze medical records, lab tests, demographics, and social determinants to identify patients at risk of specific diseases, while NLP algorithms extract insights from clinical notes to spot early signs of illness. This synergy enables personalized treatment plans, considering factors like treatment response and lifestyle. ML predicts potential exacerbations, allowing proactive interventions, and NLP interprets real-time data for remote monitoring. The benefits include improved patient outcomes, reduced costs, and enhanced medical decision-making. However, challenges like data privacy, algorithmic bias, and the need for transparency underscore the importance of ethical and responsible AI implementation in healthcare.
“North America is expected to have the largest market share during the forecast period.”
The healthcare sector in North America is witnessing an influx of new entrants into the Artificial Intelligence (AI) landscape, driven by cross-industry involvement and a substantial rise in venture capital investments. An example is Navina (US), a startup dedicated to an AI-driven primary care platform, securing a substantial USD 44 million in its series B funding round in October 2022. These investments propel Navina’s AI and Machine Learning (ML) technology advancements. Another illustration is Tempus (US), specializing in AI-based precision medicine solutions, securing a notable USD 1.3 billion from 11 investors, including Ares Management and Google, in the same month.
Extensive primary interviews were conducted with key industry experts in the AI in Healthcare market space to determine and verify the market size for various segments and subsegments gathered through secondary research. The break-up of primary participants for the report has been shown below:
The break-up of the profile of primary participants in the AI in Healthcare market:
• By Company Type: Tier 1 – 50%, Tier 2 – 30%, and Tier 3 – 20%
• By Designation: C Level – 60%, Director Level – 30%, Others-10%
• By Region: North America – 40%, Europe – 20%, Asia Pacific – 30%, ROW- 10%
The report profiles key players in the AI in Healthcare market with their respective market ranking analysis. Prominent players profiled in this report are Koninklijke Philips N.V. (Netherlands), Microsoft (US), Siemens Healthineers AG (Germany), Intel Corporation (US), NVIDIA Corporation (US), Google Inc. (US), GE HealthCare Technologies Inc. (US), Oracle (US), and Johnson & Johnson Services, Inc. (US) among others.
Apart from this, Merative (US), General Vision, Inc., (US), CloudMedx (US), Oncora Medical (US), Enlitic (US), Lunit Inc., (South Korea), Qure.ai (India), Tempus (US), COTA (US), FDNA INC. (US), Recursion (US), Atomwise (US), Virgin Pulse (US), Babylon Health (UK), MDLIVE (US), Stryker (US), Qventus (US), Sweetch (Israel), Sirona Medical, Inc. (US), Ginger (US), Biobeat (Israel) are among a few emerging companies in the AI in Healthcare market.
Research Coverage:
This research report categorizes the AI in Healthcare market based on offering, technology, application, end user, and region. The report describes the major drivers, restraints, challenges, and opportunities pertaining to the AI in Healthcare market and forecasts the same till 2029. Apart from these, the report also consists of leadership mapping and analysis of all the companies included in the AI in Healthcare ecosystem.
Key Benefits of Buying the Report The report will help the market leaders/new entrants in this market with information on the closest approximations of the revenue numbers for the overall AI in Healthcare market and the subsegments. This report will help stakeholders understand the competitive landscape and gain more insights to position their businesses better and plan suitable go-to-market strategies. The report also helps stakeholders understand the pulse of the market and provides them with information on key market drivers, restraints, challenges, and opportunities.
The report provides insights on the following pointers:
• Analysis of key drivers (Generation of large and complex healthcare datasets, Pressing need to reduce healthcare costs, Improving computing power and declining hardware cost, Rising number of partnerships and collaborations among different domains in healthcare sector, and Growing need for improvised healthcare services due to imbalance between healthcare workforce and patients) influencing the growth of the AI in Healthcare market.
• Product Development/Innovation: Detailed insights on upcoming technologies, research & development activities, and new product & service launches in the AI in Healthcare market.
• Market Development: Comprehensive information about lucrative markets – the report analysis the AI in Healthcare market across varied regions
• Market Diversification: Exhaustive information about new products & services, untapped geographies, recent developments, and investments in the AI in Healthcare market
• Competitive Assessment: In-depth assessment of market shares, growth strategies, and service offerings of leading players like Koninklijke Philips N.V. (Netherlands), Microsoft (US), Siemens Healthineers AG (Germany), Intel Corporation (US), NVIDIA Corporation (US) among others in the AI in Healthcare market.
Table of Contents
1 INTRODUCTION 44
1.1 STUDY OBJECTIVES 44
1.2 MARKET DEFINITION 44
1.2.1 INCLUSIONS AND EXCLUSIONS 45
1.3 STUDY SCOPE 46
1.3.1 MARKETS COVERED 46
1.3.2 REGIONAL SCOPE 47
1.3.3 YEARS CONSIDERED 47
1.4 CURRENCY CONSIDERED 47
1.5 UNITS CONSIDERED 48
1.6 LIMITATIONS 48
1.7 STAKEHOLDERS 48
1.8 SUMMARY OF CHANGES 49
1.9 IMPACT OF RECESSION 50
FIGURE 1 GDP GROWTH PROJECTION DATA FOR MAJOR ECONOMIES, 2021–2023 50
1.10 GDP GROWTH PROJECTION UNTIL 2024 FOR MAJOR ECONOMIES 51
2 RESEARCH METHODOLOGY 52
2.1 RESEARCH DATA 52
FIGURE 2 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: RESEARCH DESIGN 52
2.1.1 SECONDARY DATA 53
2.1.1.1 List of major secondary sources 53
2.1.1.2 Key data from secondary sources 54
2.1.2 PRIMARY DATA 54
2.1.2.1 List of key interview participants 54
2.1.2.2 Key data from primary sources 55
2.1.2.3 Key industry insights 55
2.1.2.4 Breakdown of primaries 56
2.1.3 SECONDARY AND PRIMARY RESEARCH 56
2.2 MARKET SIZE ESTIMATION 57
FIGURE 3 RESEARCH FLOW: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET SIZE ESTIMATION 57
FIGURE 4 MARKET SIZE ESTIMATION METHODOLOGY (SUPPLY SIDE): REVENUE GENERATED BY COMPANIES FROM ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET 58
2.2.1 BOTTOM-UP APPROACH 58
2.2.1.1 Approach to estimate market size using bottom-up analysis (demand side) 58
FIGURE 5 MARKET SIZE ESTIMATION METHODOLOGY: BOTTOM-UP APPROACH 59
FIGURE 6 MARKET SIZE ESTIMATION METHODOLOGY: BOTTOM-UP APPROACH (DEMAND SIDE): REVENUE GENERATED FROM ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER 59
2.2.2 TOP-DOWN APPROACH 60
2.2.2.1 Approach to estimate market size using top-down analysis (supply side) 60
FIGURE 7 MARKET SIZE ESTIMATION METHODOLOGY: TOP-DOWN APPROACH 60
2.3 DATA TRIANGULATION 61
FIGURE 8 DATA TRIANGULATION 61
2.4 RESEARCH ASSUMPTIONS 62
2.5 RISK ASSESSMENT 62
2.6 PARAMETERS CONSIDERED TO ANALYZE RECESSION IMPACT ON STUDIED MARKET 63
2.7 RESEARCH LIMITATIONS 63
3 EXECUTIVE SUMMARY 64
FIGURE 9 SOFTWARE SEGMENT TO HOLD LARGEST MARKET SHARE IN 2029 64
FIGURE 10 MACHINE LEARNING SEGMENT TO DOMINATE MARKET DURING FORECAST PERIOD 65
FIGURE 11 PATIENTS SEGMENT TO REGISTER HIGHEST CAGR DURING FORECAST PERIOD 66
FIGURE 12 MEDICAL IMAGING & DIAGNOSTICS SEGMENT TO GROW AT HIGHEST CAGR DURING FORECAST PERIOD 67
FIGURE 13 NORTH AMERICA ACCOUNTED FOR LARGEST MARKET SHARE OF GLOBAL ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET IN 2023 68
4 PREMIUM INSIGHTS 69
4.1 ATTRACTIVE OPPORTUNITIES FOR PLAYERS IN AI IN HEALTHCARE MARKET 69
FIGURE 14 INCREASING ADOPTION OF AI-BASED TOOLS IN HEALTHCARE FACILITIES TO CREATE LUCRATIVE OPPORTUNITIES FOR MARKET PLAYERS 69
4.2 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY OFFERING 69
FIGURE 15 SOFTWARE SEGMENT TO ACCOUNT FOR LARGEST MARKET SHARE IN 2024 69
4.3 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TECHNOLOGY 70
FIGURE 16 MACHINE LEARNING TECHNOLOGY TO COMMAND MARKET FROM 2023 TO 2029 70
4.4 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER 70
FIGURE 17 HOSPITALS & HEALTHCARE PROVIDERS SEGMENT TO LEAD MARKET THROUGHOUT FORECAST PERIOD 70
4.5 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION 71
FIGURE 18 MEDICAL IMAGING & DIAGNOSTICS SEGMENT TO REGISTER HIGHEST GROWTH DURING FORECAST PERIOD 71
4.6 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY COUNTRY 71
FIGURE 19 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET IN MEXICO TO GROW AT HIGHEST CAGR FROM 2024 TO 2029 71
5 MARKET OVERVIEW 72
5.1 INTRODUCTION 72
5.2 MARKET DYNAMICS 72
FIGURE 20 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: DRIVERS, RESTRAINTS, OPPORTUNITIES, AND CHALLENGES 72
5.2.1 DRIVERS 73
FIGURE 21 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: DRIVERS AND THEIR IMPACT 73
5.2.1.1 Exponential growth in data volume and complexity due to surging adoption of digital technologies 73
5.2.1.2 Significant cost pressure on healthcare service providers with increasing prevalence of chronic diseases 74
5.2.1.3 Rapid proliferation of AI in healthcare sector 74
5.2.1.4 Growing need for improvised healthcare services 75
5.2.2 RESTRAINTS 76
FIGURE 22 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: RESTRAINTS AND THEIR IMPACT 76
5.2.2.1 Reluctance among medical practitioners to adopt AI-based technologies 76
5.2.2.2 Shortage of skilled AI professionals handling AI-powered solutions 77
5.2.2.3 Lack of standardized frameworks for AL and ML technologies 77
5.2.3 OPPORTUNITIES 78
FIGURE 23 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: OPPORTUNITIES AND THEIR IMPACT 78
5.2.3.1 Increasing use of AI-powered solutions in elderly care 78
5.2.3.2 Increasing focus on developing human-aware AI systems 79
5.2.3.3 Rising use of technology in pharmaceuticals industry 79
5.2.3.4 Strategic partnerships and collaborations among healthcare companies and AI technology providers 80
5.2.4 CHALLENGES 82
FIGURE 24 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: CHALLENGES AND THEIR IMPACT 82
5.2.4.1 Inaccurate predictions due to scarcity of high-quality healthcare data 82
5.2.4.2 Concerns regarding data privacy 83
FIGURE 25 DATA BREACHES IN HEALTHCARE SECTOR, 2019–2023 83
5.2.4.3 Lack of interoperability between AI solutions offered by different vendors 84
FIGURE 26 CHALLENGES ASSOCIATED WITH HEALTHCARE DATA INTEROPERABILITY 84
5.3 TRENDS/DISRUPTIONS IMPACTING CUSTOMERS’ BUSINESSES 85
FIGURE 27 TRENDS/DISRUPTIONS IMPACTING CUSTOMERS’ BUSINESSES 85
5.4 PRICING ANALYSIS 85
5.4.1 AVERAGE SELLING PRICE (ASP) TREND OF COMPONENTS OFFERED BY KEY PLAYERS, 2020–2029 86
FIGURE 28 AVERAGE SELLING PRICE (ASP) OF PROCESSOR COMPONENTS OFFERED BY KEY PLAYERS 86
TABLE 1 AVERAGE SELLING PRICE (ASP) OF PROCESSOR COMPONENTS OFFERED BY KEY PLAYERS 86
5.4.2 AVERAGE SELLING PRICE (ASP) TREND OF PROCESSOR COMPONENTS, BY REGION, 2020–2029 87
FIGURE 29 AVERAGE SELLING PRICE (ASP) TREND OF PROCESSOR COMPONENTS, BY REGION, 2020–2029 87
5.5 VALUE CHAIN ANALYSIS 88
FIGURE 30 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: VALUE CHAIN ANALYSIS 88
5.6 ECOSYSTEM MAPPING 89
FIGURE 31 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: ECOSYSTEM MAPPING 89
TABLE 2 COMPANIES AND THEIR ROLES IN ARTIFICIAL INTELLIGENCE IN HEALTHCARE ECOSYSTEM 90
5.7 TECHNOLOGY ANALYSIS 91
5.7.1 CLOUD COMPUTING 91
5.7.2 CLOUD GPU 91
5.7.3 GENERATIVE AI 92
5.7.4 CLOUD-BASED PACS 92
5.7.5 MULTI-CLOUD 92
5.8 PATENT ANALYSIS 93
TABLE 3 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: INNOVATIONS AND PATENT REGISTRATIONS 93
FIGURE 32 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: PATENTS GRANTED, 2013–2023 97
FIGURE 33 TOP 10 PATENT OWNERS IN LAST 10 YEARS, 2013–2023 97
TABLE 4 TOP PATENT OWNERS IN ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET IN LAST 10 YEARS 97
5.9 TRADE ANALYSIS 98
FIGURE 34 IMPORT DATA FOR HS CODE 854231-COMPLIANT PRODUCTS, BY COUNTRY, 2018–2022 (USD MILLION) 98
FIGURE 35 EXPORT DATA FOR HS CODE 854231-COMPLIANT PRODUCTS, BY COUNTRY, 2018–2022 (USD MILLION) 99
5.10 KEY CONFERENCES AND EVENTS, 2024–2025 99
TABLE 5 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: LIST OF CONFERENCES AND EVENTS, 2024–2025 99
5.11 CASE STUDY ANALYSIS 102
5.11.1 BIOBEAT LAUNCHED HOME-BASED REMOTE PATIENT MONITORING KIT DURING PEAK WAVE OF COVID-19 102
5.11.2 MICROSOFT COLLABORATED WITH CLEVELAND CLINIC TO APPLY PREDICTIVE AND ADVANCED ANALYTICS TO IDENTIFY POTENTIAL AT-RISK PATIENTS UNDER ICU CARE 102
5.11.3 TGEN COLLABORATED WITH INTEL CORPORATION AND DELL TECHNOLOGIES TO ASSIST PHYSICIANS AND RESEARCHERS ACCELERATE DIAGNOSIS AND TREATMENT AT LOWER COST 103
5.11.4 INSILICO DEVELOPED ML-POWERED TOOLS FOR DRUG IDENTIFICATION AND CHEMISTRY42 FOR NOVEL COMPOUND DESIGN 103
5.11.5 GE HEALTHCARE IMPROVED PATIENT OUTCOMES BY REDUCING WORKFLOW PROCESSING TIME USING MEDICAL IMAGING DATA 104
5.12 TARIFFS, STANDARDS, AND REGULATORY LANDSCAPE 104
TABLE 6 MFN TARIFF FOR HS CODE 854231-COMPLIANT PRODUCTS EXPORTED BY US, 2022 104
TABLE 7 MFN TARIFF FOR HS CODE 854231-COMPLIANT PRODUCTS EXPORTED BY CHINA, 2022 105
TABLE 8 MFN TARIFF FOR HS CODE 854231-COMPLIANT PRODUCTS EXPORTED BY GERMANY, 2022 105
5.12.1 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS 106
TABLE 9 NORTH AMERICA: REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS 106
TABLE 10 EUROPE: REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS 107
TABLE 11 ASIA PACIFIC: REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS 108
TABLE 12 ROW: REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS 109
5.12.2 STANDARDS 110
5.12.2.1 ISO 22399:2020 110
5.12.2.2 IEC 62366:2015 110
5.12.2.3 Health Insurance Portability and Accountability Act (HIPAA) 110
5.12.2.4 EU General Data Protection Regulation (GDPR) 110
5.12.2.5 Fast Healthcare Interoperability Resources (HL7 FHIR) 110
5.12.2.6 Medical Device Regulation 111
5.12.2.7 World Health Organization Artificial intelligence for Health Guide 111
5.12.2.8 Algorithmic Justice League framework for assessing AI in healthcare 111
5.12.3 GOVERNMENT REGULATIONS 111
5.12.3.1 US 111
5.12.3.2 Europe 111
5.12.3.3 China 112
5.12.3.4 Japan 112
5.12.3.5 India 112
5.13 PORTER’S FIVE FORCES ANALYSIS 112
TABLE 13 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: PORTER’S FIVE FORCES ANALYSIS 112
FIGURE 36 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: PORTER’S FIVE FORCES ANALYSIS 113
5.13.1 THREAT OF NEW ENTRANTS 113
5.13.2 THREAT OF SUBSTITUTES 114
5.13.3 BARGAINING POWER OF SUPPLIERS 114
5.13.4 BARGAINING POWER OF BUYERS 114
5.13.5 INTENSITY OF COMPETITIVE RIVALRY 114
5.14 KEY STAKEHOLDERS AND BUYING CRITERIA 115
5.14.1 KEY STAKEHOLDERS IN BUYING PROCESS 115
FIGURE 37 INFLUENCE OF KEY STAKEHOLDERS ON BUYING PROCESS FOR TOP THREE END USERS 115
TABLE 14 INFLUENCE OF STAKEHOLDERS ON BUYING PROCESS FOR TOP THREE END USERS 115
5.14.2 BUYING CRITERIA 115
FIGURE 38 KEY BUYING CRITERIA FOR TOP THREE END USERS 115
TABLE 15 KEY BUYING CRITERIA FOR TOP THREE END USERS 116
6 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY OFFERING 117
6.1 INTRODUCTION 118
FIGURE 39 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY OFFERING 118
FIGURE 40 SOFTWARE SEGMENT TO DOMINATE MARKET DURING FORECAST PERIOD 119
TABLE 16 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY OFFERING, 2020–2023 (USD MILLION) 119
TABLE 17 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY OFFERING, 2024–2029 (USD MILLION) 119
6.2 HARDWARE 120
TABLE 18 HARDWARE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2020–2023 (USD MILLION) 120
TABLE 19 HARDWARE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2024–2029 (USD MILLION) 120
TABLE 20 HARDWARE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 121
TABLE 21 HARDWARE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 121
6.2.1 PROCESSOR 121
6.2.1.1 Need for real-time processing of patient data to boost demand 121
TABLE 22 PROCESSOR: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2020–2023 (MILLION UNITS) 122
TABLE 23 PROCESSOR: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2024–2029 (MILLION UNITS) 123
TABLE 24 PROCESSOR: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2020–2023 (USD MILLION) 123
TABLE 25 PROCESSOR: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2024–2029 (USD MILLION) 123
6.2.1.2 MPUs/CPUs 124
TABLE 26 CASE STUDY: PHILIPS COLLABORATED WITH INTEL CORPORATION TO OPTIMIZE AI INFERENCING HEALTHCARE WORKLOADS ON INTEL XEON SCALABLE PROCESSORS USING OPENVINO TOOLKIT 124
6.2.1.3 GPUs 124
TABLE 27 CASE STUDY: DEEPPHARMA PLATFORM, OFFERED BY INSILICO, EQUIPPED WITH ADVANCED DEEP LEARNING TECHNIQUES, HELPS ANALYZE MULTI-OMICS DATA AND TISSUE-SPECIFIC PATHWAY ACTIVATION PROFILES 125
6.2.1.4 FPGAs 125
TABLE 28 CASE STUDY: INTEL CORPORATION, IN COLLABORATION WITH BROAD INSTITUTE, DEVELOPED BIGSTACK* 2.0 TO MEET EVOLVING DEMANDS OF GENOMICS RESEARCH 126
6.2.1.5 ASICs 126
6.2.2 MEMORY 127
6.2.2.1 Increasing demand for real-time medical image analysis and diagnosis support systems to drive market 127
TABLE 29 CASE STUDY: HUAWEI ASSISTED TOULOUSE UNIVERSITY HOSPITAL WITH OCEANSTOR ALL-FLASH SOLUTION THAT OFFERS LOW LATENCY AND SIMPLIFIED OPERATIONS AND MAINTENANCE MANAGEMENT 128
6.2.3 NETWORK 128
6.2.3.1 Growing need for remote patient monitoring and precision medicine to foster segmental growth 128
TABLE 30 NETWORK: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2020–2023 (USD MILLION) 129
TABLE 31 NETWORK: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2024–2029 (USD MILLION) 129
6.3 SOFTWARE 129
TABLE 32 SOFTWARE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2020–2023 (USD MILLION) 130
TABLE 33 SOFTWARE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2024–2029 (USD MILLION) 130
TABLE 34 SOFTWARE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 131
TABLE 35 SOFTWARE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 131
6.3.1 AI SOLUTION 131
6.3.1.1 Integration of non-procedural languages into AI solutions to accelerate segmental growth 131
TABLE 36 CASE STUDY: COGNIZANT LEVERAGED AZURE PLATFORM OF MICROSOFT AND DEVELOPED RESOLV, THAT EMPLOYS NATURAL LANGUAGE PROCESSING TO PROVIDE REAL-TIME RESPONSE TO ANALYTICAL QUERIES 132
TABLE 37 SOFTWARE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET FOR AI SOLUTIONS, BY DEPLOYMENT TYPE, 2020–2023 (USD MILLION) 132
TABLE 38 SOFTWARE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET FOR AI SOLUTIONS, BY DEPLOYMENT TYPE, 2024–2029 (USD MILLION) 133
6.3.1.2 On-premises 133
TABLE 39 CASE STUDY: GE HEALTHCARE ENHANCED ON-PREMISES CAPABILITY WITH SCYLLADB’S PROJECT ALTERNATOR 133
6.3.1.3 Cloud 134
TABLE 40 CASE STUDY: TAKEDA COLLABORATED WITH DELOITTE TO EMPLOY DEEP MINER TOOLKIT FOR RAPID DEVELOPMENT AND TESTING OF PREDICTIVE MODELS 134
6.3.2 AI PLATFORM 135
6.3.2.1 Increasing applications in development of toolkits for healthcare solutions to drive market 135
TABLE 41 CASE STUDY: CAYUGA MEDICAL CENTER SOUGHT SIMPLE CDI SOFTWARE SOLUTION TO IMPROVE WORKFLOWS AND REDUCE COSTS 135
TABLE 42 SOFTWARE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET FOR AI PLATFORMS, BY TYPE, 2020–2023 (USD MILLION) 136
TABLE 43 SOFTWARE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET FOR AI PLATFORMS, BY TYPE, 2024–2029 (USD MILLION) 136
6.3.2.2 Machine learning framework 136
6.3.2.3 Application program interface 137
6.4 SERVICES 137
TABLE 44 SERVICES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2020–2023 (USD MILLION) 137
TABLE 45 SERVICES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2024–2029 (USD MILLION) 138
TABLE 46 SERVICES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 138
TABLE 47 SERVICES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 138
6.4.1 DEPLOYMENT & INTEGRATION 139
6.4.1.1 Enhanced patient care along with streamlines workflows to drive demand 139
6.4.2 SUPPORT & MAINTENANCE 139
6.4.2.1 Need to evaluate performance and maintain operational stability to drive market 139
7 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TECHNOLOGY 140
7.1 INTRODUCTION 141
FIGURE 41 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TECHNOLOGY 141
FIGURE 42 MACHINE LEARNING TECHNOLOGY TO LEAD MARKET DURING FORECAST PERIOD 142
TABLE 48 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TECHNOLOGY, 2020–2023 (USD MILLION) 142
TABLE 49 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TECHNOLOGY, 2024–2029 (USD MILLION) 142
7.2 MACHINE LEARNING 143
TABLE 50 CASE STUDY: IN COLLABORATION WITH INTEL AND APOQLAR, THEBLUE.AI INTRODUCED BLUW.GDPR. EQUIPPED WITH ML ALGORITHMS ACCELERATED BY OPENVINO TOOLKIT 143
TABLE 51 MACHINE LEARNING: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2020–2023 (USD MILLION) 144
TABLE 52 MACHINE LEARNING: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2024–2029 (USD MILLION) 144
7.2.1 DEEP LEARNING 144
7.2.1.1 Rising applications in voice recognition, fraud detection, and recommendation engines to drive market 144
TABLE 53 WINNING HEALTH TECHNOLOGY INTRODUCED AI MEDICAL IMAGING SOLUTION BASED ON AMAX DEEP LEARNING ALL-IN-ONE TO REDUCE OVERALL MODEL INFERENCE TIME FROM OVER 0.5 HOURS TO LESS THAN 2 MINUTES FOR AI-AIDED DIAGNOSTIC IMAGING OF PULMONARY NODULES 146
7.2.2 SUPERVISED LEARNING 146
7.2.2.1 Contribution to clinical decision-making and enhancing personalized medications to boost demand 146
7.2.3 REINFORCEMENT LEARNING 147
7.2.3.1 Enhanced diagnostic accuracy in medical imaging analysis to fuel market growth 147
7.2.4 UNSUPERVISED LEARNING 147
7.2.4.1 Ability to uncover hidden patterns and handle unlabeled data challenges to boost demand 147
7.2.5 OTHERS 147
7.3 NATURAL LANGUAGE PROCESSING 148
TABLE 54 CASE STUDY: MARUTI TECHLABS ASSISTED UKHEALTH WITH ML MODEL FOR AUTOMATIC DATA EXTRACTION AND CLASSIFICATION 148
TABLE 55 NATURAL LANGUAGE PROCESSING: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2020–2023 (USD MILLION) 149
TABLE 56 NATURAL LANGUAGE PROCESSING: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2024–2029 (USD MILLION) 149
7.3.1 IVR 150
7.3.1.1 Enhanced operational efficiency and optimized clinical support to drive market 150
7.3.2 OCR 150
7.3.2.1 Reduced errors in data entry and streamlined administrative processes to spur demand 150
7.3.3 PATTERN AND IMAGE RECOGNITION 151
7.3.3.1 Optimized therapeutic outcomes and development of personal medication to foster segmental growth 151
7.3.4 AUTO CODING 152
7.3.4.1 Contribution to cost-saving and optimization of coding processes to drive market 152
7.3.5 CLASSIFICATION AND CATEGORIZATION 152
7.3.5.1 Accurate prediction of disease outcomes to boost demand 152
7.3.6 TEXT ANALYTICS 152
7.3.6.1 Significant contribution to drug discovery by examining extensive datasets of scientific literature to boost demand 152
7.3.7 SPEECH ANALYTICS 153
7.3.7.1 Contribution to sentiment analysis by assessing tone of patient conversations to boost demand 153
7.4 CONTEXT-AWARE COMPUTING 153
TABLE 57 CONTEXT-AWARE COMPUTING: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2020–2023 (USD MILLION) 154
TABLE 58 CONTEXT-AWARE COMPUTING: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2024–2029 (USD MILLION) 154
7.4.1 DEVICE CONTEXT 154
7.4.1.1 Ability to offer comprehensive view of patient data to boost demand 154
7.4.2 USER CONTEXT 155
7.4.2.1 Better predictive analysis for disease prevention to foster segmental growth 155
7.4.3 PHYSICAL CONTEXT 155
7.4.3.1 Ability to address individualized needs based on surrounding environment to boost market 155
7.5 COMPUTER VISION 155
7.5.1 ENHANCED PRECISION WITH 3D VISUALIZATIONS AND PERSONALIZED PROCEDURES TO FOSTER SEGMENTAL GROWTH 155
TABLE 59 CASE STUDY: PUNKTUM COLLABORATED WITH MAYO CLINIC TO DEVELOP CUTTING-EDGE DEEP LEARNING-BASED MODEL FOCUSED ON COMPUTER VISION FOR ACCURATE CLASSIFICATION OF ISCHEMIC STROKE ORIGINS 157
8 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION 158
8.1 INTRODUCTION 159
FIGURE 43 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION 159
FIGURE 44 MEDICAL IMAGING & DIAGNOSTICS SEGMENT TO ACCOUNT FOR LARGEST MARKET SHARE IN 2029 159
TABLE 60 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2020–2023 (USD MILLION) 160
TABLE 61 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2024–2029 (USD MILLION) 160
8.2 PATIENT DATA & RISK ANALYSIS 161
8.2.1 CONVERGENCE OF ML AND NLP TO OFFER LUCRATIVE GROWTH OPPORTUNITIES FOR PLAYERS 161
TABLE 62 CASE STUDY: MAYO CLINIC PARTNERED WITH GOOGLE TO IMPLEMENT AI MODELS AND ENHANCE PATIENT CARE 162
TABLE 63 PATIENT DATA & RISK ANALYSIS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 162
TABLE 64 PATIENT DATA & RISK ANALYSIS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 162
TABLE 65 PATIENT DATA & RISK ANALYSIS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 163
TABLE 66 PATIENT DATA & RISK ANALYSIS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 163
8.3 IN-PATIENT CARE & HOSPITAL MANAGEMENT 163
8.3.1 EASE OF PATIENT SCHEDULING WITH CHATBOTS AND VIRTUAL ASSISTANTS TO DRIVE MARKET 163
TABLE 67 CASE STUDY: PROMINENT MULTISPECIALTY HOSPITAL EMPLOYED ADOBE XD TO PREVENT RESOURCE WASTAGE AND ENHANCE EFFICIENCY 164
TABLE 68 IN-PATIENT CARE & HOSPITAL MANAGEMENT: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 165
TABLE 69 IN-PATIENT CARE & HOSPITAL MANAGEMENT: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 165
TABLE 70 IN-PATIENT CARE & HOSPITAL MANAGEMENT: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 165
TABLE 71 IN-PATIENT CARE & HOSPITAL MANAGEMENT: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 165
8.4 MEDICAL IMAGING & DIAGNOSTICS 166
8.4.1 ACCESSIBILITY IN MEDICAL IMAGING AND WORKFLOW OPTIMIZATION TO FOSTER SEGMENTAL GROWTH 166
TABLE 72 CASE STUDY: PHILIPS TRANSFORMED HEALTHCARE WITH AWS-POWERED AI SOLUTIONS 167
TABLE 73 MEDICAL IMAGING & DIAGNOSTICS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 167
TABLE 74 MEDICAL IMAGING & DIAGNOSTICS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 168
TABLE 75 MEDICAL IMAGING & DIAGNOSTICS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 168
TABLE 76 MEDICAL IMAGING & DIAGNOSTICS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 168
8.5 LIFESTYLE MANAGEMENT & REMOTE PATIENT MONITORING 169
8.5.1 ENHANCED PATIENT COMPLIANCE THROUGH BEHAVIORAL ANALYSIS TO BOOST DEMAND 169
TABLE 77 LIFESTYLE MANAGEMENT & REMOTE PATIENT MONITORING: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 170
TABLE 78 LIFESTYLE MANAGEMENT & REMOTE PATIENT MONITORING: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 171
TABLE 79 LIFESTYLE MANAGEMENT & REMOTE PATIENT MONITORING: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 171
TABLE 80 LIFESTYLE MANAGEMENT & REMOTE PATIENT MONITORING: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 171
8.6 VIRTUAL ASSISTANTS 171
8.6.1 ABILITY TO OFFER SIMPLIFIED COMPLEX MEDICAL INFORMATION TO DRIVE MARKET 171
TABLE 81 CASE STUDY: OSF COLLABORATED WITH GYANT TO IMPLEMENT CLARE, AI VIRTUAL CARE NAVIGATION ASSISTANT, BOOSTING DIGITAL HEALTH TRANSFORMATION 172
TABLE 82 VIRTUAL ASSISTANT: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 173
TABLE 83 VIRTUAL ASSISTANT: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 173
TABLE 84 VIRTUAL ASSISTANT: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 173
TABLE 85 VIRTUAL ASSISTANT: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 174
8.7 DRUG DISCOVERY 174
8.7.1 ACCELERATED IDENTIFICATION OF POTENTIAL DRUG CANDIDATES TO BOOST DEMAND 174
TABLE 86 CASE STUDY: AZOTHBIO UTILIZED RESCALE’S PLATFORM TO ENHANCE R&D AGILITY 175
TABLE 87 DRUG DISCOVERY: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 175
TABLE 88 DRUG DISCOVERY: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 175
TABLE 89 DRUG DISCOVERY: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 176
TABLE 90 DRUG DISCOVERY: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 176
8.8 RESEARCH 176
8.8.1 GROWING IMPORTANCE IN ANALYSIS OF SEQUENCE AND FUNCTIONAL PATTERNS FROM SEQUENCE DATABASES TO ACCELERATE DEMAND 176
TABLE 91 RESEARCH: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 177
TABLE 92 RESEARCH: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 177
TABLE 93 RESEARCH: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 177
TABLE 94 RESEARCH: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 178
8.9 HEALTHCARE ASSISTANCE ROBOTS 178
8.9.1 USE TO REVOLUTIONIZE PATIENT CARE BY STREAMLINING TASKS AND ENABLING REAL-TIME DATA ANALYSIS AND ENHANCE HEALTHCARE EXPERIENCES TO DRIVE MARKET 178
TABLE 95 HEALTHCARE ASSISTANCE ROBOTS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 179
TABLE 96 HEALTHCARE ASSISTANCE ROBOTS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 180
TABLE 97 HEALTHCARE ASSISTANCE ROBOTS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 180
TABLE 98 HEALTHCARE ASSISTANCE ROBOTS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 180
8.10 PRECISION MEDICINES 180
8.10.1 PERSONALIZED HEALTHCARE BY STREAMLINING CLINICAL TRIALS TO ACCELERATE DEMAND 180
TABLE 99 PRECISION MEDICINE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 181
TABLE 100 PRECISION MEDICINE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 181
TABLE 101 PRECISION MEDICINE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 182
TABLE 102 PRECISION MEDICINE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 182
8.11 EMERGENCY ROOMS & SURGERIES 182
8.11.1 QUICK IDENTIFICATION OF LIFE-THREATENING PATHOLOGIES TO FOSTER SEGMENTAL GROWTH 182
TABLE 103 EMERGENCY ROOMS & SURGERIES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 183
TABLE 104 EMERGENCY ROOMS & SURGERIES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 183
TABLE 105 EMERGENCY ROOMS & SURGERIES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 183
TABLE 106 EMERGENCY ROOMS & SURGERIES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 183
8.12 WEARABLES 184
8.12.1 PERSONALIZED TREATMENT STRATEGIES AND REAL-TIME INSIGHTS TO BOOST DEMAND 184
TABLE 107 CASE STUDY: KENSCI COLLABORATED WITH MICROSOFT TO ASSIST US NATIONAL GOVERNMENT IN IDENTIFYING PATIENTS WITH COPD 184
TABLE 108 WEARABLES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 185
TABLE 109 WEARABLES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 185
TABLE 110 WEARABLES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 185
TABLE 111 WEARABLES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 186
8.13 MENTAL HEALTH 186
8.13.1 PRESSING NEED TO DETECT DEPRESSION AND IDENTIFY SUICIDE RISKS THROUGH TEXT ANALYSIS TO DRIVE MARKET 186
TABLE 112 MENTAL HEALTH: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 187
TABLE 113 MENTAL HEALTH: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 187
TABLE 114 MENTAL HEALTH: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 187
TABLE 115 MENTAL HEALTH: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 188
8.14 CYBERSECURITY 188
8.14.1 PREVENTION OF INFILTRATION ATTEMPTS AND ENHANCED SPEED OF THREAT DETECTION TO BOOST DEMAND 188
TABLE 116 CASE STUDY: SNORKEL FLOW CREATED HIGH-ACCURACY ML MODELS TO OVERCOME HAND-LABELING CHALLENGES 189
TABLE 117 CYBERSECURITY: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 189
TABLE 118 CYBERSECURITY: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 189
TABLE 119 CYBERSECURITY: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 190
TABLE 120 CYBERSECURITY: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 190
9 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER 191
9.1 INTRODUCTION 192
FIGURE 45 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER 193
FIGURE 46 HOSPITALS & HEALTHCARE PROVIDERS TO HOLD LARGEST MARKET SHARE IN 2029 193
TABLE 121 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 194
TABLE 122 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 194
9.2 HOSPITALS & HEALTHCARE PROVIDERS 194
9.2.1 INCREASING USE IN MINING MEDICAL DATA AND STUDYING GENOMICS-BASED DATA FOR PERSONALIZED MEDICINE TO BOOST MARKET GROWTH 194
TABLE 123 CASE STUDY: UNIVERSITY COLLEGE LONDON, KING’S COLLEGE LONDON, AND NATIONAL HEALTH SERVICE COLLABORATION RESULTED IN DEVELOPMENT OF COGSTACK, THAT REVOLUTIONIZED HEALTHCARE DATA UTILIZATION 196
TABLE 124 HOSPITALS & HEALTHCARE PROVIDERS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2020–2023 (USD MILLION) 196
TABLE 125 HOSPITALS & HEALTHCARE PROVIDERS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2024–2029 (USD MILLION) 197
TABLE 126 HOSPITALS & HEALTHCARE PROVIDERS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 197
TABLE 127 HOSPITALS & HEALTHCARE PROVIDERS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 197
9.3 PATIENTS 198
9.3.1 RISE IN USE OF AI IN MENTAL HEALTH SUPPORT APPLICATIONS THROUGH CHATBOTS AND VIRTUAL THERAPISTS TO BOOST MARKET GROWTH 198
TABLE 128 CASE STUDY: COGNIZANT PARTNERED WITH ONE OF CLIENTS TO ENHANCE CALLER SELF-SERVICE AND IMPROVE MEMBER EXPERIENCE METRICS 199
TABLE 129 PATIENTS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2020–2023 (USD MILLION) 199
TABLE 130 PATIENTS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2024–2029 (USD MILLION) 199
TABLE 131 PATIENTS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 200
TABLE 132 PATIENTS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 200
9.4 PHARMACEUTICALS & BIOTECHNOLOGY COMPANIES 200
9.4.1 GROWING PARTNERSHIPS AMONG PLAYERS TO OFFER LUCRATIVE GROWTH OPPORTUNITIES TO PLAYERS 200
TABLE 133 CASE STUDY: AZURE MACHINE LEARNING-BASED INTELLIGENT SYSTEM ASSISTED LEADING PHARMA COMPANY TO AUTO-CLASSIFY PRODUCTS INTO MARKET-RELATED CATEGORIES THAT BOOSTED OPERATIONAL EFFICIENCY 202
TABLE 134 PHARMACEUTICALS & BIOTECHNOLOGY COMPANIES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2020–2023 (USD MILLION) 202
TABLE 135 PHARMACEUTICALS & BIOTECHNOLOGY COMPANIES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2024–2029 (USD MILLION) 203
TABLE 136 PHARMACEUTICALS & BIOTECHNOLOGY COMPANIES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 203
TABLE 137 PHARMACEUTICALS & BIOTECHNOLOGY COMPANIES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 203
9.5 HEALTHCARE PAYERS 204
9.5.1 FAST AND ACCURATE CLAIM PROCESSING AND ENHANCED FRAUD DETECTION BENEFITS TO BOOST DEMAND 204
TABLE 138 HEALTHCARE PAYERS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2020–2023 (USD MILLION) 205
TABLE 139 HEALTHCARE PAYERS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2024–2029 (USD MILLION) 205
TABLE 140 HEALTHCARE PAYERS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 205
TABLE 141 HEALTHCARE PAYERS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 206
9.6 OTHERS 206
TABLE 142 OTHERS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2020–2023 (USD MILLION) 207
TABLE 143 OTHERS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2024–2029 (USD MILLION) 207
TABLE 144 OTHERS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 207
TABLE 145 OTHERS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 208
10 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION 209
10.1 INTRODUCTION 210
FIGURE 47 ASIA PACIFIC TO REGISTER HIGHEST CAGR DURING FORECAST PERIOD 210
TABLE 146 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 211
TABLE 147 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 211
10.2 NORTH AMERICA 211
10.2.1 NORTH AMERICA: RECESSION IMPACT 212
FIGURE 48 NORTH AMERICA: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET SNAPSHOT 213
FIGURE 49 US TO DOMINATE NORTH AMERICAN ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET IN 2029 213
TABLE 148 NORTH AMERICA: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY COUNTRY, 2020–2023 (USD MILLION) 214
TABLE 149 NORTH AMERICA: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY COUNTRY, 2024–2029 (USD MILLION) 214
TABLE 150 NORTH AMERICA: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY OFFERING, 2020–2023 (USD MILLION) 214
TABLE 151 NORTH AMERICA: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY OFFERING, 2024–2029 (USD MILLION) 214
TABLE 152 NORTH AMERICA: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2020–2023 (USD MILLION) 215
TABLE 153 NORTH AMERICA: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2024–2029 (USD MILLION) 215
TABLE 154 NORTH AMERICA: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 216
TABLE 155 NORTH AMERICA: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 216
10.2.2 US 216
10.2.2.1 High healthcare spending in US to drive market 216
TABLE 156 US: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 218
TABLE 157 US: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 218
10.2.3 CANADA 218
10.2.3.1 Government-led initiatives to support deployment of AI in healthcare sector to boost demand 218
TABLE 158 CANADA: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 220
TABLE 159 CANADA: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 220
10.2.4 MEXICO 220
10.2.4.1 Increasing private sector investments in AI healthcare technologies to drive market 220
TABLE 160 MEXICO: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 221
TABLE 161 MEXICO: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 221
10.3 EUROPE 222
10.3.1 EUROPE: RECESSION IMPACT 222
FIGURE 50 EUROPE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET SNAPSHOT 223
FIGURE 51 REST OF EUROPE TO EXHIBIT HIGHEST CAGR IN EUROPEAN ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET DURING FORECAST PERIOD 224
TABLE 162 EUROPE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY COUNTRY, 2020–2023 (USD MILLION) 224
TABLE 163 EUROPE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY COUNTRY, 2024–2029 (USD MILLION) 224
TABLE 164 EUROPE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY OFFERING, 2020–2023 (USD MILLION) 225
TABLE 165 EUROPE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY OFFERING, 2024–2029 (USD MILLION) 225
TABLE 166 EUROPE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2020–2023 (USD MILLION) 225
TABLE 167 EUROPE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2024–2029 (USD MILLION) 226
TABLE 168 EUROPE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 226
TABLE 169 EUROPE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 227
10.3.2 GERMANY 227
10.3.2.1 Rising healthcare data generation to drive market 227
TABLE 170 GERMANY: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 228
TABLE 171 GERMANY: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 228
10.3.3 UK 228
10.3.3.1 Targeted treatment with increased success rates to fuel market growth 228
TABLE 172 UK: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 229
TABLE 173 UK: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 230
10.3.4 FRANCE 230
10.3.4.1 Focus on telemedicine and chronic disease management to drive market 230
TABLE 174 FRANCE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 231
TABLE 175 FRANCE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 231
10.3.5 ITALY 232
10.3.5.1 Rising geriatric population to drive market 232
TABLE 176 ITALY: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 232
TABLE 177 ITALY: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 233
10.3.6 SPAIN 233
10.3.6.1 Growing partnerships between technology firms and healthcare providers to boost demand 233
TABLE 178 SPAIN: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 234
TABLE 179 SPAIN: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 234
10.3.7 REST OF EUROPE 234
TABLE 180 REST OF EUROPE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 235
TABLE 181 REST OF EUROPE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 235
10.4 ASIA PACIFIC 236
10.4.1 ASIA PACIFIC: RECESSION IMPACT 236
FIGURE 52 ASIA PACIFIC: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET SNAPSHOT 237
FIGURE 53 CHINA TO EXHIBIT HIGHEST CAGR IN ASIA PACIFIC ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET DURING FORECAST PERIOD 238
TABLE 182 ASIA PACIFIC: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY COUNTRY, 2020–2023 (USD MILLION) 238
TABLE 183 ASIA PACIFIC: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY COUNTRY, 2024–2029 (USD MILLION) 238
TABLE 184 ASIA PACIFIC: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY OFFERING, 2020–2023 (USD MILLION) 239
TABLE 185 ASIA PACIFIC: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY OFFERING, 2024–2029 (USD MILLION) 239
TABLE 186 ASIA PACIFIC: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2020–2023 (USD MILLION) 239
TABLE 187 ASIA PACIFIC: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2024–2029 (USD MILLION) 240
TABLE 188 ASIA PACIFIC: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 240
TABLE 189 ASIA PACIFIC: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 241
10.4.2 CHINA 241
10.4.2.1 Government-led measures to expedite integration of AI into healthcare sector to drive market 241
TABLE 190 CHINA: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 242
TABLE 191 CHINA: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 242
10.4.3 JAPAN 243
10.4.3.1 Increasing number of AI-driven start-ups manufacturing diagnostic and therapeutic tools to fuel market growth 243
TABLE 192 JAPAN: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 243
TABLE 193 JAPAN: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 244
10.4.4 SOUTH KOREA 244
10.4.4.1 Increasing incidence of cancer to drive market 244
TABLE 194 SOUTH KOREA: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 245
TABLE 195 SOUTH KOREA: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 245
10.4.5 INDIA 246
10.4.5.1 Developing IT infrastructure and AI-friendly government initiatives to spur market growth 246
TABLE 196 INDIA: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 247
TABLE 197 INDIA: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 247
10.4.6 REST OF ASIA PACIFIC 247
TABLE 198 REST OF ASIA PACIFIC: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 248
TABLE 199 REST OF ASIA PACIFIC: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 249
10.5 ROW 249
10.5.1 ROW: RECESSION IMPACT 250
FIGURE 54 ROW: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET SNAPSHOT 250
FIGURE 55 SOUTH AMERICA TO DOMINATE ROW MARKET IN 2029 251
TABLE 200 ROW: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 251
TABLE 201 ROW: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 251
TABLE 202 ROW: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY OFFERING, 2020–2023 (USD MILLION) 252
TABLE 203 ROW: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY OFFERING, 2024–2029 (USD MILLION) 252
TABLE 204 ROW: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2020–2023 (USD MILLION) 252
TABLE 205 ROW: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2024–2029 (USD MILLION) 253
TABLE 206 ROW: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 253
TABLE 207 ROW: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 254
10.5.2 SOUTH AMERICA 254
10.5.2.1 High investments in healthcare IT to drive market 254
TABLE 208 SOUTH AMERICA: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 255
TABLE 209 SOUTH AMERICA: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 255
10.5.3 GCC 255
10.5.3.1 Rising focus on technological advancements in healthcare sector to drive market 255
TABLE 210 GCC: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 256
TABLE 211 GCC: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 256
10.5.4 REST OF MIDDLE EAST & AFRICA 257
10.5.4.1 Growing investments in information and communication technologies to boost demand 257
TABLE 212 REST OF MIDDLE EAST & AFRICA: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 257
TABLE 213 REST OF MEA: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 258
11 COMPETITIVE LANDSCAPE 259
11.1 OVERVIEW 259
11.2 STRATEGIES ADOPTED BY MAJOR PLAYERS, 2020–2023 259
TABLE 214 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: OVERVIEW OF STRATEGIES DEPLOYED BY KEY PLAYERS, 2020–2023 259
11.3 REVENUE ANALYSIS, 2019–2023 261
FIGURE 56 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: REVENUE ANALYSIS OF TOP FIVE PLAYERS, 2019–2023 261
11.4 MARKET SHARE ANALYSIS, 2023 261
FIGURE 57 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET SHARE ANALYSIS, 2023 262
TABLE 215 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET SHARE ANALYSIS, 2023 262
11.5 COMPANY EVALUATION MATRIX, 2023 264
11.5.1 STARS 264
11.5.2 EMERGING LEADERS 264
11.5.3 PERVASIVE PLAYERS 265
11.5.4 PARTICIPANTS 265
FIGURE 58 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: COMPANY EVALUATION MATRIX, 2023 265
11.5.5 COMPANY FOOTPRINT 266
TABLE 216 OVERALL COMPANY FOOTPRINT 266
TABLE 217 COMPANY OFFERING FOOTPRINT 266
TABLE 218 COMPANY END USER FOOTPRINT 267
TABLE 219 COMPANY REGION FOOTPRINT 267
11.6 START-UP/SMALL AND MEDIUM-SIZED ENTERPRISE (SME) EVALUATION MATRIX, 2023 268
11.6.1 PROGRESSIVE COMPANIES 268
11.6.2 RESPONSIVE COMPANIES 268
11.6.3 DYNAMIC COMPANIES 268
11.6.4 STARTING BLOCKS 268
FIGURE 59 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: START-UP/SME EVALUATION MATRIX, 2023 269
TABLE 220 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: LIST OF KEY START-UPS/SMES 269
11.6.5 COMPETITIVE BENCHMARKING 271
TABLE 221 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: COMPETITIVE BENCHMARKING OF KEY START-UPS/SMES 271
11.7 COMPETITIVE SCENARIOS AND TRENDS 272
11.7.1 PRODUCT LAUNCHES 272
TABLE 222 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: PRODUCT LAUNCHES, 2020 – 2023 272
11.7.2 DEALS 273
TABLE 223 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: DEALS, 2020 – 2023 273
12 COMPANY PROFILES 275
12.1 KEY PLAYERS 275
(Business overview, Products /Solutions/Services offered, Recent developments, Product launches, MnM view, Key strengths/Right to win, Strategic choices made, and Weaknesses and Competitive threats)*
12.1.1 KONINKLIJKE PHILIPS N.V. 275
TABLE 224 KONINKLIJKE PHILIPS N.V.: COMPANY OVERVIEW 276
FIGURE 60 KONINKLIJKE PHILIPS N.V.: COMPANY SNAPSHOT 276
TABLE 225 KONINKLIJKE PHILIPS N.V.: PRODUCTS/SOLUTIONS/SERVICES OFFERED 277
TABLE 226 KONINKLIJKE PHILIPS N.V.: PRODUCT LAUNCHES 279
TABLE 227 KONINKLIJKE PHILIPS N.V.: DEALS 280
TABLE 228 KONINKLIJKE PHILIPS N.V.: OTHERS 282
12.1.2 MICROSOFT 284
TABLE 229 MICROSOFT: COMPANY OVERVIEW 284
FIGURE 61 MICROSOFT: COMPANY SNAPSHOT 285
TABLE 230 MICROSOFT: PRODUCTS/SOLUTIONS/SERVICES OFFERED 285
TABLE 231 MICROSOFT: PRODUCT LAUNCHES 286
TABLE 232 MICROSOFT: DEALS 286
TABLE 233 MICROSOFT: OTHERS 288
12.1.3 SIEMENS HEALTHINEERS AG 290
TABLE 234 SIEMENS HEALTHINEERS AG: COMPANY OVERVIEW 290
FIGURE 62 SIEMENS HEALTHINEERS AG: COMPANY SNAPSHOT 291
TABLE 235 SIEMENS HEALTHINEERS AG: PRODUCTS/SOLUTIONS/SERVICES OFFERED 291
TABLE 236 SIEMENS HEALTHINEERS AG: PRODUCT LAUNCHES 292
TABLE 237 SIEMENS HEALTHINEERS AG: DEALS 293
TABLE 238 SIEMENS HEALTHINEERS AG: OTHERS 295
12.1.4 INTEL CORPORATION 297
TABLE 239 INTEL CORPORATION: COMPANY OVERVIEW 297
FIGURE 63 INTEL CORPORATION: COMPANY SNAPSHOT 298
TABLE 240 INTEL CORPORATION: PRODUCTS/SOLUTIONS/SERVICES OFFERED 298
TABLE 241 INTEL CORPORATION: PRODUCT LAUNCHES 299
TABLE 242 INTEL CORPORATION: DEALS 300
TABLE 243 INTEL CORPORATION: OTHERS 302
12.1.5 NVIDIA CORPORATION 304
TABLE 244 NVIDIA CORPORATION: COMPANY OVERVIEW 304
FIGURE 64 NVIDIA CORPORATION: COMPANY SNAPSHOT 305
TABLE 245 NVIDIA CORPORATION: PRODUCTS/SOLUTIONS/SERVICES OFFERED 305
TABLE 246 NVIDIA CORPORATION: PRODUCT LAUNCHES 306
TABLE 247 NVIDIA CORPORATION: DEALS 308
TABLE 248 NVIDIA CORPORATION: OTHERS 311
12.1.6 GOOGLE INC. 312
TABLE 249 GOOGLE INC.: COMPANY OVERVIEW 312
FIGURE 65 GOOGLE INC.: COMPANY SNAPSHOT 313
TABLE 250 GOOGLE INC.: PRODUCTS/SOLUTIONS/SERVICES OFFERED 313
TABLE 251 GOOGLE INC.: PRODUCT LAUNCHES 314
TABLE 252 GOOGLE INC.: DEALS 315
TABLE 253 GOOGLE INC.: OTHERS 316
12.1.7 GE HEALTHCARE 317
TABLE 254 GE HEALTHCARE: COMPANY OVERVIEW 317
FIGURE 66 GE HEALTHCARE: COMPANY SNAPSHOT 318
TABLE 255 GE HEALTHCARE: PRODUCTS/SOLUTIONS/SERVICES OFFERED 318
TABLE 256 GE HEALTHCARE: PRODUCT LAUNCHES 319
TABLE 257 GE HEALTHCARE: DEALS 319
12.1.8 MEDTRONIC 323
TABLE 258 MEDTRONIC: COMPANY OVERVIEW 323
FIGURE 67 MEDTRONIC: COMPANY SNAPSHOT 324
TABLE 259 MEDTRONIC: PRODUCTS/SOLUTIONS/SERVICES OFFERED 324
TABLE 260 MEDTRONIC: DEALS 325
12.1.9 MICRON TECHNOLOGY, INC. 327
TABLE 261 MICRON TECHNOLOGY, INC.: COMPANY OVERVIEW 327
FIGURE 68 MICRON TECHNOLOGY, INC.: COMPANY SNAPSHOT 328
TABLE 262 MICRON TECHNOLOGY, INC.: PRODUCTS/SOLUTIONS/SERVICES OFFERED 328
TABLE 263 MICRON TECHNOLOGY, INC: .PRODUCT LAUNCHES 329
TABLE 264 MICRON TECHNOLOGY, INC.: DEALS 330
12.1.10 AMAZON.COM, INC. 331
TABLE 265 AMAZON.COM, INC.: COMPANY OVERVIEW 331
FIGURE 69 AMAZON.COM, INC.: COMPANY SNAPSHOT 332
TABLE 266 AMAZON.COM, INC.: PRODUCTS/SOLUTIONS/SERVICES OFFERED 332
TABLE 267 AMAZON.COM, INC.: PRODUCT LAUNCHES 333
TABLE 268 AMAZON.COM, INC.: DEALS 334
12.1.11 ORACLE 336
TABLE 269 ORACLE: COMPANY OVERVIEW 336
FIGURE 70 ORACLE: COMPANY SNAPSHOT 337
TABLE 270 ORACLE: PRODUCTS/SOLUTIONS/SERVICES OFFERED 337
TABLE 271 ORACLE: PRODUCT LAUNCHES 338
TABLE 272 ORACLE: DEALS 338
12.1.12 JOHNSON & JOHNSON SERVICES, INC. 340
TABLE 273 JOHNSON & JOHNSON SERVICES, INC.: COMPANY OVERVIEW 340
FIGURE 71 JOHNSON & JOHNSON SERVICES, INC.: COMPANY SNAPSHOT 341
TABLE 274 JOHNSON & JOHNSON SERVICES, INC.: PRODUCTS/SOLUTIONS/SERVICES OFFERED 341
TABLE 275 JOHNSON & JOHNSON SERVICES, INC.: DEALS 342
12.2 OTHER PLAYERS 343
12.2.1 MERATIVE 343
12.2.2 GENERAL VISION INC. 344
12.2.3 CLOUDMEDX 345
12.2.4 ONCORA MEDICAL 346
12.2.5 ENLITIC, INC. 347
12.2.6 LUNIT INC. 348
12.2.7 QURE.AI 349
12.2.8 TEMPUS 350
12.2.9 COTA 351
12.2.10 FDNA INC. 352
12.2.11 RECURSION 353
12.2.12 ATOMWISE INC. 354
12.2.13 VIRGIN PULSE 355
12.2.14 BABYLON HEALTHCARE SERVICES LTD 356
12.2.15 MDLIVE (EVERNORTH GROUP) 357
12.2.16 STRYKER 358
12.2.17 QVENTUS 359
12.2.18 SWEETCH 360
12.2.19 SIRONA MEDICAL, INC. 361
12.2.20 GINGER 362
12.2.21 BIOBEAT 363
*Details on Business overview, Products /Solutions/Services offered, Recent developments, Product launches, MnM view, Key strengths/Right to win, Strategic choices made, and Weaknesses and Competitive threats might not be captured in case of unlisted companies.
13 APPENDIX 364
13.1 DISCUSSION GUIDE 364
13.2 KNOWLEDGESTORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL 368
13.3 CUSTOMIZATION OPTIONS 370
13.4 RELATED REPORTS 370
13.5 AUTHOR DETAILS 371