Artificial Intelligence (AI) in Healthcare Market

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

Report Code: SE 5225 Jan, 2024, by marketsandmarkets.com

Updated on : March 19, 2024

The global AI in Healthcare market size was valued at USD 20.9 billion in 2024 and is estimated to reach USD 148.4 billion by 2029, registering a CAGR of 48.1% during the forecast period. The growth of AI in the healthcare market is driven by the generation of large and complex healthcare datasets, the pressing need to reduce healthcare costs, improving computing power and declining hardware costs, and the rising number of partnerships and collaborations among different domains in the healthcare sector, and growing need for improvised healthcare services due to imbalance between healthcare workforce and patients.

Artificial Intelligence (AI) in Healthcare Market

Artificial Intelligence (AI) in Healthcare Market Statistics Forecast to 2029

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AI in Healthcare Market Dynamics:

Driver: Generation of large and complex healthcare datasets

Generating extensive and intricate healthcare datasets is a pivotal driver for AI in the healthcare Market. Advanced technologies enable the accumulation of diverse patient information, from medical records to genomic data. This abundance of data catalyzes AI applications, facilitating the identification of patterns and insights crucial for diagnostics, personalized medicine, and treatment planning. Integrating big data analytics and AI promises to revolutionize healthcare processes, enhancing accuracy and efficiency. In the coming years, the impact of this data-driven approach is expected to be high, ushering in a transformative era in healthcare delivery with improved patient outcomes and streamlined operations.

Restraint: Reluctance among medical practitioners to adopt AI-based technologies.

Stemming from concerns about job displacement, skepticism regarding AI system reliability, and unease over integration into established practices, this reluctance impedes the market's growth. Addressing the challenge requires significant investments in training, contributing to a learning curve that discourages healthcare professionals. Overcoming this obstacle necessitates focused initiatives that underscore education and foster collaboration between technology developers and healthcare institutions. Such efforts are crucial for fostering understanding and acceptance, enabling the realization of AI's potential in healthcare, including enhanced diagnostics, improved treatment plans, and ultimately superior patient outcomes.

Opportunities: The growing potential of AI-based tools for elderly care

Factors such as increased life expectancy, shifting demographics, and challenges in traditional caregiving contribute to the growing potential of AI in elderly care. AI holds the transformative potential to enhance elderly care, ensuring effective and affordable solutions. Through continuous health monitoring, AI facilitates early detection of health issues, while fall detection algorithms improve safety. AI-driven medication management ensures adherence to treatment plans, and personalized care plans optimize interventions based on individual health data. Cognitive assistance and social interaction facilitated by AI contribute to mental well-being, particularly for seniors with conditions like dementia. The integration of companion robots and virtual assistants addresses loneliness.

Moreover, AI streamlines routine tasks, improving resource allocation in healthcare settings and enhancing cost efficiency. AI in elderly care represents a paradigm shift, offering proactive, personalized, and cost-effective solutions to ensure the well-being of the aging population. AI opens opportunities for more effective applications in healthcare, such as predictive analytics for disease outbreaks, personalized treatment plans based on genetic profiles, and advanced diagnostic tools that improve accuracy and speed in identifying medical conditions.

Challenge: Lack of curated healthcare data

The profound potential of AI in healthcare faces a substantial impediment – the scarcity of curated healthcare data. This bottleneck hampers AI performance, leading to inaccurate predictions and potential patient harm. Data fragmentation, privacy concerns, high costs, and expertise barriers exacerbate the challenge. For instance, in November 2023, the World Health Organization (WHO) released guidelines outlining essential regulatory considerations for applying artificial intelligence (AI) in healthcare. Emphasizing safety, efficacy, and collaboration, the document addresses risks related to AI's use of health data, advocating for robust legal and regulatory frameworks to ensure privacy and security. The guidelines highlight six critical areas for regulating AI in healthcare: transparency, risk management, external validation of data, commitment to data quality, addressing complex regulations like GDPR and HIPAA, and encouraging collaboration among stakeholders. Proposed solutions encompass standardization initiatives, public-private partnerships for responsible data sharing, synthetic data generation, and AI-powered curation tools to streamline the process. Overcoming this obstacle requires proactive measures like data standardization, collaboration, and technological advancements. Addressing specific healthcare sub-areas, exploring ethical considerations, and analyzing regulatory roles will further enrich the understanding and advancement of AI in healthcare.

Artificial Intelligence (AI) in Healthcare Market Segmentation

Artificial Intelligence (AI) in Healthcare Market by Segmentation

The market for Software segment to hold largest market share during the forecast period.

The integration of non-procedural languages marks a transformative shift in the AI landscape of healthcare, traditionally dominated by procedural languages like Python and Java. These intuitive, declarative languages, such as SQL, offer a potential game-changer by emphasizing outcomes over step-by-step instructions. This shift democratizes AI development, enabling healthcare professionals to contribute directly, fostering collaboration, and leveraging domain expertise. Non-procedural languages enhance model explainability, streamline workflows, and focus on core clinical knowledge, promising significant segmental growth in areas like clinical decision support systems, medical imaging analysis, personalized medicine, and public health. Despite challenges, the potential benefits position non-procedural languages as a compelling avenue for advancing AI in healthcare, promising improved patient care and outcomes.

Deep learning segment in the machine learning technology to hold the largest share in the AI in Healthcare market during the forecast period.

Deep learning's transformative impact on healthcare lies in its ability to construct hierarchical representations through artificial neural networks (ANNs). These interconnected layers of neurons emulate the human brain's structure, learning from extensive datasets to extract intricate features and patterns. In medical imaging, deep learning excels in tasks like image classification, detecting diseases in X-rays and MRIs, and object segmentation for precise analysis. Natural Language Processing (NLP) enables the extraction of valuable information from clinical notes and research papers, facilitating diagnosis and drug discovery. Moreover, deep learning predicts molecular interactions in drug development and precision medicine, identifies drug targets, and tailors treatments based on individual genetic profiles. Clinical decision support, personalized healthcare plans, and predictive analytics further demonstrate the potential of deep learning.

Patient Data & Risk Analysis segment in application to hold the highest market share of the AI in Healthcare market during the forecast period.

Natural Language Processing (NLP) in healthcare enables computers to analyze, generate, and translate human language. It unlocks insights from unstructured data, streamlines tasks, empowers patients through chatbots, and enhances personalized medicine, revolutionizing healthcare delivery. Natural Language Processing (NLP) plays a pivotal role in revolutionizing patient data analysis and risk assessment within AI in healthcare. By converting unstructured text in medical records into structured data, NLP enables rapid and scalable analysis. It empowers clinicians to identify at-risk patients by detecting nuanced details often missed in structured data. For instance, in January 2023, IQVIA Inc’s (US) NLP Risk Adjustment Solution, applied by a large US healthcare payer, successfully automated, and digitalized their risk adjustment process, enhancing efficiency by over 25%. Utilizing NLP, they improved medical record reviews, enabling nurses to identify conditions more accurately and submit reimbursement claims to CMS with increased precision. The solution's clinically intelligent NLP, processing millions of records an hour, ensured high coding accuracy (>90% precision and recall), reduced review time, and provided a comprehensive audit trail for accepted ICD10-CM codes, enhancing overall risk adjustment submissions.

Patients segment to account for largest CAGR of the AI in Healthcare market during the forecast period.

Integrating artificial intelligence (AI) with smartphones and wearables is revolutionizing the healthcare landscape. This powerful combination is democratizing health data, allowing patients to actively participate in their well-being by tracking vital signs, sleep patterns, activity levels, and moods. The wealth of personal health data generated is analyzed by AI algorithms, enabling the identification of patterns, prediction of health risks, and personalization of treatment plans. This proactive and data-driven approach is reshaping healthcare, providing individuals with a deeper understanding of their health.

The AI in Healthcare market in the Asia Pacific is estimated to grow at a higher CAGR during the forecast period.

The factor driving the growth of the AI in healthcare Industry in the Asia Pacific region is the rise in the number of cancer patients in Asia Pacific countries. According to the report from the National Library of Medicine, in 2023, the Asia-Pacific region, home to over 60% of the global population, will account for half of all cancer cases and 58% of cancer-related deaths. Worldwide, there were 19.2 million new cancer cases and 9.9 million deaths, with the Asia-Pacific region witnessing nearly 50% of the new cases and over half of the cancer-related fatalities. These figures underscore the substantial cancer burden in the Asia-Pacific region. Given that nurses constitute more than half of the oncology healthcare workforce, acquiring new knowledge and embracing evidence-based practices is crucial for delivering efficient and effective cancer care.

Artificial Intelligence (AI) in Healthcare Market
 by Region

Artificial Intelligence (AI) in Healthcare Market by Region

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Top AI in Healthcare Companies  - Key Market Players:

Major vendors in the AI in healthcare companies include 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), Medtronic (US), Micron Technology, Inc (US), Amazon.com Inc (US), Oracle (US), and Johnson & Johnson Services, Inc. (US). 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.

AI in Healthcare Market Report Scope :

Report Metric

Details

Estimated Market Size  USD 20.9 billion in 2024 
Projected Market Size USD 148.4 billion by 2029
Growth Rate CAGR of 48.1% 

Market size available for years

2020—2029

Base year

2023

Forecast period

2024—2029

Segments covered

  • By Offering,
  • By Technology,
  • By Application, and
  • By End User and Region

Geographic regions covered

  • North America,
  • Europe,
  • Asia Pacific, and
  • RoW

Companies covered

The major players include 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), Medtronic (US), Micron Technology, Inc (US), Amazon.com Inc (US), Oracle (US), and Johnson & Johnson Services, Inc. (US) and Others- total 33 players have been covered.

Artificial Intelligence (AI) in Healthcare Market Highlights

This research report categorizes the AI in Healthcare market Offering, Technology, Application, and End User, and Region.

Segment

Subsegment

By Offering

  • Hardware
    • Processor
      • MPU
      • GPU
      • FPGA
      • ASIC
    • Memory
    • Network
      • Adapter
      • Switch
      • Interconnect
  • Software
    • AI Platform
      • Application Program Interface (API)
      • Machine learning Framework
    • AI Solution
      • On-Premise
      • Cloud
  • Services
    • Deployment and Integration
    • Support & Maintenance

By Technology:

  • Machine Learning
    • Deep Learning
    • Supervised
    • Unsupervised
    • Reinforcement Learning
    • Others (Reinforcement Learning, Semisupervised)
  • Natural Language Processing
    • IVR
    • OCR
    • Pattern and Image Recognition
    • Auto Coding
    • Classification and Categorization
    • Text Analytics
    • Speech Analytics
  • Context-Aware Computing
    • Device Context
    • User Context
    • Physical Context
  • Computer Vision

By Application:

  • Patient Data & Risk Analysis
  • Medical Imaging & Diagnostics
  • Precision Medicine
  • Drug Discovery
  • Lifestyle Management & Remote Patient Monitoring
  • Virtual Assistants
  • Wearables
  • In-Patient Care & Hospital Management
  • Research
  • Emergency Room & Surgery
  • Mental Health
  • Healthcare Assistance Robots
  • Cybersecurity

By End User:

  • Hospitals & healthcare providers
  • Healthcare Payers
  • Pharmaceutical & Biotechnology Companies
  • Patients
  • Others (ACOs & MCOs)

By Region:

  • North America
    • US
    • Canada
    • Mexico
  • Europe
    • Germany
    • UK
    • France
    • Italy
    • Spain
    • Rest of Europe
  • Asia Pacific
    • China
    • Japan
    • South Korea
    • India
    • Rest of Asia Pacific
  • Rest of the World (RoW)
    • South America
    • GCC
    • Rest of MEA

Recent Developments in AI In Helthcare Industry :

  • In October 2023, Microsoft (US) launched new data and AI solutions, Microsoft Cloud at the HLTH 2023 conference, aiming to empower healthcare organizations in unlocking insights and enhancing patient and clinician experiences. The introduced industry-specific data solutions in Microsoft Fabric provide a unified analytics platform, simplifying the integration of diverse health data sources and enabling secure access to valuable insights.
  • In November 2023, Koninklijke Philips N.V., (Netherlands) collaborated with Vestre Viken Health Trust in Norway, deploying its AI Manager platform to enhance radiology workflows. The AI-enabled bone fracture application streamlined diagnoses, allowing radiologists to focus on complex cases. This initiative, spanning 30 hospitals and serving around 3.8 million people, marked Philips' most extensive AI deployment in Europe, contributing to improved patient care and accelerated diagnostic processes.

Key Questions Addressed in the Report:

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TABLE OF CONTENTS
 
1 INTRODUCTION (Page No. - 44)
    1.1 STUDY OBJECTIVES 
    1.2 MARKET DEFINITION 
           1.2.1 INCLUSIONS AND EXCLUSIONS
    1.3 STUDY SCOPE 
           1.3.1 MARKETS COVERED
           1.3.2 REGIONAL SCOPE
           1.3.3 YEARS CONSIDERED
    1.4 CURRENCY CONSIDERED 
    1.5 UNITS CONSIDERED 
    1.6 LIMITATIONS 
    1.7 STAKEHOLDERS 
    1.8 SUMMARY OF CHANGES 
    1.9 IMPACT OF RECESSION 
           FIGURE 1 GDP GROWTH PROJECTION DATA FOR MAJOR ECONOMIES, 2021–2023
    1.10 GDP GROWTH PROJECTION UNTIL 2024 FOR MAJOR ECONOMIES 
 
2 RESEARCH METHODOLOGY (Page No. - 52)
    2.1 RESEARCH DATA 
           FIGURE 2 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: RESEARCH DESIGN
           2.1.1 SECONDARY DATA
                    2.1.1.1 List of major secondary sources
                    2.1.1.2 Key data from secondary sources
           2.1.2 PRIMARY DATA
                    2.1.2.1 List of key interview participants
                    2.1.2.2 Key data from primary sources
                    2.1.2.3 Key industry insights
                    2.1.2.4 Breakdown of primaries
           2.1.3 SECONDARY AND PRIMARY RESEARCH
    2.2 MARKET SIZE ESTIMATION 
           FIGURE 3 RESEARCH FLOW: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET SIZE ESTIMATION
           FIGURE 4 MARKET SIZE ESTIMATION METHODOLOGY (SUPPLY SIDE): REVENUE GENERATED BY COMPANIES FROM ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET
           2.2.1 BOTTOM-UP APPROACH
                    2.2.1.1 Approach to estimate market size using bottom-up analysis (demand side)
                               FIGURE 5 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET SIZE ESTIMATION METHODOLOGY: BOTTOM-UP APPROACH
                               FIGURE 6 MARKET SIZE ESTIMATION METHODOLOGY: BOTTOM-UP APPROACH (DEMAND SIDE): REVENUE GENERATED FROM ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER
           2.2.2 TOP-DOWN APPROACH
                    2.2.2.1 Approach to estimate market size using top-down analysis (supply side)
                               FIGURE 7 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET SIZE ESTIMATION METHODOLOGY: TOP-DOWN APPROACH
    2.3 DATA TRIANGULATION 
           FIGURE 8 DATA TRIANGULATION
    2.4 RESEARCH ASSUMPTIONS 
    2.5 RISK ASSESSMENT 
    2.6 PARAMETERS CONSIDERED TO ANALYZE RECESSION IMPACT ON STUDIED MARKET 
    2.7 RESEARCH LIMITATIONS 
 
3 EXECUTIVE SUMMARY (Page No. - 64)
    FIGURE 9 SOFTWARE SEGMENT TO HOLD LARGEST MARKET SHARE IN 2029
    FIGURE 10 MACHINE LEARNING SEGMENT TO DOMINATE MARKET DURING FORECAST PERIOD
    FIGURE 11 PATIENTS SEGMENT TO REGISTER HIGHEST CAGR DURING FORECAST PERIOD
    FIGURE 12 MEDICAL IMAGING & DIAGNOSTICS SEGMENT TO GROW AT HIGHEST CAGR DURING FORECAST PERIOD
    FIGURE 13 NORTH AMERICA ACCOUNTED FOR LARGEST MARKET SHARE OF GLOBAL ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET IN 2023
 
4 PREMIUM INSIGHTS (Page No. - 69)
    4.1 ATTRACTIVE OPPORTUNITIES FOR PLAYERS IN AI IN HEALTHCARE MARKET 
           FIGURE 14 INCREASING ADOPTION OF AI-BASED TOOLS IN HEALTHCARE FACILITIES TO CREATE LUCRATIVE OPPORTUNITIES FOR MARKET PLAYERS
    4.2 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY OFFERING 
           FIGURE 15 SOFTWARE SEGMENT TO ACCOUNT FOR LARGEST MARKET SHARE IN 2024
    4.3 AI IN HEALTHCARE MARKET, BY TECHNOLOGY 
           FIGURE 16 MACHINE LEARNING TECHNOLOGY TO COMMAND MARKET FROM 2023 TO 2029
    4.4 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER 
           FIGURE 17 HOSPITALS & HEALTHCARE PROVIDERS SEGMENT TO LEAD MARKET THROUGHOUT FORECAST PERIOD
    4.5 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION 
           FIGURE 18 MEDICAL IMAGING & DIAGNOSTICS SEGMENT TO REGISTER HIGHEST GROWTH DURING FORECAST PERIOD
    4.6 AI IN HEALTHCARE MARKET, BY COUNTRY 
           FIGURE 19 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET IN MEXICO TO GROW AT HIGHEST CAGR FROM 2024 TO 2029
 
5 MARKET OVERVIEW (Page No. - 72)
    5.1 INTRODUCTION 
    5.2 MARKET DYNAMICS 
           FIGURE 20 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: DRIVERS, RESTRAINTS, OPPORTUNITIES, AND CHALLENGES
           5.2.1 DRIVERS
                    FIGURE 21 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET : DRIVERS AND THEIR IMPACT
                    5.2.1.1 Exponential growth in data volume and complexity due to surging adoption of digital technologies
                    5.2.1.2 Significant cost pressure on healthcare service providers with increasing prevalence of chronic diseases
                    5.2.1.3 Rapid proliferation of AI in healthcare sector
                    5.2.1.4 Growing need for improvised healthcare services
           5.2.2 RESTRAINTS
                    FIGURE 22 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: RESTRAINTS AND THEIR IMPACT
                    5.2.2.1 Reluctance among medical practitioners to adopt AI-based technologies
                    5.2.2.2 Shortage of skilled AI professionals handling AI-powered solutions
                    5.2.2.3 Lack of standardized frameworks for AL and ML technologies
           5.2.3 OPPORTUNITIES
                    FIGURE 23 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: OPPORTUNITIES AND THEIR IMPACT
                    5.2.3.1 Increasing use of AI-powered solutions in elderly care
                    5.2.3.2 Increasing focus on developing human-aware AI systems
                    5.2.3.3 Rising use of technology in pharmaceuticals industry
                    5.2.3.4 Strategic partnerships and collaborations among healthcare companies and AI technology providers
           5.2.4 CHALLENGES
                    FIGURE 24 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: CHALLENGES AND THEIR IMPACT
                    5.2.4.1 Inaccurate predictions due to scarcity of high-quality healthcare data
                    5.2.4.2 Concerns regarding data privacy
                               FIGURE 25 DATA BREACHES IN HEALTHCARE SECTOR, 2019–2023
                    5.2.4.3 Lack of interoperability between AI solutions offered by different vendors
                               FIGURE 26 CHALLENGES ASSOCIATED WITH HEALTHCARE DATA INTEROPERABILITY
    5.3 TRENDS/DISRUPTIONS IMPACTING CUSTOMERS’ BUSINESSES 
           FIGURE 27 TRENDS/DISRUPTIONS IMPACTING CUSTOMERS’ BUSINESSES
    5.4 PRICING ANALYSIS 
           5.4.1 AVERAGE SELLING PRICE (ASP) TREND OF COMPONENTS OFFERED BY KEY PLAYERS, 2020–2029
                    FIGURE 28 AVERAGE SELLING PRICE (ASP) OF PROCESSOR COMPONENTS OFFERED BY KEY PLAYERS
                    TABLE 1 AVERAGE SELLING PRICE (ASP) OF PROCESSOR COMPONENTS OFFERED BY KEY PLAYERS
           5.4.2 AVERAGE SELLING PRICE (ASP) TREND OF PROCESSOR COMPONENTS, BY REGION, 2020–2029
                    FIGURE 29 AVERAGE SELLING PRICE (ASP) TREND OF PROCESSOR COMPONENTS, BY REGION, 2020–2029
    5.5 VALUE CHAIN ANALYSIS 
           FIGURE 30 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: VALUE CHAIN ANALYSIS
    5.6 ECOSYSTEM MAPPING 
           FIGURE 31 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: ECOSYSTEM MAPPING
           TABLE 2 COMPANIES AND THEIR ROLES IN ARTIFICIAL INTELLIGENCE IN HEALTHCARE ECOSYSTEM
    5.7 TECHNOLOGY ANALYSIS 
           5.7.1 CLOUD COMPUTING
           5.7.2 CLOUD GPU
           5.7.3 GENERATIVE AI
           5.7.4 CLOUD-BASED PACS
           5.7.5 MULTI-CLOUD
    5.8 PATENT ANALYSIS 
           TABLE 3 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: INNOVATIONS AND PATENT REGISTRATIONS
           FIGURE 32 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: PATENTS GRANTED, 2013–2023
           FIGURE 33 TOP 10 PATENT OWNERS IN LAST 10 YEARS, 2013–2023
           TABLE 4 TOP PATENT OWNERS IN ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET IN LAST 10 YEARS
    5.9 TRADE ANALYSIS 
           FIGURE 34 IMPORT DATA FOR HS CODE 854231-COMPLIANT PRODUCTS, BY COUNTRY, 2018–2022 (USD MILLION)
           FIGURE 35 EXPORT DATA FOR HS CODE 854231-COMPLIANT PRODUCTS, BY COUNTRY, 2018–2022 (USD MILLION)
    5.10 KEY CONFERENCES AND EVENTS, 2024–2025 
           TABLE 5 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: LIST OF CONFERENCES AND EVENTS, 2024–2025
    5.11 CASE STUDY ANALYSIS 
           5.11.1 BIOBEAT LAUNCHED HOME-BASED REMOTE PATIENT MONITORING KIT DURING PEAK WAVE OF COVID-19
           5.11.2 MICROSOFT COLLABORATED WITH CLEVELAND CLINIC TO APPLY PREDICTIVE AND ADVANCED ANALYTICS TO IDENTIFY POTENTIAL AT-RISK PATIENTS UNDER ICU CARE
           5.11.3 TGEN COLLABORATED WITH INTEL CORPORATION AND DELL TECHNOLOGIES TO ASSIST PHYSICIANS AND RESEARCHERS ACCELERATE DIAGNOSIS AND TREATMENT AT LOWER COST
           5.11.4 INSILICO DEVELOPED ML-POWERED TOOLS FOR DRUG IDENTIFICATION AND CHEMISTRY42 FOR NOVEL COMPOUND DESIGN
           5.11.5 GE HEALTHCARE IMPROVED PATIENT OUTCOMES BY REDUCING WORKFLOW PROCESSING TIME USING MEDICAL IMAGING DATA
    5.12 TARIFFS, STANDARDS, AND REGULATORY LANDSCAPE 
           TABLE 6 MFN TARIFF FOR HS CODE 854231-COMPLIANT PRODUCTS EXPORTED BY US, 2022
           TABLE 7 MFN TARIFF FOR HS CODE 854231-COMPLIANT PRODUCTS EXPORTED BY CHINA, 2022
           TABLE 8 MFN TARIFF FOR HS CODE 854231-COMPLIANT PRODUCTS EXPORTED BY GERMANY, 2022
           5.12.1 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
                    TABLE 9 NORTH AMERICA: REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
                    TABLE 10 EUROPE: REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
                    TABLE 11 ASIA PACIFIC: REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
                    TABLE 12 ROW: REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
           5.12.2 STANDARDS
                    5.12.2.1 ISO 22399:2020
                    5.12.2.2 IEC 62366:2015
                    5.12.2.3 Health Insurance Portability and Accountability Act (HIPAA)
                    5.12.2.4 EU General Data Protection Regulation (GDPR)
                    5.12.2.5 Fast Healthcare Interoperability Resources (HL7 FHIR)
                    5.12.2.6 Medical Device Regulation
                    5.12.2.7 World Health Organization Artificial intelligence for Health Guide
                    5.12.2.8 Algorithmic Justice League framework for assessing AI in healthcare
           5.12.3 GOVERNMENT REGULATIONS
                    5.12.3.1 US
                    5.12.3.2 Europe
                    5.12.3.3 China
                    5.12.3.4 Japan
                    5.12.3.5 India
    5.13 PORTER’S FIVE FORCES ANALYSIS 
           TABLE 13 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: PORTER’S FIVE FORCES ANALYSIS
           FIGURE 36 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: PORTER’S FIVE FORCES ANALYSIS
           5.13.1 THREAT OF NEW ENTRANTS
           5.13.2 THREAT OF SUBSTITUTES
           5.13.3 BARGAINING POWER OF SUPPLIERS
           5.13.4 BARGAINING POWER OF BUYERS
           5.13.5 INTENSITY OF COMPETITIVE RIVALRY
    5.14 KEY STAKEHOLDERS AND BUYING CRITERIA 
           5.14.1 KEY STAKEHOLDERS IN BUYING PROCESS
                    FIGURE 37 INFLUENCE OF KEY STAKEHOLDERS ON BUYING PROCESS FOR TOP THREE END USERS
                    TABLE 14 INFLUENCE OF STAKEHOLDERS ON BUYING PROCESS FOR TOP THREE END USERS
           5.14.2 BUYING CRITERIA
                    FIGURE 38 KEY BUYING CRITERIA FOR TOP THREE END USERS
                    TABLE 15 KEY BUYING CRITERIA FOR TOP THREE END USERS
 
6 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY OFFERING (Page No. - 117)
    6.1 INTRODUCTION 
           FIGURE 39 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY OFFERING
           FIGURE 40 SOFTWARE SEGMENT TO DOMINATE MARKET DURING FORECAST PERIOD
           TABLE 16  MARKET, BY OFFERING, 2020–2023 (USD MILLION)
           TABLE 17  MARKET, BY OFFERING, 2024–2029 (USD MILLION)
    6.2 HARDWARE 
           TABLE 18 HARDWARE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2020–2023 (USD MILLION)
           TABLE 19 HARDWARE: MARKET, BY TYPE, 2024–2029 (USD MILLION)
           TABLE 20 HARDWARE: MARKET, BY REGION, 2020–2023 (USD MILLION)
           TABLE 21 HARDWARE: MARKET, BY REGION, 2024–2029 (USD MILLION)
           6.2.1 PROCESSOR
                    6.2.1.1 Need for real-time processing of patient data to boost demand
                               TABLE 22 PROCESSOR: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2020–2023 (MILLION UNITS)
                               TABLE 23 PROCESSOR: MARKET, BY TYPE, 2024–2029 (MILLION UNITS)
                               TABLE 24 PROCESSOR: MARKET, BY TYPE, 2020–2023 (USD MILLION)
                               TABLE 25 PROCESSOR: MARKET, BY TYPE, 2024–2029 (USD MILLION)
                    6.2.1.2 MPUs/CPUs
                               TABLE 26 CASE STUDY: PHILIPS COLLABORATED WITH INTEL CORPORATION TO OPTIMIZE AI INFERENCING HEALTHCARE WORKLOADS ON INTEL XEON SCALABLE PROCESSORS USING OPENVINO TOOLKIT
                    6.2.1.3 GPUs
                               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
                    6.2.1.4 FPGAs
                               TABLE 28 CASE STUDY: INTEL CORPORATION, IN COLLABORATION WITH BROAD INSTITUTE, DEVELOPED BIGSTACK* 2.0 TO MEET EVOLVING DEMANDS OF GENOMICS RESEARCH
                    6.2.1.5 ASICs
           6.2.2 MEMORY
                    6.2.2.1 Increasing demand for real-time medical image analysis and diagnosis support systems to drive market
                               TABLE 29 CASE STUDY: HUAWEI ASSISTED TOULOUSE UNIVERSITY HOSPITAL WITH OCEANSTOR ALL-FLASH SOLUTION THAT OFFERS LOW LATENCY AND SIMPLIFIED OPERATIONS AND MAINTENANCE MANAGEMENT
           6.2.3 NETWORK
                    6.2.3.1 Growing need for remote patient monitoring and precision medicine to foster segmental growth
                               TABLE 30 NETWORK: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2020–2023 (USD MILLION)
                               TABLE 31 NETWORK: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2024–2029 (USD MILLION)
    6.3 SOFTWARE 
           TABLE 32 SOFTWARE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2020–2023 (USD MILLION)
           TABLE 33 SOFTWARE: MARKET, BY TYPE, 2024–2029 (USD MILLION)
           TABLE 34 SOFTWARE: MARKET, BY REGION, 2020–2023 (USD MILLION)
           TABLE 35 SOFTWARE: MARKET, BY REGION, 2024–2029 (USD MILLION)
           6.3.1 AI SOLUTION
                    6.3.1.1 Integration of non-procedural languages into AI solutions to accelerate segmental growth
                               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
                               TABLE 37 SOFTWARE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET FOR AI SOLUTIONS, BY DEPLOYMENT TYPE, 2020–2023 (USD MILLION)
                               TABLE 38 SOFTWARE: MARKET FOR AI SOLUTIONS, BY DEPLOYMENT TYPE, 2024–2029 (USD MILLION)
                    6.3.1.2 On-premises
                               TABLE 39 CASE STUDY: GE HEALTHCARE ENHANCED ON-PREMISES CAPABILITY WITH SCYLLADB’S PROJECT ALTERNATOR
                    6.3.1.3 Cloud
                               TABLE 40 CASE STUDY: TAKEDA COLLABORATED WITH DELOITTE TO EMPLOY DEEP MINER TOOLKIT FOR RAPID DEVELOPMENT AND TESTING OF PREDICTIVE MODELS
           6.3.2 AI PLATFORM
                    6.3.2.1 Increasing applications in development of toolkits for healthcare solutions to drive market
                               TABLE 41 CASE STUDY: CAYUGA MEDICAL CENTER SOUGHT SIMPLE CDI SOFTWARE SOLUTION TO IMPROVE WORKFLOWS AND REDUCE COSTS
                               TABLE 42 SOFTWARE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET FOR AI PLATFORMS, BY TYPE, 2020–2023 (USD MILLION)
                               TABLE 43 SOFTWARE: MARKET FOR AI PLATFORMS, BY TYPE, 2024–2029 (USD MILLION)
                    6.3.2.2 Machine learning framework
                    6.3.2.3 Application program interface
    6.4 SERVICES 
           TABLE 44 SERVICES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET , BY TYPE, 2020–2023 (USD MILLION)
           TABLE 45 SERVICES: AI  IN HEALTHCARE MARKET , BY TYPE, 2024–2029 (USD MILLION)
           TABLE 46 SERVICES: AI IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION)
           TABLE 47 SERVICES: AI IN HEALTHCARE MARKET , BY REGION, 2024–2029 (USD MILLION)
           6.4.1 DEPLOYMENT & INTEGRATION
                    6.4.1.1 Enhanced patient care along with streamlines workflows to drive demand
           6.4.2 SUPPORT & MAINTENANCE
                    6.4.2.1 Need to evaluate performance and maintain operational stability to drive market
 
7 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TECHNOLOGY (Page No. - 140)
    7.1 INTRODUCTION 
           FIGURE 41 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TECHNOLOGY
           FIGURE 42 MACHINE LEARNING TECHNOLOGY TO LEAD MARKET DURING FORECAST PERIOD
           TABLE 48 MARKET, BY TECHNOLOGY, 2020–2023 (USD MILLION)
           TABLE 49 MARKET, BY TECHNOLOGY, 2024–2029 (USD MILLION)
    7.2 MACHINE LEARNING 
           TABLE 50 CASE STUDY: IN COLLABORATION WITH INTEL AND APOQLAR, THEBLUE.AI INTRODUCED BLUW.GDPR. EQUIPPED WITH ML ALGORITHMS ACCELERATED BY OPENVINO TOOLKIT
           TABLE 51 MACHINE LEARNING: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2020–2023 (USD MILLION)
           TABLE 52 MACHINE LEARNING: AI IN HEALTHCARE MARKET, BY TYPE, 2024–2029 (USD MILLION)
           7.2.1 DEEP LEARNING
                    7.2.1.1 Rising applications in voice recognition, fraud detection, and recommendation engines to drive market
                               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
           7.2.2 SUPERVISED LEARNING
                    7.2.2.1 Contribution to clinical decision-making and enhancing personalized medications to boost demand
           7.2.3 REINFORCEMENT LEARNING
                    7.2.3.1 Enhanced diagnostic accuracy in medical imaging analysis to fuel market growth
           7.2.4 UNSUPERVISED LEARNING
                    7.2.4.1 Ability to uncover hidden patterns and handle unlabeled data challenges to boost demand
           7.2.5 OTHERS
    7.3 NATURAL LANGUAGE PROCESSING 
           TABLE 54 CASE STUDY: MARUTI TECHLABS ASSISTED UKHEALTH WITH ML MODEL FOR AUTOMATIC DATA EXTRACTION AND CLASSIFICATION
           TABLE 55 NATURAL LANGUAGE PROCESSING: AI IN HEALTHCARE MARKET , BY TYPE, 2020–2023 (USD MILLION)
           TABLE 56 NATURAL LANGUAGE PROCESSING: AI IN HEALTHCARE MARKET , BY TYPE, 2024–2029 (USD MILLION)
           7.3.1 IVR
                    7.3.1.1 Enhanced operational efficiency and optimized clinical support to drive market
           7.3.2 OCR
                    7.3.2.1 Reduced errors in data entry and streamlined administrative processes to spur demand
           7.3.3 PATTERN AND IMAGE RECOGNITION
                    7.3.3.1 Optimized therapeutic outcomes and development of personal medication to foster segmental growth
           7.3.4 AUTO CODING
                    7.3.4.1 Contribution to cost-saving and optimization of coding processes to drive market
           7.3.5 CLASSIFICATION AND CATEGORIZATION
                    7.3.5.1 Accurate prediction of disease outcomes to boost demand
           7.3.6 TEXT ANALYTICS
                    7.3.6.1 Significant contribution to drug discovery by examining extensive datasets of scientific literature to boost demand
           7.3.7 SPEECH ANALYTICS
                    7.3.7.1 Contribution to sentiment analysis by assessing tone of patient conversations to boost demand
    7.4 CONTEXT-AWARE COMPUTING 
           TABLE 57 CONTEXT-AWARE COMPUTING: AI IN HEALTHCARE MARKET , BY TYPE, 2020–2023 (USD MILLION)
           TABLE 58 CONTEXT-AWARE COMPUTING: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET , BY TYPE, 2024–2029 (USD MILLION)
           7.4.1 DEVICE CONTEXT
                    7.4.1.1 Ability to offer comprehensive view of patient data to boost demand
           7.4.2 USER CONTEXT
                    7.4.2.1 Better predictive analysis for disease prevention to foster segmental growth
           7.4.3 PHYSICAL CONTEXT
                    7.4.3.1 Ability to address individualized needs based on surrounding environment to boost market
    7.5 COMPUTER VISION 
           7.5.1 ENHANCED PRECISION WITH 3D VISUALIZATIONS AND PERSONALIZED PROCEDURES TO FOSTER SEGMENTAL GROWTH
                    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
 
8 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION (Page No. - 158)
    8.1 INTRODUCTION 
           FIGURE 43 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION
           FIGURE 44 MEDICAL IMAGING & DIAGNOSTICS SEGMENT TO ACCOUNT FOR LARGEST MARKET SHARE IN 2029
           TABLE 60 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2020–2023 (USD MILLION)
           TABLE 61 MARKET, BY APPLICATION, 2024–2029 (USD MILLION)
    8.2 PATIENT DATA & RISK ANALYSIS 
           8.2.1 CONVERGENCE OF ML AND NLP TO OFFER LUCRATIVE GROWTH OPPORTUNITIES FOR PLAYERS
                    TABLE 62 CASE STUDY: MAYO CLINIC PARTNERED WITH GOOGLE TO IMPLEMENT AI MODELS AND ENHANCE PATIENT CARE
                    TABLE 63 PATIENT DATA & RISK ANALYSIS: AI IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION)
                    TABLE 64 PATIENT DATA & RISK ANALYSIS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION)
                    TABLE 65 PATIENT DATA & RISK ANALYSIS: MARKET, BY END USER, 2020–2023 (USD MILLION)
                    TABLE 66 PATIENT DATA & RISK ANALYSIS: MARKET, BY END USER, 2024–2029 (USD MILLION)
    8.3 IN-PATIENT CARE & HOSPITAL MANAGEMENT 
           8.3.1 EASE OF PATIENT SCHEDULING WITH CHATBOTS AND VIRTUAL ASSISTANTS TO DRIVE MARKET
                    TABLE 67 CASE STUDY: PROMINENT MULTISPECIALTY HOSPITAL EMPLOYED ADOBE XD TO PREVENT RESOURCE WASTAGE AND ENHANCE EFFICIENCY
                    TABLE 68 IN-PATIENT CARE & HOSPITAL MANAGEMENT: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION)
                    TABLE 69 IN-PATIENT CARE & HOSPITAL MANAGEMENT: MARKET, BY REGION, 2024–2029 (USD MILLION)
                    TABLE 70 IN-PATIENT CARE & HOSPITAL MANAGEMENT: MARKET, BY END USER, 2020–2023 (USD MILLION)
                    TABLE 71 IN-PATIENT CARE & HOSPITAL MANAGEMENT: MARKET, BY END USER, 2024–2029 (USD MILLION)
    8.4 MEDICAL IMAGING & DIAGNOSTICS 
           8.4.1 ACCESSIBILITY IN MEDICAL IMAGING AND WORKFLOW OPTIMIZATION TO FOSTER SEGMENTAL GROWTH
                    TABLE 72 CASE STUDY: PHILIPS TRANSFORMED HEALTHCARE WITH AWS-POWERED AI SOLUTIONS
                    TABLE 73 MEDICAL IMAGING & DIAGNOSTICS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION)
                    TABLE 74 MEDICAL IMAGING & DIAGNOSTICS: MARKET, BY REGION, 2024–2029 (USD MILLION)
                    TABLE 75 MEDICAL IMAGING & DIAGNOSTICS: MARKET, BY END USER, 2020–2023 (USD MILLION)
                    TABLE 76 MEDICAL IMAGING & DIAGNOSTICS: MARKET, BY END USER, 2024–2029 (USD MILLION)
    8.5 LIFESTYLE MANAGEMENT & REMOTE PATIENT MONITORING 
           8.5.1 ENHANCED PATIENT COMPLIANCE THROUGH BEHAVIORAL ANALYSIS TO BOOST DEMAND
                    TABLE 77 LIFESTYLE MANAGEMENT & REMOTE PATIENT MONITORING: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION)
                    TABLE 78 LIFESTYLE MANAGEMENT & REMOTE PATIENT MONITORING: MARKET, BY REGION, 2024–2029 (USD MILLION)
                    TABLE 79 LIFESTYLE MANAGEMENT & REMOTE PATIENT MONITORING: MARKET, BY END USER, 2020–2023 (USD MILLION)
                    TABLE 80 LIFESTYLE MANAGEMENT & REMOTE PATIENT MONITORING: MARKET, BY END USER, 2024–2029 (USD MILLION)
    8.6 VIRTUAL ASSISTANTS 
           8.6.1 ABILITY TO OFFER SIMPLIFIED COMPLEX MEDICAL INFORMATION TO DRIVE MARKET
                    TABLE 81 CASE STUDY: OSF COLLABORATED WITH GYANT TO IMPLEMENT CLARE, AI VIRTUAL CARE NAVIGATION ASSISTANT, BOOSTING DIGITAL HEALTH TRANSFORMATION
                    TABLE 82 VIRTUAL ASSISTANT: AI IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION)
                    TABLE 83 VIRTUAL ASSISTANT: MARKET, BY REGION, 2024–2029 (USD MILLION)
                    TABLE 84 VIRTUAL ASSISTANT: MARKET, BY END USER, 2020–2023 (USD MILLION)
                    TABLE 85 VIRTUAL ASSISTANT: MARKET, BY END USER, 2024–2029 (USD MILLION)
    8.7 DRUG DISCOVERY 
           8.7.1 ACCELERATED IDENTIFICATION OF POTENTIAL DRUG CANDIDATES TO BOOST DEMAND
                    TABLE 86 CASE STUDY: AZOTHBIO UTILIZED RESCALE’S PLATFORM TO ENHANCE R&D AGILITY
                    TABLE 87 DRUG DISCOVERY: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION)
                    TABLE 88 DRUG DISCOVERY: MARKET, BY REGION, 2024–2029 (USD MILLION)
                    TABLE 89 DRUG DISCOVERY: MARKET, BY END USER, 2020–2023 (USD MILLION)
                    TABLE 90 DRUG DISCOVERY: MARKET, BY END USER, 2024–2029 (USD MILLION)
    8.8 RESEARCH 
           8.8.1 GROWING IMPORTANCE IN ANALYSIS OF SEQUENCE AND FUNCTIONAL PATTERNS FROM SEQUENCE DATABASES TO ACCELERATE DEMAND
                    TABLE 91 RESEARCH: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION)
                    TABLE 92 RESEARCH: MARKET, BY REGION, 2024–2029 (USD MILLION)
                    TABLE 93 RESEARCH: MARKET, BY END USER, 2020–2023 (USD MILLION)
                    TABLE 94 RESEARCH: MARKET, BY END USER, 2024–2029 (USD MILLION)
    8.9 HEALTHCARE ASSISTANCE ROBOTS 
           8.9.1 USE TO REVOLUTIONIZE PATIENT CARE BY STREAMLINING TASKS AND ENABLING REAL-TIME DATA ANALYSIS AND ENHANCE HEALTHCARE EXPERIENCES TO DRIVE MARKET
                    TABLE 95 HEALTHCARE ASSISTANCE ROBOTS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION)
                    TABLE 96 HEALTHCARE ASSISTANCE ROBOTS: MARKET, BY REGION, 2024–2029 (USD MILLION)
                    TABLE 97 HEALTHCARE ASSISTANCE ROBOTS: MARKET, BY END USER, 2020–2023 (USD MILLION)
                    TABLE 98 HEALTHCARE ASSISTANCE ROBOTS: MARKET, BY END USER, 2024–2029 (USD MILLION)
    8.10 PRECISION MEDICINES 
           8.10.1 PERSONALIZED HEALTHCARE BY STREAMLINING CLINICAL TRIALS TO ACCELERATE DEMAND
                    TABLE 99 PRECISION MEDICINE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION)
                    TABLE 100 PRECISION MEDICINE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION)
                    TABLE 101 PRECISION MEDICINE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION)
                    TABLE 102 PRECISION MEDICINE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION)
    8.11 EMERGENCY ROOMS & SURGERIES 
           8.11.1 QUICK IDENTIFICATION OF LIFE-THREATENING PATHOLOGIES TO FOSTER SEGMENTAL GROWTH
                    TABLE 103 EMERGENCY ROOMS & SURGERIES: MARKET, BY REGION, 2020–2023 (USD MILLION)
                    TABLE 104 EMERGENCY ROOMS & SURGERIES: MARKET, BY REGION, 2024–2029 (USD MILLION)
                    TABLE 105 EMERGENCY ROOMS & SURGERIES: MARKET, BY END USER, 2020–2023 (USD MILLION)
                    TABLE 106 EMERGENCY ROOMS & SURGERIES: MARKET, BY END USER, 2024–2029 (USD MILLION)
    8.12 WEARABLES 
           8.12.1 PERSONALIZED TREATMENT STRATEGIES AND REAL-TIME INSIGHTS TO BOOST DEMAND
                    TABLE 107 CASE STUDY: KENSCI COLLABORATED WITH MICROSOFT TO ASSIST US NATIONAL GOVERNMENT IN IDENTIFYING PATIENTS WITH COPD
                    TABLE 108 WEARABLES: MARKET, BY REGION, 2020–2023 (USD MILLION)
                    TABLE 109 WEARABLES: MARKET, BY REGION, 2024–2029 (USD MILLION)
                    TABLE 110 WEARABLES: MARKET, BY END USER, 2020–2023 (USD MILLION)
                    TABLE 111 WEARABLES: MARKET, BY END USER, 2024–2029 (USD MILLION)
    8.13 MENTAL HEALTH 
           8.13.1 PRESSING NEED TO DETECT DEPRESSION AND IDENTIFY SUICIDE RISKS THROUGH TEXT ANALYSIS TO DRIVE MARKET
                    TABLE 112 MENTAL HEALTH: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION)
                    TABLE 113 MENTAL HEALTH: MARKET, BY REGION, 2024–2029 (USD MILLION)
                    TABLE 114 MENTAL HEALTH: MARKET, BY END USER, 2020–2023 (USD MILLION)
                    TABLE 115 MENTAL HEALTH: MARKET, BY END USER, 2024–2029 (USD MILLION)
    8.14 CYBERSECURITY 
           8.14.1 PREVENTION OF INFILTRATION ATTEMPTS AND ENHANCED SPEED OF THREAT DETECTION TO BOOST DEMAND
                    TABLE 116 CASE STUDY: SNORKEL FLOW CREATED HIGH-ACCURACY ML MODELS TO OVERCOME HAND-LABELING CHALLENGES
                    TABLE 117 CYBERSECURITY: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION)
                    TABLE 118 CYBERSECURITY: MARKET, BY REGION, 2024–2029 (USD MILLION)
                    TABLE 119 CYBERSECURITY: MARKET, BY END USER, 2020–2023 (USD MILLION)
                    TABLE 120 CYBERSECURITY: MARKET, BY END USER, 2024–2029 (USD MILLION)
 
9 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER (Page No. - 191)
    9.1 INTRODUCTION 
           FIGURE 45 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER
           FIGURE 46 HOSPITALS & HEALTHCARE PROVIDERS TO HOLD LARGEST MARKET SHARE IN 2029
           TABLE 121 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION)
           TABLE 122 MARKET, BY END USER, 2024–2029 (USD MILLION)
    9.2 HOSPITALS & HEALTHCARE PROVIDERS 
           9.2.1 INCREASING USE IN MINING MEDICAL DATA AND STUDYING GENOMICS-BASED DATA FOR PERSONALIZED MEDICINE TO BOOST MARKET GROWTH
                    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
                    TABLE 124 HOSPITALS & HEALTHCARE PROVIDERS: MARKET, BY APPLICATION, 2020–2023 (USD MILLION)
                    TABLE 125 HOSPITALS & HEALTHCARE PROVIDERS: MARKET, BY APPLICATION, 2024–2029 (USD MILLION)
                    TABLE 126 HOSPITALS & HEALTHCARE PROVIDERS: MARKET, BY REGION, 2020–2023 (USD MILLION)
                    TABLE 127 HOSPITALS & HEALTHCARE PROVIDERS: MARKET, BY REGION, 2024–2029 (USD MILLION)
    9.3 PATIENTS 
           9.3.1 RISE IN USE OF AI IN MENTAL HEALTH SUPPORT APPLICATIONS THROUGH CHATBOTS AND VIRTUAL THERAPISTS TO BOOST MARKET GROWTH
                    TABLE 128 CASE STUDY: COGNIZANT PARTNERED WITH ONE OF CLIENTS TO ENHANCE CALLER SELF-SERVICE AND IMPROVE MEMBER EXPERIENCE METRICS
                    TABLE 129 PATIENTS: MARKET, BY APPLICATION, 2020–2023 (USD MILLION)
                    TABLE 130 PATIENTS: MARKET, BY APPLICATION, 2024–2029 (USD MILLION)
                    TABLE 131 PATIENTS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION)
                    TABLE 132 PATIENTS: MARKET, BY REGION, 2024–2029 (USD MILLION)
    9.4 PHARMACEUTICALS & BIOTECHNOLOGY COMPANIES 
           9.4.1 GROWING PARTNERSHIPS AMONG PLAYERS TO OFFER LUCRATIVE GROWTH OPPORTUNITIES TO PLAYERS
                    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
                    TABLE 134 PHARMACEUTICALS & BIOTECHNOLOGY COMPANIES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2020–2023 (USD MILLION)
                    TABLE 135 PHARMACEUTICALS & BIOTECHNOLOGY COMPANIES: MARKET, BY APPLICATION, 2024–2029 (USD MILLION)
                    TABLE 136 PHARMACEUTICALS & BIOTECHNOLOGY COMPANIES: MARKET, BY REGION, 2020–2023 (USD MILLION)
                    TABLE 137 PHARMACEUTICALS & BIOTECHNOLOGY COMPANIES: MARKET, BY REGION, 2024–2029 (USD MILLION)
    9.5 HEALTHCARE PAYERS 
           9.5.1 FAST AND ACCURATE CLAIM PROCESSING AND ENHANCED FRAUD DETECTION BENEFITS TO BOOST DEMAND
                    TABLE 138 HEALTHCARE PAYERS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2020–2023 (USD MILLION)
                    TABLE 139 HEALTHCARE PAYERS: MARKET, BY APPLICATION, 2024–2029 (USD MILLION)
                    TABLE 140 HEALTHCARE PAYERS: MARKET, BY REGION, 2020–2023 (USD MILLION)
                    TABLE 141 HEALTHCARE PAYERS: MARKET, BY REGION, 2024–2029 (USD MILLION)
    9.6 OTHERS 
           TABLE 142 OTHERS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2020–2023 (USD MILLION)
           TABLE 143 OTHERS: MARKET, BY APPLICATION, 2024–2029 (USD MILLION)
           TABLE 144 OTHERS: MARKET, BY REGION, 2020–2023 (USD MILLION)
           TABLE 145 OTHERS: MARKET, BY REGION, 2024–2029 (USD MILLION)
 
10 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION (Page No. - 209)
     10.1 INTRODUCTION 
             FIGURE 47 ASIA PACIFIC TO REGISTER HIGHEST CAGR DURING FORECAST PERIOD
             TABLE 146 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION)
             TABLE 147 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION)
     10.2 NORTH AMERICA 
             10.2.1 NORTH AMERICA: RECESSION IMPACT
                        FIGURE 48 NORTH AMERICA: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET SNAPSHOT
                        FIGURE 49 US TO DOMINATE NORTH AMERICAN ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET IN 2029
                        TABLE 148 NORTH AMERICA: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY COUNTRY, 2020–2023 (USD MILLION)
                        TABLE 149 NORTH AMERICA: MARKET, BY COUNTRY, 2024–2029 (USD MILLION)
                        TABLE 150 NORTH AMERICA: MARKET, BY OFFERING, 2020–2023 (USD MILLION)
                        TABLE 151 NORTH AMERICA: MARKET, BY OFFERING, 2024–2029 (USD MILLION)
                        TABLE 152 NORTH AMERICA: MARKET, BY APPLICATION, 2020–2023 (USD MILLION)
                        TABLE 153 NORTH AMERICA: MARKET, BY APPLICATION, 2024–2029 (USD MILLION)
                        TABLE 154 NORTH AMERICA: MARKET, BY END USER, 2020–2023 (USD MILLION)
                        TABLE 155 NORTH AMERICA: MARKET, BY END USER, 2024–2029 (USD MILLION)
             10.2.2 US
                        10.2.2.1 High healthcare spending in US to drive market
                                     TABLE 156 US: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION)
                                     TABLE 157 US: MARKET, BY END USER, 2024–2029 (USD MILLION)
             10.2.3 CANADA
                        10.2.3.1 Government-led initiatives to support deployment of AI in healthcare sector to boost demand
                                     TABLE 158 CANADA: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION)
                                     TABLE 159 CANADA: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION)
             10.2.4 MEXICO
                        10.2.4.1 Increasing private sector investments in AI healthcare technologies to drive market
                                     TABLE 160 MEXICO: AI IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION)
                                     TABLE 161 MEXICO: MARKET, BY END USER, 2024–2029 (USD MILLION)
     10.3 EUROPE 
             10.3.1 EUROPE: RECESSION IMPACT
                        FIGURE 50 EUROPE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET SNAPSHOT
                        FIGURE 51 REST OF EUROPE TO EXHIBIT HIGHEST CAGR IN EUROPEAN ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET DURING FORECAST PERIOD
                        TABLE 162 EUROPE: AI IN HEALTHCARE MARKET, BY COUNTRY, 2020–2023 (USD MILLION)
                        TABLE 163 EUROPE: MARKET, BY COUNTRY, 2024–2029 (USD MILLION)
                        TABLE 164 EUROPE: MARKET, BY OFFERING, 2020–2023 (USD MILLION)
                        TABLE 165 EUROPE: MARKET, BY OFFERING, 2024–2029 (USD MILLION)
                        TABLE 166 EUROPE: MARKET, BY APPLICATION, 2020–2023 (USD MILLION)
                        TABLE 167 EUROPE: MARKET, BY APPLICATION, 2024–2029 (USD MILLION)
                        TABLE 168 EUROPE: AI IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION)
                        TABLE 169 EUROPE: MARKET, BY END USER, 2024–2029 (USD MILLION)
             10.3.2 GERMANY
                        10.3.2.1 Rising healthcare data generation to drive market
                                     TABLE 170 GERMANY: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION)
                                     TABLE 171 GERMANY: AI IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION)
             10.3.3 UK
                        10.3.3.1 Targeted treatment with increased success rates to fuel market growth
                                     TABLE 172 UK: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION)
                                     TABLE 173 UK: MARKET, BY END USER, 2024–2029 (USD MILLION)
             10.3.4 FRANCE
                        10.3.4.1 Focus on telemedicine and chronic disease management to drive market
                                     TABLE 174 FRANCE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION)
                                     TABLE 175 FRANCE: MARKET, BY END USER, 2024–2029 (USD MILLION)
             10.3.5 ITALY
                        10.3.5.1 Rising geriatric population to drive market
                                     TABLE 176 ITALY: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION)
                                     TABLE 177 ITALY: MARKET, BY END USER, 2024–2029 (USD MILLION)
             10.3.6 SPAIN
                        10.3.6.1 Growing partnerships between technology firms and healthcare providers to boost demand
                                     TABLE 178 SPAIN: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION)
                                     TABLE 179 SPAIN: MARKET, BY END USER, 2024–2029 (USD MILLION)
             10.3.7 REST OF EUROPE
                        TABLE 180 REST OF EUROPE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION)
                        TABLE 181 REST OF EUROPE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION)
     10.4 ASIA PACIFIC 
             10.4.1 ASIA PACIFIC: RECESSION IMPACT
                        FIGURE 52 ASIA PACIFIC: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET SNAPSHOT
                        FIGURE 53 CHINA TO EXHIBIT HIGHEST CAGR IN ASIA PACIFIC ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET DURING FORECAST PERIOD
                        TABLE 182 ASIA PACIFIC: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY COUNTRY, 2020–2023 (USD MILLION)
                        TABLE 183 ASIA PACIFIC: MARKET, BY COUNTRY, 2024–2029 (USD MILLION)
                        TABLE 184 ASIA PACIFIC: MARKET, BY OFFERING, 2020–2023 (USD MILLION)
                        TABLE 185 ASIA PACIFIC: MARKET, BY OFFERING, 2024–2029 (USD MILLION)
                        TABLE 186 ASIA PACIFIC: MARKET, BY APPLICATION, 2020–2023 (USD MILLION)
                        TABLE 187 ASIA PACIFIC: MARKET, BY APPLICATION, 2024–2029 (USD MILLION)
                        TABLE 188 ASIA PACIFIC: MARKET, BY END USER, 2020–2023 (USD MILLION)
                        TABLE 189 ASIA PACIFIC: MARKET, BY END USER, 2024–2029 (USD MILLION)
             10.4.2 CHINA
                        10.4.2.1 Government-led measures to expedite integration of AI into healthcare sector to drive market
                                     TABLE 190 CHINA: AI IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION)
                                     TABLE 191 CHINA: MARKET, BY END USER, 2024–2029 (USD MILLION)
             10.4.3 JAPAN
                        10.4.3.1 Increasing number of AI-driven start-ups manufacturing diagnostic and therapeutic tools to fuel market growth
                                     TABLE 192 JAPAN: MARKET, BY END USER, 2020–2023 (USD MILLION)
                                     TABLE 193 JAPAN: MARKET, BY END USER, 2024–2029 (USD MILLION)
             10.4.4 SOUTH KOREA
                        10.4.4.1 Increasing incidence of cancer to drive market
                                     TABLE 194 SOUTH KOREA: MARKET, BY END USER, 2020–2023 (USD MILLION)
                                     TABLE 195 SOUTH KOREA: MARKET, BY END USER, 2024–2029 (USD MILLION)
             10.4.5 INDIA
                        10.4.5.1 Developing IT infrastructure and AI-friendly government initiatives to spur market growth
                                     TABLE 196 INDIA: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION)
                                     TABLE 197 INDIA: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION)
             10.4.6 REST OF ASIA PACIFIC
                        TABLE 198 REST OF ASIA PACIFIC: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION)
                        TABLE 199 REST OF ASIA PACIFIC: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION)
     10.5 ROW 
             10.5.1 ROW: RECESSION IMPACT
                        FIGURE 54 ROW: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET SNAPSHOT
                        FIGURE 55 SOUTH AMERICA TO DOMINATE ROW MARKET IN 2029
                        TABLE 200 ROW: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION)
                        TABLE 201 ROW: MARKET, BY REGION, 2024–2029 (USD MILLION)
                        TABLE 202 ROW: MARKET, BY OFFERING, 2020–2023 (USD MILLION)
                        TABLE 203 ROW: MARKET, BY OFFERING, 2024–2029 (USD MILLION)
                        TABLE 204 ROW: MARKET, BY APPLICATION, 2020–2023 (USD MILLION)
                        TABLE 205 ROW: MARKET, BY APPLICATION, 2024–2029 (USD MILLION)
                        TABLE 206 ROW: MARKET, BY END USER, 2020–2023 (USD MILLION)
                        TABLE 207 ROW: MARKET, BY END USER, 2024–2029 (USD MILLION)
             10.5.2 SOUTH AMERICA
                        10.5.2.1 High investments in healthcare IT to drive market
                                     TABLE 208 SOUTH AMERICA: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION)
                                     TABLE 209 SOUTH AMERICA: MARKET, BY END USER, 2024–2029 (USD MILLION)
             10.5.3 GCC
                        10.5.3.1 Rising focus on technological advancements in healthcare sector to drive market
                                     TABLE 210 GCC: MARKET, BY END USER, 2020–2023 (USD MILLION)
                                     TABLE 211 GCC: MARKET, BY END USER, 2024–2029 (USD MILLION)
             10.5.4 REST OF MIDDLE EAST & AFRICA
                        10.5.4.1 Growing investments in information and communication technologies to boost demand
                                     TABLE 212 REST OF MIDDLE EAST & AFRICA: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION)
                                     TABLE 213 REST OF MEA: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION)
 
11 COMPETITIVE LANDSCAPE (Page No. - 259)
     11.1 OVERVIEW 
     11.2 STRATEGIES ADOPTED BY MAJOR PLAYERS, 2020–2023 
             TABLE 214 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: OVERVIEW OF STRATEGIES DEPLOYED BY KEY PLAYERS, 2020–2023
     11.3 REVENUE ANALYSIS, 2019–2023 
             FIGURE 56 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: REVENUE ANALYSIS OF TOP FIVE PLAYERS, 2019–2023
     11.4 MARKET SHARE ANALYSIS, 2023 
             FIGURE 57 MARKET SHARE ANALYSIS, 2023
             TABLE 215 MARKET SHARE ANALYSIS, 2023
     11.5 COMPANY EVALUATION MATRIX, 2023 
             11.5.1 STARS
             11.5.2 EMERGING LEADERS
             11.5.3 PERVASIVE PLAYERS
             11.5.4 PARTICIPANTS
                        FIGURE 58 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: COMPANY EVALUATION MATRIX, 2023
             11.5.5 COMPANY FOOTPRINT
                        TABLE 216 OVERALL COMPANY FOOTPRINT
                        TABLE 217 COMPANY OFFERING FOOTPRINT
                        TABLE 218 COMPANY END USER FOOTPRINT
                        TABLE 219 COMPANY REGION FOOTPRINT
     11.6 START-UP/SMALL AND MEDIUM-SIZED ENTERPRISE (SME) EVALUATION MATRIX, 2023 
             11.6.1 PROGRESSIVE COMPANIES
             11.6.2 RESPONSIVE COMPANIES
             11.6.3 DYNAMIC COMPANIES
             11.6.4 STARTING BLOCKS
                        FIGURE 59 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: START-UP/SME EVALUATION MATRIX, 2023
                        TABLE 220 AI IN HEALTHCARE MARKET: LIST OF KEY START-UPS/SMES
             11.6.5 COMPETITIVE BENCHMARKING
                        TABLE 221 AI IN HEALTHCARE MARKET: COMPETITIVE BENCHMARKING OF KEY START-UPS/SMES
     11.7 COMPETITIVE SCENARIOS AND TRENDS 
             11.7.1 PRODUCT LAUNCHES
                        TABLE 222 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: PRODUCT LAUNCHES, 2020 - 2023
             11.7.2 DEALS
                        TABLE 223 AI IN HEALTHCARE MARKET: DEALS, 2020 - 2023
 
12 COMPANY PROFILES (Page No. - 275)
     12.1 KEY PLAYERS 
(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.
                        TABLE 224 KONINKLIJKE PHILIPS N.V.: COMPANY OVERVIEW
                        FIGURE 60 KONINKLIJKE PHILIPS N.V.: COMPANY SNAPSHOT
                        TABLE 225 KONINKLIJKE PHILIPS N.V.: PRODUCTS/SOLUTIONS/SERVICES OFFERED
                        TABLE 226 KONINKLIJKE PHILIPS N.V.: PRODUCT LAUNCHES
                        TABLE 227 KONINKLIJKE PHILIPS N.V.: DEALS
                        TABLE 228 KONINKLIJKE PHILIPS N.V.: OTHERS
             12.1.2 MICROSOFT
                        TABLE 229 MICROSOFT: COMPANY OVERVIEW
                        FIGURE 61 MICROSOFT: COMPANY SNAPSHOT
                        TABLE 230 MICROSOFT: PRODUCTS/SOLUTIONS/SERVICES OFFERED
                        TABLE 231 MICROSOFT: PRODUCT LAUNCHES
                        TABLE 232 MICROSOFT: DEALS
                        TABLE 233 MICROSOFT: OTHERS
             12.1.3 SIEMENS HEALTHINEERS AG
                        TABLE 234 SIEMENS HEALTHINEERS AG: COMPANY OVERVIEW
                        FIGURE 62 SIEMENS HEALTHINEERS AG: COMPANY SNAPSHOT
                        TABLE 235 SIEMENS HEALTHINEERS AG: PRODUCTS/SOLUTIONS/SERVICES OFFERED
                        TABLE 236 SIEMENS HEALTHINEERS AG: PRODUCT LAUNCHES
                        TABLE 237 SIEMENS HEALTHINEERS AG: DEALS
                        TABLE 238 SIEMENS HEALTHINEERS AG: OTHERS
             12.1.4 INTEL CORPORATION
                        TABLE 239 INTEL CORPORATION: COMPANY OVERVIEW
                        FIGURE 63 INTEL CORPORATION: COMPANY SNAPSHOT
                        TABLE 240 INTEL CORPORATION: PRODUCTS/SOLUTIONS/SERVICES OFFERED
                        TABLE 241 INTEL CORPORATION: PRODUCT LAUNCHES
                        TABLE 242 INTEL CORPORATION: DEALS
                        TABLE 243 INTEL CORPORATION: OTHERS
             12.1.5 NVIDIA CORPORATION
                        TABLE 244 NVIDIA CORPORATION: COMPANY OVERVIEW
                        FIGURE 64 NVIDIA CORPORATION: COMPANY SNAPSHOT
                        TABLE 245 NVIDIA CORPORATION: PRODUCTS/SOLUTIONS/SERVICES OFFERED
                        TABLE 246 NVIDIA CORPORATION: PRODUCT LAUNCHES
                        TABLE 247 NVIDIA CORPORATION: DEALS
                        TABLE 248 NVIDIA CORPORATION: OTHERS
             12.1.6 GOOGLE INC.
                        TABLE 249 GOOGLE INC.: COMPANY OVERVIEW
                        FIGURE 65 GOOGLE INC.: COMPANY SNAPSHOT
                        TABLE 250 GOOGLE INC.: PRODUCTS/SOLUTIONS/SERVICES OFFERED
                        TABLE 251 GOOGLE INC.: PRODUCT LAUNCHES
                        TABLE 252 GOOGLE INC.: DEALS
                        TABLE 253 GOOGLE INC.: OTHERS
             12.1.7 GE HEALTHCARE
                        TABLE 254 GE HEALTHCARE: COMPANY OVERVIEW
                        FIGURE 66 GE HEALTHCARE: COMPANY SNAPSHOT
                        TABLE 255 GE HEALTHCARE: PRODUCTS/SOLUTIONS/SERVICES OFFERED
                        TABLE 256 GE HEALTHCARE: PRODUCT LAUNCHES
                        TABLE 257 GE HEALTHCARE: DEALS
             12.1.8 MEDTRONIC
                        TABLE 258 MEDTRONIC: COMPANY OVERVIEW
                        FIGURE 67 MEDTRONIC: COMPANY SNAPSHOT
                        TABLE 259 MEDTRONIC: PRODUCTS/SOLUTIONS/SERVICES OFFERED
                        TABLE 260 MEDTRONIC: DEALS
             12.1.9 MICRON TECHNOLOGY, INC.
                        TABLE 261 MICRON TECHNOLOGY, INC.: COMPANY OVERVIEW
                        FIGURE 68 MICRON TECHNOLOGY, INC.: COMPANY SNAPSHOT
                        TABLE 262 MICRON TECHNOLOGY, INC.: PRODUCTS/SOLUTIONS/SERVICES OFFERED
                        TABLE 263 MICRON TECHNOLOGY, INC: .PRODUCT LAUNCHES
                        TABLE 264 MICRON TECHNOLOGY, INC.: DEALS
             12.1.10 AMAZON.COM, INC.
                        TABLE 265 AMAZON.COM, INC.: COMPANY OVERVIEW
                        FIGURE 69 AMAZON.COM, INC.: COMPANY SNAPSHOT
                        TABLE 266 AMAZON.COM, INC.: PRODUCTS/SOLUTIONS/SERVICES OFFERED
                        TABLE 267 AMAZON.COM, INC.: PRODUCT LAUNCHES
                        TABLE 268 AMAZON.COM, INC.: DEALS
             12.1.11 ORACLE
                        TABLE 269 ORACLE: COMPANY OVERVIEW
                        FIGURE 70 ORACLE: COMPANY SNAPSHOT
                        TABLE 270 ORACLE: PRODUCTS/SOLUTIONS/SERVICES OFFERED
                        TABLE 271 ORACLE: PRODUCT LAUNCHES
                        TABLE 272 ORACLE: DEALS
             12.1.12 JOHNSON & JOHNSON SERVICES, INC.
                        TABLE 273 JOHNSON & JOHNSON SERVICES, INC.: COMPANY OVERVIEW
                        FIGURE 71 JOHNSON & JOHNSON SERVICES, INC.: COMPANY SNAPSHOT
                        TABLE 274 JOHNSON & JOHNSON SERVICES, INC.: PRODUCTS/SOLUTIONS/SERVICES OFFERED
                        TABLE 275 JOHNSON & JOHNSON SERVICES, INC.: DEALS
     12.2 OTHER PLAYERS 
             12.2.1 MERATIVE
             12.2.2 GENERAL VISION INC.
             12.2.3 CLOUDMEDX
             12.2.4 ONCORA MEDICAL
             12.2.5 ENLITIC, INC.
             12.2.6 LUNIT INC.
             12.2.7 QURE.AI
             12.2.8 TEMPUS
             12.2.9 COTA
             12.2.10 FDNA INC.
             12.2.11 RECURSION
             12.2.12 ATOMWISE INC.
             12.2.13 VIRGIN PULSE
             12.2.14 BABYLON HEALTHCARE SERVICES LTD
             12.2.15 MDLIVE (EVERNORTH GROUP)
             12.2.16 STRYKER
             12.2.17 QVENTUS
             12.2.18 SWEETCH
             12.2.19 SIRONA MEDICAL, INC.
             12.2.20 GINGER
             12.2.21 BIOBEAT
*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 (Page No. - 364)
     13.1 DISCUSSION GUIDE 
     13.2 KNOWLEDGESTORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL 
     13.3 CUSTOMIZATION OPTIONS 
     13.4 RELATED REPORTS 
     13.5 AUTHOR DETAILS 
 

 

The research study involved the extensive use of secondary sources, directories, and databases (annual reports or presentations of companies, industry association publications, directories, technical handbooks, World Economic Outlook (WEO), trade websites, Hoovers, Bloomberg Businessweek, Factiva, and OneSource) to identify and collect information useful for this technical, market-oriented, and commercial study of the AI in Healthcare market. Primary sources mainly comprise several experts from the core and related industries, along with preferred suppliers, manufacturers, distributors, service providers, system providers, technology developers, alliances, and standards and certification organizations related to various phases of this industry’s value chain.

Secondary Research

Various secondary sources have been referred to in the secondary research process for identifying and collecting information important for this study. The secondary sources include annual reports, press releases, and investor presentations of companies; white papers; journals and certified publications; and articles from recognized authors, websites, directories, and databases. Secondary research has been conducted to obtain key information about the industry’s supply chain, market’s value chain, the total pool of key players, market segmentation according to the industry trends (to the bottom-most level), geographic markets, and key developments from both market- and technology-oriented perspectives. The secondary data has been collected and analyzed to determine the overall market size, further validated by primary research.

Primary Research

In the primary research process, various primary sources from the supply and demand sides have been interviewed to obtain qualitative and quantitative information for this report. The primary sources from the supply side include industry experts, such as CEOs, vice presidents, marketing directors, technology & innovation directors, and related key executives from key companies and organizations operating in the AI in Healthcare market across four major regions: North America, Europe, Asia Pacific, and RoW (South America, GCC, and Rest of MEA). Primary data has been collected through questionnaires, e-mails, and telephonic interviews. Approximately 40% and 60% of primary interviews have been conducted from the demand and supply sides, respectively.

Artificial Intelligence (AI) in Healthcare Market
 Size, and Share

To know about the assumptions considered for the study, download the pdf brochure

Market Size Estimation

In the complete market engineering process, both top-down and bottom-up approaches have been used along with several data triangulation methods to perform market estimation and forecasting for the overall market segments and subsegments listed in this report. Key players in the market have been identified through secondary research, and their market shares in the respective regions have been determined through primary and secondary research. This entire procedure includes the study of annual and financial reports of the top market players and extensive interviews for key insights (quantitative and qualitative) with industry experts (CEOs, VPs, directors, and marketing executives).

In this approach, important players, such Koninklijke Philips N.V. (Netherlands), Microsoft (US), Siemens Healthineers (Germany), Intel Corporation (US), and NVIDIA Corporation (US) have been identified. After confirming these companies through primary interviews with industry experts, their total revenue has been estimated by referring to annual reports, SEC filings, and paid databases. Revenues of these companies pertaining to the business units (Bus) that offer AI in Healthcare have been identified through similar sources. Industry experts have reconfirmed these revenues through primary interviews.

AI in Healthcare Market: Bottom-Up Approach

Artificial Intelligence (AI) in Healthcare Market
 Size, and Bottom-Up Approach

The bottom-up approach has been employed to arrive at the overall size of the AI in Healthcare market from the revenues of key players and their share in the market.

AI in Healthcare Market: Top-Down Approach

Artificial Intelligence (AI) in Healthcare Market
 Size, and Top-Down Approach

In the top-down approach, the overall market size has been used to estimate the size of the individual markets (mentioned in the market segmentation) through percentage splits from secondary and primary research. The most appropriate immediate parent market size has been used to implement the top-down approach to calculate the market size of specific segments. The top-down approach has been implemented for the data extracted from the secondary research to validate the market size obtained.
Each company’s market share has been estimated to verify the revenue shares used earlier in the supply-side approach. The overall parent market size and individual market sizes were determined and confirmed in this study by the data triangulation method and the validation of data through primaries. The data triangulation method used in this study is explained in the next section.

Data Triangulation

After arriving at the overall market size from the market size estimation process explained earlier, the total market was split into several segments and subsegments. Data triangulation and market breakdown procedures have been employed to complete the overall market engineering process and arrive at the exact statistics for all segments and subsegments, wherever applicable. The data has been triangulated by studying various factors and trends from both the demand and supply sides. Along with this, the AI in healthcare market has been validated using both top-down and bottom-up approaches.

Market Definition

AI in Healthcare harnesses artificial intelligence's power to transform healthcare delivery and patient outcomes. It utilizes sophisticated machine learning, NLP, context-aware computing, and computer vision technologies to analyze massive amounts of medical data, enabling early disease detection, personalized treatment plans, enhanced clinical decision-making, and streamlined administrative processes.

The ecosystem of the AI in healthcare market comprises hardware providers, software providers, cloud service providers, AI solution providers, and end users of AI in healthcare. This market is competitive and diversified, with over 30 companies competing across its value chain to sustain their position and increase their share in the market. The market is expected to grow significantly in the coming years due to the increasing use of large and complex datasets in hospitals, biotechnology, and pharmaceutical companies.

Key Stakeholders

  • Semiconductor companies
  • Technology providers
  • Universities and research organizations
  • Hospitals and healthcare payers
  • System integrators
  • AI solution providers
  • AI platform providers
  • Cloud service providers
  • AI system providers
  • Medical research and biotechnology companies
  • Investors and venture capitalists
  • Manufacturers and individuals implementing AI technology in healthcare devices and systems

Report Objectives

  • To describe and forecast the artificial intelligence (AI) in healthcare market, in terms of value, by offering, technology, application, and end-user
  • To describe and forecast the AI in healthcare market, in terms of value, for four main regions—
    North America, Europe, Asia Pacific, and the Rest of the World (RoW)
  • To forecast the size and market segments of the AI in Healthcare market by volume based on Processor hardware.
  • To provide detailed information regarding the major factors influencing the growth of the market (drivers, restraints, opportunities, and challenges)
  • To provide an ecosystem analysis, case study analysis, patent analysis, technology analysis, ASP analysis, Porter’s Five Forces analysis, and regulations pertaining to the market.
  • To provide a comprehensive overview of the value chain of the AI in healthcare market ecosystem
  • To strategically analyze micromarkets1 with respect to individual growth trends, prospects, and contributions to the total market
  • To strategically profile the key players and comprehensively analyze their market shares and core competencies.
  • To analyze the opportunities in the market for stakeholders and describe the competitive landscape of the market.
  • To analyze competitive developments such as collaborations, agreements, partnerships, product developments, and research and development (R&D) in the market.
  • To analyze the impact of the recession on the AI in Healthcare market.

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Company Information

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Report Code
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Published ON
Jan, 2024
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