Artificial Intelligence (AI) in Healthcare Market Size, Share & Trends

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 : September 25, 2024

Artificial Intelligence (AI) in Healthcare Market Size

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  Trends & 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 Size. 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

AI in Healthcare Market Share

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.

AI in Healthcare Market Regional Analysis

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 growth.

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 size Offering, Technology, Application, and End User, and Region.

Segment

Subsegment

AI in Healthcare market size, 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

AI in Healthcare market sizeBy 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

AI in Healthcare market shareBy 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

AI in Healthcare market share, By End User:

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

AI in Healthcare market share, 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 
2 RESEARCH METHODOLOGY 
3 EXECUTIVE SUMMARY 
  
4 PREMIUM INSIGHTS (Page No. - 54)
    4.1 ATTRACTIVE GROWTH OPPORTUNITIES FOR AI IN HEALTHCARE MARKET 
    4.2 AI IN HEALTHCARE MARKET, BY OFFERING 
    4.3 MARKET, BY TECHNOLOGY 
    4.4 MARKET, BY APPLICATION 
    4.5 MARKET, BY END USER 
    4.6 AI IN HEALTHCARE MARKET, BY COUNTRY 
 
5 MARKET OVERVIEW (Page No. - 57)
    5.1 INTRODUCTION 
    5.2 MARKET DYNAMICS 
           5.2.1 DRIVERS
                    5.2.1.1 Generation of large and complex healthcare datasets
                    5.2.1.2 Pressing need to reduce healthcare costs
                    5.2.1.3 Improving computing power and declining hardware cost
                    5.2.1.4 Rising number of partnerships and collaborations among different domains in healthcare sector
                    5.2.1.5 Growing need for improvised healthcare services due to imbalance between healthcare workforce and patients
           5.2.2 RESTRAINTS
                    5.2.2.1 Reluctance among medical practitioners to adopt AI-based technologies
                    5.2.2.2 Lack of skilled AI workforce and ambiguous regulatory guidelines for medical software
           5.2.3 OPPORTUNITIES
                    5.2.3.1 Growing potential of AI-based tools for elderly care
                    5.2.3.2 Increasing focus on developing human-aware AI systems
                    5.2.3.3 Rising potential of AI technology in genomics, drug discovery, and imaging & diagnostics
           5.2.4 CHALLENGES
                    5.2.4.1 Lack of curated healthcare data
                    5.2.4.2 Concerns regarding data privacy
                    5.2.4.3 Lack of interoperability between AI solutions offered by different vendors
    5.3 VALUE CHAIN ANALYSIS 
    5.4 PORTER’S FIVE FORCES ANALYSIS 
    5.5 ECOSYSTEM ANALYSIS 
    5.6 REVENUE SHIFTS AND NEW REVENUE POCKETS FOR AI IN HEALTHCARE MARKET 
    5.7 CASE STUDY ANALYSIS 
           5.7.1 USE CASE – BIOBEAT (ISRAEL)
           5.7.2 USE CASE – CLEVELAND CLINIC AND MICROSOFT
           5.7.3 USE CASE – TRANSLATIONAL GENOMICS RESEARCH INSTITUTE (TGEN)
    5.8 TECHNOLOGY ANALYSIS 
           5.8.1 CLOUD COMPUTING
           5.8.2 CLOUD GPU
    5.9 PRICING ANALYSIS 
           5.9.1 AVERAGE SELLING PRICE (ASP) ANALYSIS OF COMPONENTS OFFERED BY KEY PLAYERS
           5.9.2 ASP TRENDS
    5.10 TRADE ANALYSIS 
    5.11 PATENT ANALYSIS 
           5.11.1 MAJOR PATENTS
    5.12 REGULATORY LANDSCAPE 
           5.12.1 REGULATIONS
                    5.12.1.1 Export–import regulations
                    5.12.1.2 Restriction of Hazardous Substances (ROHS) and Waste Electrical and Electronic Equipment (WEEE)
                    5.12.1.3 Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH)
                    5.12.1.4 General Data Protection Regulation (GDPR)
 
6 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY OFFERING (Page No. - 89)
    6.1 INTRODUCTION 
    6.2 HARDWARE 
           6.2.1 PROCESSOR
                    6.2.1.1 Intel Corporation (US), NVIDIA Corporation (US), AND Xilinx (US) key providers of hardware components for AI applications
                    6.2.1.2 MPU/CPU
                    6.2.1.3 GPU
                    6.2.1.4 FPGA
                    6.2.1.5 GPU
                    6.2.1.6 ASIC
                    6.2.1.7 GPU
           6.2.2 MEMORY
                    6.2.2.1 Development of high-bandwidth memory for AI applications to drive market
           6.2.3 NETWORK
                    6.2.3.1 NVIDIA Corporation (US) and Intel Corporation (US) among key providers of network interconnect adapters for AI applications
    6.3 SOFTWARE 
           6.3.1 AI SOLUTION
                    6.3.1.1 Integration of non-procedural languages in AI solutions to propel segmental growth
                    6.3.1.2 On-premises
                    6.3.1.3 Cloud
           6.3.2 AI PLATFORM
                    6.3.2.1 Increasing use of AI platforms for decision-making and data management to boost segmental growth
                    6.3.2.2 Machine learning framework
                    6.3.2.3 Application program interface
    6.4 SERVICES 
           6.4.1 DEPLOYMENT & INTEGRATION
                    6.4.1.1 Key services for configuring AI systems in healthcare
           6.4.2 SUPPORT & MAINTENANCE
                    6.4.2.1 Help to keep up performance of systems post installation
 
7 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TECHNOLOGY (Page No. - 109)
    7.1 INTRODUCTION 
    7.2 MACHINE LEARNING 
           7.2.1 DEEP LEARNING
                    7.2.1.1 Enables machines to build hierarchical representations
           7.2.2 SUPERVISED LEARNING
                    7.2.2.1 Classification and regression major segments of supervised learning
           7.2.3 REINFORCEMENT LEARNING
                    7.2.3.1 Allows systems and software to determine ideal behavior for maximizing performance of systems
           7.2.4 UNSUPERVISED LEARNING
                    7.2.4.1 Includes clustering methods consisting of algorithms with unlabeled training data
           7.2.5 OTHERS
    7.3 NATURAL LANGUAGE PROCESSING 
           7.3.1 WIDELY USED BY CLINICAL AND RESEARCH COMMUNITIES IN HEALTHCARE
    7.4 CONTEXT-AWARE COMPUTING 
           7.4.1 DEVELOPMENT OF MORE SOPHISTICATED HARD AND SOFT SENSORS TO ACCELERATE GROWTH OF CONTEXT-AWARE COMPUTING
    7.5 COMPUTER VISION 
           7.5.1 USED FOR SIGNIFICANT APPLICATIONS IN SURGERY AND THERAPY
 
8 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION (Page No. - 120)
    8.1 INTRODUCTION 
    8.2 PATIENT DATA & RISK ANALYSIS 
           8.2.1 PROVIDE PREDICTIVE INSIGHTS INTO PATIENT HEALTH USING MACHINE LEARNING AND NATURAL LANGUAGE PROCESSING ALGORITHMS
    8.3 IN-PATIENT CARE & HOSPITAL MANAGEMENT 
           8.3.1 HELP TO CUT DOWN EXCESSIVE OPERATIONAL COSTS AND LOWER COST OF PATIENT CARE
    8.4 MEDICAL IMAGING & DIAGNOSTICS 
           8.4.1 HELP TO GENERATE HIGHLY ACCURATE IMAGING DATA
    8.5 LIFESTYLE MANAGEMENT & REMOTE PATIENT MONITORING 
           8.5.1 HELP REDUCE BURDEN ON HOSPITALS
    8.6 VIRTUAL ASSISTANTS 
           8.6.1 ASSIST IN DISSEMINATING PRECISE MEDICAL INFORMATION AMONG VULNERABLE POPULATIONS
    8.7 DRUG DISCOVERY 
           8.7.1 AI EXPECTED TO REDUCE TIME AND COST INVOLVED IN DRUG DISCOVERY
    8.8 RESEARCH 
           8.8.1 AI ALGORITHMS USED BY BIOINFORMATICS RESEARCHERS FOR DATABASE CLASSIFICATION AND MINING
    8.9 HEALTHCARE ASSISTANCE ROBOTS 
           8.9.1 HELP TO SIGNIFICANTLY REDUCE NEED FOR ROUND-THE-CLOCK MANUAL NURSING CARE
    8.1 PRECISION MEDICINE 
           8.10.1 AI EXPECTED TO FULFIL DEMAND FOR PERSONALIZED TREATMENT PLANS FOR PATIENTS ADMINISTERED WITH PRECISION MEDICINE
    8.11 EMERGENCY ROOM & SURGERY 
           8.11.1 LIMITED AVAILABILITY OF SKILLED WORKFORCE IN EMERGENCY ROOMS TO DRIVE ADOPTION OF AI
    8.12 WEARABLES 
           8.12.1 FACILITATE IMPROVED, REAL-TIME PATIENT MONITORING
    8.13 MENTAL HEALTH 
           8.13.1 AI USED IN DIAGNOSIS OF MENTAL DISTRESS AND NEUROLOGICAL ABNORMALITIES
    8.14 CYBERSECURITY 
           8.14.1 AI IN HEALTHCARE CYBERSECURITY TO BECOME CRITICAL IN PROTECTION OF ONSITE SYSTEMS
 
9 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER (Page No. - 151)
    9.1 INTRODUCTION 
    9.2 HOSPITALS AND HEALTHCARE PROVIDERS 
           9.2.1 UTILIZE AI TO PREDICT AND PREVENT READMISSIONS AND IMPROVE OPERATIONS
    9.3 PATIENTS 
           9.3.1 INCREASING POPULARITY OF SMARTPHONE APPLICATIONS AND WEARABLES TO DRIVE ADOPTION OF AI AMONG PATIENTS
    9.4 PHARMACEUTICALS & BIOTECHNOLOGY COMPANIES 
           9.4.1 USE AI FOR DRUG DISCOVERY, PRECISION MEDICINE, AND RESEARCH APPLICATIONS
    9.5 HEALTHCARE PAYERS 
           9.5.1 USE AI TOOLS TO MANAGE RISKS, IDENTIFY CLAIM TRENDS, AND MAXIMIZE PAYMENT ACCURACY
    9.6 OTHERS 
 
10 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION (Page No. - 167)
     10.1 INTRODUCTION 
     10.2 NORTH AMERICA 
             10.2.1 US
                        10.2.1.1 High healthcare spending combined with increasing demand for AI in medical sector to complement market growth
             10.2.2 CANADA
                        10.2.2.1 Continuous research on NLP and ML across research institutions and universities in Canada to propel market
             10.2.3 MEXICO
                        10.2.3.1 Mexico to account for smallest share of AI in healthcare market in North America during forecast period
     10.3 EUROPE 
             10.3.1 GERMANY
                        10.3.1.1 Government initiatives to expedite AI development to support market growth
             10.3.2 UK
                        10.3.2.1 Adoption of AI in drug discovery space to fuel market growth
             10.3.3 FRANCE
                        10.3.3.1 Government endeavors to develop healthcare IT in France likely to support market
             10.3.4 ITALY
                        10.3.4.1 Development of electronic health records and aging population to drive market
             10.3.5 SPAIN
                        10.3.5.1 Growing awareness of AI in Spain to favor market growth
             10.3.6 REST OF EUROPE
     10.4 ASIA PACIFIC 
             10.4.1 CHINA
                        10.4.1.1 Concrete government measures to accelerate AI development to augment market growth
             10.4.2 JAPAN
                        10.4.2.1 AI adoption to expedite drug discovery to motivate market growth
             10.4.3 SOUTH KOREA
                        10.4.3.1 Government’s AI National Strategy to push market growth in South Korea
             10.4.4 INDIA
                        10.4.4.1 Developing IT infrastructure and AI-friendly government initiatives to spur market growth
             10.4.5 REST OF ASIA PACIFIC
     10.5 REST OF THE WORLD 
             10.5.1 SOUTH AMERICA
                        10.5.1.1 High investments in healthcare IT to encourage market growth
             10.5.2 MIDDLE EAST AND AFRICA
                        10.5.2.1 Growing healthcare expenditure in Middle East and North Africa to foster growth of AI in healthcare market
 
11 COMPETITIVE LANDSCAPE (Page No. - 202)
     11.1 INTRODUCTION 
     11.2 MARKET EVALUATION FRAMEWORK 
             11.2.1 PRODUCT PORTFOLIO
             11.2.2 REGIONAL FOCUS
             11.2.3 MANUFACTURING FOOTPRINT
             11.2.4 ORGANIC/INORGANIC GROWTH STRATEGIES
     11.3 REVENUE ANALYSIS OF TOP PLAYERS IN AI IN HEALTHCARE MARKET 
     11.4 MARKET SHARE ANALYSIS, 2022 
     11.5 COMPANY EVALUATION QUADRANT 
             11.5.1 STARS
             11.5.2 PERVASIVE PLAYERS
             11.5.3 EMERGING LEADERS
             11.5.4 PARTICIPANTS
     11.6 STARTUP/SME EVALUATION QUADRANT 
             11.6.1 PROGRESSIVE COMPANIES
             11.6.2 RESPONSIVE COMPANIES
             11.6.3 DYNAMIC COMPANIES
             11.6.4 STARTING BLOCKS
     11.7 AI IN HEALTHCARE MARKET: COMPANY FOOTPRINT 
     11.8 COMPETITIVE BENCHMARKING 
     11.9 COMPETITIVE SCENARIOS AND TRENDS 
             11.9.1 PRODUCT LAUNCHES & DEVELOPMENTS
             11.9.2 DEALS
 
12 COMPANY PROFILES (Page No. - 219)
     12.1 KEY PLAYERS 
             12.1.1 INTEL CORPORATION
                        12.1.1.1 Business overview
                        12.1.1.2 Products/Solutions/Services offered
                        12.1.1.3 Recent developments
                                     12.1.1.3.1 Product launches
                                     12.1.1.3.2 Deals
                        12.1.1.4 MnM view
                                     12.1.1.4.1 Key strengths/Right to win
                                     12.1.1.4.2 Strategic choices made
                                     12.1.1.4.3 Weaknesses and competitive threats
             12.1.2 KONINKLIJKE PHILIPS N.V.
                        12.1.2.1 Business overview
                        12.1.2.2 Products/Solutions/Services offered
                        12.1.2.3 Recent developments
                                     12.1.2.3.1 Product launches
                                     12.1.2.3.2 Deals
                                     12.1.2.3.3 Others
                        12.1.2.4 MnM view
                                     12.1.2.4.1 Key strengths/Right to win
                                     12.1.2.4.2 Strategic choices made
                                     12.1.2.4.3 Weaknesses and competitive threats
             12.1.3 MICROSOFT
                        12.1.3.1 Business overview
                        12.1.3.2 Products/Solutions/Services offered
                        12.1.3.3 Recent developments
                                     12.1.3.3.1 Product launches
                                     12.1.3.3.2 Deals
                        12.1.3.4 MnM view
                                     12.1.3.4.1 Key strengths/Right to win
                                     12.1.3.4.2 Strategic choices made
                                     12.1.3.4.3 Weaknesses and competitive threats
             12.1.4 SIEMENS HEALTHINEERS
                        12.1.4.1 Business overview
                        12.1.4.2 Products/Solutions/Services offered
                        12.1.4.3 Recent developments
                                     12.1.4.3.1 Product launches
                                     12.1.4.3.2 Deals
                        12.1.4.4 MnM view
                                     12.1.4.4.1 Key strengths/Right to win
                                     12.1.4.4.2 Strategic choices made
                                     12.1.4.4.3 Weaknesses and competitive threats
             12.1.5 NVIDIA CORPORATION
                        12.1.5.1 Business overview
                        12.1.5.2 Products/Solutions/Services offered
                        12.1.5.3 Recent developments
                                     12.1.5.3.1 Product launches
                                     12.1.5.3.2 Deals
                                     12.1.5.3.3 Others
                        12.1.5.4 MnM view
                                     12.1.5.4.1 Key strengths/Right to win
                                     12.1.5.4.2 Strategic choices made
                                     12.1.5.4.3 Weaknesses and competitive threats
             12.1.6 GOOGLE INC.
                        12.1.6.1 Business overview
                        12.1.6.2 Products/Solutions/Services offered
                        12.1.6.3 Recent developments
                                     12.1.6.3.1 Product launches
                                     12.1.6.3.2 Deals
             12.1.7 GENERAL ELECTRIC COMPANY
                        12.1.7.1 Business overview
                        12.1.7.2 Products/Solutions/Services offered
                        12.1.7.3 Recent developments
                                     12.1.7.3.1 Product launches
                                     12.1.7.3.2 Deals
             12.1.8 MEDTRONIC
                        12.1.8.1 Business overview
                        12.1.8.2 Products/Solutions/Services offered
                        12.1.8.3 Recent developments
                                     12.1.8.3.1 Deals
             12.1.9 MICRON TECHNOLOGY, INC.
                        12.1.9.1 Business overview
                        12.1.9.2 Products/Solutions/Services offered
                        12.1.9.3 Recent developments
                                     12.1.9.3.1 Product launches
                                     12.1.9.3.2 Deals
             12.1.10 AMAZON WEB SERVICES (AWS)
                        12.1.10.1 Business overview
                        12.1.10.2 Products/Solutions/Services offered
                        12.1.10.3 Recent developments
                                     12.1.10.3.1 Product launches
                                     12.1.10.3.2 Deals
             12.1.11 JOHNSON & JOHNSON SERVICES, INC.
                        12.1.11.1 Business overview
                        12.1.11.2 Products/Solutions/Services offered
     12.2 STARTUP ECOSYSTEM 
             12.2.1 MERATIVE
             12.2.2 GENERAL VISION
             12.2.3 CLOUDMEDX
             12.2.4 ONCORA MEDICAL
             12.2.5 ENLITIC
             12.2.6 LUNIT INC.
             12.2.7 QURE.AI
             12.2.8 ARTERYS INC.
             12.2.9 COTA
             12.2.10 FDNA INC.
             12.2.11 RECURSION
             12.2.12 ATOMWISE
             12.2.13 VIRGIN PULSE
             12.2.14 BABYLON HEALTH
             12.2.15 MDLIVE (EVERNORTH GROUP)
             12.2.16 STRYKER
             12.2.17 QVENTUS
             12.2.18 DESKTOP GENETICS
             12.2.19 SIRONA MEDICAL, INC.
             12.2.20 GINGER
             12.2.21 BIOBEAT
 
13 APPENDIX (Page No. - 283)
     13.1 INSIGHTS FROM INDUSTRY EXPERTS 
     13.2 DISCUSSION GUIDE 
     13.3 KNOWLEDGESTORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL 
     13.4 CUSTOMIZATION OPTIONS 
     13.5 RELATED REPORTS 
     13.6 AUTHOR DETAILS 
 
LIST OF TABLES (206 TABLES)
 
TABLE 1 LIMITATIONS AND ASSOCIATED RISKS
TABLE 2 AI IN HEALTHCARE MARKET: PORTER’S FIVE FORCES ANALYSIS
TABLE 3 ECOSYSTEM: MARKET
TABLE 4 ASP RANGE OF PROCESSOR COMPONENTS, 2019–2028
TABLE 5 ASP RANGE OF SERVER SOFTWARE
TABLE 6 TOP 20 PATENT OWNERS IN LAST 10 YEARS
TABLE 7 MAJOR PATENTS IN MARKET
TABLE 8 TARIFF FOR ELECTRONIC INTEGRATED CIRCUITS AS PROCESSORS AND CONTROLLERS EXPORTED BY US, 2022
TABLE 9 TARIFF FOR ELECTRONIC INTEGRATED CIRCUITS AS PROCESSORS AND CONTROLLERS EXPORTED BY CHINA, 2022
TABLE 10 TARIFF FOR ELECTRONIC INTEGRATED CIRCUITS AS PROCESSORS AND CONTROLLERS EXPORTED BY GERMANY, 2020
TABLE 11 MARKET, BY OFFERING, 2019–2022 (USD MILLION)
TABLE 12  MARKET, BY OFFERING, 2023–2028 (USD MILLION)
TABLE 13 HARDWARE:MARKET, BY TYPE, 2019–2022 (USD MILLION)
TABLE 14 HARDWARE: MARKET, BY TYPE, 2023–2028 (USD MILLION)
TABLE 15 HARDWARE: MARKET, BY REGION, 2019–2022 (USD MILLION)
TABLE 16 HARDWARE: MARKET, BY REGION, 2023–2028 (USD MILLION)
TABLE 17 PROCESSOR: MARKET, BY TYPE, 2019–2022 (MILLION UNITS)
TABLE 18 PROCESSOR: MARKET, BY TYPE, 2023–2028 (MILLION UNITS)
TABLE 19 PROCESSOR: MARKET, BY TYPE, 2019–2022 (USD MILLION)
TABLE 20 PROCESSOR: AI IN HEALTHCARE MARKET, BY TYPE, 2023–2028 (USD MILLION)
TABLE 21 CASE STUDY: DYNALIFE’S AND ALTAML’S COLON POLYP PROJECT
TABLE 22 CASE STUDY: UNIVERSITY OF SYDNEY, BRAIN AND MIND CENTER (SNAC) AND NVIDIA CORPORATION
TABLE 23 FPGA: RECENT DEVELOPMENT
TABLE 24 CASE STUDY: XILINX AND SPLINE. AI
TABLE 25 CASE STUDY: LONDON MEDICAL IMAGING & AI CENTRE & RUN:AI
TABLE 26 MEMORY: RECENT DEVELOPMENT
TABLE 27 CASE STUDY: INTEL, DELL, AND UNIVERSITY OF FLORIDA
TABLE 28 NETWORK: MARKET, BY TYPE, 2019–2022 (MILLION UNITS)
TABLE 29 NETWORK: MARKET, BY TYPE, 2023–2028 (MILLION UNITS)
TABLE 30 NETWORK: MARKET, BY TYPE, 2019–2022 (USD MILLION)
TABLE 31 NETWORK: AI IN HEALTHCARE MARKET, BY TYPE, 2023–2028 (USD MILLION)
TABLE 32 SOFTWARE: MARKET, BY TYPE, 2019–2022 (USD MILLION)
TABLE 33 SOFTWARE: MARKET, BY TYPE, 2023–2028 (USD MILLION)
TABLE 34 SOFTWARE: MARKET, BY REGION, 2019–2022 (USD MILLION)
TABLE 35 SOFTWARE: MARKET, BY REGION, 2023–2028 (USD MILLION)
TABLE 36 CASE STUDY: PHILIPS AND AWS
TABLE 37 SOFTWARE: MARKET FOR AI SOLUTIONS, BY DEPLOYMENT TYPE, 2019–2022 (USD MILLION)
TABLE 38 SOFTWARE: MARKET FOR AI SOLUTIONS, BY DEPLOYMENT TYPE, 2023–2028 (USD MILLION)
TABLE 39 ON-PREMISES: RECENT DEVELOPMENT
TABLE 40 SOFTWARE:  MARKET FOR AI PLATFORMS, BY TYPE, 2019–2022 (USD MILLION)
TABLE 41 SOFTWARE: MARKET FOR AI PLATFORMS, BY TYPE, 2023–2028 (USD MILLION)
TABLE 42 SERVICES: MARKET, BY TYPE, 2019–2022 (USD MILLION)
TABLE 43 SERVICES: MARKET, BY TYPE, 2023–2028 (USD MILLION)
TABLE 44 SERVICES: MARKET, BY REGION, 2019–2022 (USD MILLION)
TABLE 45 SERVICES: MARKET, BY REGION, 2023–2028 (USD MILLION)
TABLE 46 AI IN HEALTHCARE MARKET, BY TECHNOLOGY, 2019–2022 (USD MILLION)
TABLE 47 MARKET, BY TECHNOLOGY, 2023–2028 (USD MILLION)
TABLE 48 CASE STUDY: THE HEALTH MANAGEMENT ACADEMY (THE ACADEMY) AND NUANCE
TABLE 49 CAST STUDY: MAYO CLINIC AND GOOGLE INC
TABLE 50 MACHINE LEARNING: MARKET, BY TYPE, 2019–2022 (USD MILLION)
TABLE 51 MACHINE LEARNING: MARKET, BY TYPE, 2023–2028 (USD MILLION)
TABLE 52 CASE STUDY: SUBTLE MEDICAL AND BAYER
TABLE 53 CASE STUDY: ROCHE AND JOHN SNOW LABS
TABLE 54 NATURAL LANGUAGE PROCESSING: AI IN HEALTHCARE MARKET, BY TYPE, 2019–2022 (USD MILLION)
TABLE 55 NATURAL LANGUAGE PROCESSING:  MARKET, BY TYPE, 2023–2028 (USD MILLION)
TABLE 56 CONTEXT-AWARE COMPUTING: RECENT DEVELOPMENT
TABLE 57 CONTEXT-AWARE COMPUTING:  MARKET, BY TYPE, 2019–2022 (USD MILLION)
TABLE 58 CONTEXT-AWARE COMPUTING:  MARKET, BY TYPE, 2023–2028 (USD MILLION)
TABLE 59 AI IN HEALTHCARE MARKET, BY APPLICATION, 2019–2022 (USD MILLION)
TABLE 60  MARKET, BY APPLICATION, 2023–2028 (USD MILLION)
TABLE 61 CASE STUDY: CLEVELAND CLINIC AND MICROSOFT
TABLE 62 PATIENT DATA & RISK ANALYSIS: MARKET, BY REGION, 2019–2022 (USD MILLION)
TABLE 63 PATIENT DATA & RISK ANALYSIS: MARKET, BY REGION, 2023–2028 (USD MILLION)
TABLE 64 PATIENT DATA & RISK ANALYSIS: MARKET, BY END USER, 2019–2022 (USD MILLION)
TABLE 65 PATIENT DATA & RISK ANALYSIS:  MARKET, BY END USER, 2023–2028 (USD MILLION)
TABLE 66 CASE STUDY: TIDALHEALTH AND REGARDS
TABLE 67 IN-PATIENT CARE & HOSPITAL MANAGEMENT: MARKET, BY REGION, 2019–2022 (USD MILLION)
TABLE 68 IN-PATIENT CARE & HOSPITAL MANAGEMENT: MARKET, BY REGION, 2023–2028 (USD MILLION)
TABLE 69 IN-PATIENT CARE & HOSPITAL MANAGEMENT: MARKET, BY END USER, 2019–2022 (USD MILLION)
TABLE 70 IN-PATIENT CARE & HOSPITAL MANAGEMENT: MARKET, BY END USER, 2023–2028 (USD MILLION)
TABLE 71 MEDICAL IMAGING DIAGNOSIS: RECENT DEVELOPMENT
TABLE 72 MEDICAL IMAGING & DIAGNOSTICS:  MARKET, BY REGION, 2019–2022 (USD MILLION)
TABLE 73 MEDICAL IMAGING & DIAGNOSTICS: MARKET, BY REGION, 2023–2028 (USD MILLION)
TABLE 74 MEDICAL IMAGING & DIAGNOSTICS: MARKET, BY END USER, 2019–2022 (USD MILLION)
TABLE 75 MEDICAL IMAGING & DIAGNOSTICS: MARKET, BY END USER, 2023–2028 (USD MILLION)
TABLE 76 LIFESTYLE MANAGEMENT & REMOTE PATIENT MONITORING: RECENT DEVELOPMENT
TABLE 77 LIFESTYLE MANAGEMENT & REMOTE PATIENT MONITORING: AI IN HEALTHCARE MARKET, BY REGION, 2019–2022 (USD MILLION)
TABLE 78 LIFESTYLE MANAGEMENT & REMOTE PATIENT MONITORING: MARKET, BY REGION, 2023–2028 (USD MILLION)
TABLE 79 LIFESTYLE MANAGEMENT & REMOTE PATIENT MONITORING: MARKET, BY END USER, 2019–2022 (USD MILLION)
TABLE 80 LIFESTYLE MANAGEMENT & REMOTE PATIENT MONITORING: MARKET, BY END USER, 2023–2028 (USD MILLION)
TABLE 81 VIRTUAL ASSISTANT: RECENT DEVELOPMENT
TABLE 82 CASE STUDY: GOVERNMENT OF INDIA, ACCENTURE, AND MICROSOFT
TABLE 83 VIRTUAL ASSISTANT: MARKET, BY REGION, 2019–2022 (USD MILLION)
TABLE 84 VIRTUAL ASSISTANT: MARKET, BY REGION, 2023–2028 (USD MILLION)
TABLE 85 VIRTUAL ASSISTANT: MARKET, BY END USER, 2019–2022 (USD MILLION)
TABLE 86 VIRTUAL ASSISTANT:MARKET, BY END USER, 2023–2028 (USD MILLION)
TABLE 87 DRUG DISCOVERY: RECENT DEVELOPMENT
TABLE 88 DRUG DISCOVERY: MARKET, BY REGION, 2019–2022 (USD MILLION)
TABLE 89 DRUG DISCOVERY: MARKET, BY REGION, 2023–2028 (USD MILLION)
TABLE 90 DRUG DISCOVERY: MARKET, BY END USER, 2019–2022 (USD MILLION)
TABLE 91 DRUG DISCOVERY: MARKET, BY END USER, 2023–2028 (USD MILLION)
TABLE 92 RESEARCH: AI IN HEALTHCARE MARKET, BY REGION, 2019–2022 (USD MILLION)
TABLE 93 RESEARCH: MARKET, BY REGION, 2023–2028 (USD MILLION)
TABLE 94 RESEARCH: MARKET, BY END USER, 2019–2022 (USD MILLION)
TABLE 95 RESEARCH: MARKET, BY END USER, 2023–2028 (USD MILLION)
TABLE 96 HEALTHCARE ASSISTANCE ROBOTS: MARKET, BY REGION, 2019–2022 (USD MILLION)
TABLE 97 HEALTHCARE ASSISTANCE ROBOTS: MARKET, BY REGION, 2023–2028 (USD MILLION)
TABLE 98 HEALTHCARE ASSISTANCE ROBOTS: AI IN HEALTHCARE MARKET, BY END USER, 2019–2022 (USD MILLION)
TABLE 99 HEALTHCARE ASSISTANCE ROBOTS:  MARKET, BY END USER, 2023–2028 (USD MILLION)
TABLE 100 PRECISION MEDICINE: RECENT DEVELOPMENT
TABLE 101 PRECISION MEDICINE: MARKET, BY REGION, 2019–2022 (USD MILLION)
TABLE 102 PRECISION MEDICINE: MARKET, BY REGION, 2023–2028 (USD MILLION)
TABLE 103 PRECISION MEDICINE: MARKET, BY END USER, 2019–2022 (USD MILLION)
TABLE 104 PRECISION MEDICINE:  MARKET, BY END USER, 2023–2028 (USD MILLION)
TABLE 105 EMERGENCY ROOM & SURGERY: AI IN HEALTHCARE MARKET, BY REGION, 2019–2022 (USD MILLION)
TABLE 106 EMERGENCY ROOM & SURGERY: MARKET, BY REGION, 2023–2028 (USD MILLION)
TABLE 107 EMERGENCY ROOM & SURGERY: MARKET, BY END USER, 2019–2022 (USD MILLION)
TABLE 108 EMERGENCY ROOM & SURGERY:MARKET, BY END USER, 2023–2028 (USD MILLION)
TABLE 109 CASE STUDY: KENSCI, MICROSOFT, AND FEDERAL GOVERNMENT
TABLE 110 WEARABLES: MARKET, BY REGION, 2019–2022 (USD MILLION)
TABLE 111 WEARABLES: MARKET, BY REGION, 2023–2028 (USD MILLION)
TABLE 112 WEARABLES: MARKET, BY END USER, 2019–2022 (USD MILLION)
TABLE 113 WEARABLES:MARKET, BY END USER, 2023–2028 (USD MILLION)
TABLE 114 MENTAL HEALTH: RECENT DEVELOPMENT
TABLE 115 MENTAL HEALTH: MARKET, BY REGION, 2019–2022 (USD MILLION)
TABLE 116 MENTAL HEALTH: MARKET, BY REGION, 2023–2028 (USD MILLION)
TABLE 117 MENTAL HEALTH: MARKET, BY END USER, 2019–2022 (USD MILLION)
TABLE 118 MENTAL HEALTH:  MARKET, BY END USER, 2023–2028 (USD MILLION)
TABLE 119 CYBERSECURITY:  MARKET, BY REGION, 2019–2022 (USD MILLION)
TABLE 120 CYBERSECURITY: MARKET, BY REGION, 2023–2028 (USD MILLION)
TABLE 121 CYBERSECURITY: AI IN HEALTHCARE MARKET, BY END USER, 2019–2022 (USD MILLION)
TABLE 122 CYBERSECURITY: MARKET, BY END USER, 2023–2028 (USD MILLION)
TABLE 123 MARKET, BY END USER, 2019–2022 (USD MILLION)
TABLE 124 MARKET, BY END USER, 2023–2028 (USD MILLION)
TABLE 125 HOSPITALS & HEALTHCARE PROVIDERS: RECENT DEVELOPMENT
TABLE 126 HOSPITALS & HEALTHCARE PROVIDERS: AI IN HEALTHCARE MARKET, BY APPLICATION, 2019–2022 (USD MILLION)
TABLE 127 HOSPITALS & HEALTHCARE PROVIDERS: MARKET, BY APPLICATION, 2023–2028 (USD MILLION)
TABLE 128 HOSPITALS & HEALTHCARE PROVIDERS: MARKET, BY REGION, 2019–2022 (USD MILLION)
TABLE 129 HOSPITALS & HEALTHCARE PROVIDERS:  MARKET, BY REGION, 2023–2028 (USD MILLION)
TABLE 130 PATIENTS: RECENT DEVELOPMENT
TABLE 131 PATIENTS: AI IN HEALTHCARE MARKET, BY APPLICATION, 2019–2022 (USD MILLION)
TABLE 132 PATIENTS: MARKET, BY APPLICATION, 2023–2028 (USD MILLION)
TABLE 133 PATIENTS: MARKET, BY REGION, 2019–2022 (USD MILLION)
TABLE 134 PATIENTS: MARKET, BY REGION, 2023–2028 (USD MILLION)
TABLE 135 PHARMACEUTICALS & BIOTECHNOLOGY COMPANIES: RECENT DEVELOPMENT
TABLE 136 PHARMACEUTICALS & BIOTECHNOLOGY COMPANIES: MARKET, BY APPLICATION, 2019–2022 (USD MILLION)
TABLE 137 PHARMACEUTICALS & BIOTECHNOLOGY COMPANIES: MARKET, BY APPLICATION, 2023–2028 (USD MILLION)
TABLE 138 PHARMACEUTICALS & BIOTECHNOLOGY COMPANIES: MARKET, BY REGION, 2019–2022 (USD MILLION)
TABLE 139 PHARMACEUTICALS & BIOTECHNOLOGY COMPANIES: MARKET, BY REGION, 2023–2028 (USD MILLION)
TABLE 140 HEALTHCARE PAYERS: RECENT DEVELOPMENT
TABLE 141 HEALTHCARE PAYERS: MARKET, BY APPLICATION, 2019–2022 (USD MILLION)
TABLE 142 HEALTHCARE PAYERS:MARKET, BY APPLICATION, 2023–2028 (USD MILLION)
TABLE 143 HEALTHCARE PAYERS: MARKET, BY REGION, 2019–2022 (USD MILLION)
TABLE 144 HEALTHCARE PAYERS: MARKET, BY REGION, 2023–2028 (USD MILLION)
TABLE 145 OTHERS: MARKET, BY APPLICATION, 2019–2022 (USD MILLION)
TABLE 146 OTHERS: MARKET, BY APPLICATION, 2023–2028 (USD MILLION)
TABLE 147 OTHERS: MARKET, BY REGION, 2019–2022 (USD MILLION)
TABLE 148 OTHERS: MARKET, BY REGION, 2023–2028 (USD MILLION)
TABLE 149 AI IN HEALTHCARE MARKET, BY REGION, 2019–2022 (USD MILLION)
TABLE 150 MARKET, BY REGION, 2023–2028 (USD MILLION)
TABLE 151 NORTH AMERICA: MARKET, BY COUNTRY, 2019–2022 (USD MILLION)
TABLE 152 NORTH AMERICA: MARKET, BY COUNTRY, 2023–2028 (USD MILLION)
TABLE 153 NORTH AMERICA: MARKET, BY APPLICATION, 2019–2022 (USD MILLION)
TABLE 154 NORTH AMERICA: AMARKET, BY APPLICATION, 2023–2028 (USD MILLION)
TABLE 155 US: RECENT DEVELOPMENT
TABLE 156 CANADA: RECENT DEVELOPMENT
TABLE 157 MEXICO: RECENT DEVELOPMENT
TABLE 158 EUROPE: MARKET, BY COUNTRY, 2019–2022 (USD MILLION)
TABLE 159 EUROPE: AI IN HEALTHCARE MARKET, BY COUNTRY, 2023–2028 (USD MILLION)
TABLE 160 EUROPE: MARKET, BY END USER, 2019–2022 (USD MILLION)
TABLE 161 EUROPE: MARKET, BY END USER, 2023–2028 (USD MILLION)
TABLE 162 EUROPE: MARKET, BY APPLICATION, 2019–2022 (USD MILLION)
TABLE 163 EUROPE: MARKET, BY APPLICATION, 2023–2028 (USD MILLION)
TABLE 164 GERMANY: RECENT DEVELOPMENT
TABLE 165 UK: RECENT DEVELOPMENT
TABLE 166 FRANCE: RECENT DEVELOPMENT
TABLE 167 ITALY: RECENT DEVELOPMENT
TABLE 168 SPAIN: RECENT DEVELOPMENT
TABLE 169 ASIA PACIFIC: MARKET, BY COUNTRY, 2019–2022 (USD MILLION)
TABLE 170 ASIA PACIFIC: MARKET, BY COUNTRY, 2023–2028 (USD MILLION)
TABLE 171 ASIA PACIFIC: AI IN HEALTHCARE MARKET, BY END USER, 2019–2022 (USD MILLION)
TABLE 172 ASIA PACIFIC: MARKET, BY END USER, 2023–2028 (USD MILLION)
TABLE 173 ASIA PACIFIC: MARKET, BY APPLICATION, 2019–2022 (USD MILLION)
TABLE 174 ASIA PACIFIC: MARKET, BY APPLICATION, 2023–2028 (USD MILLION)
TABLE 175 CHINA: RECENT DEVELOPMENT
TABLE 176 SOUTH KOREA: RECENT DEVELOPMENT
TABLE 177 INDIA: RECENT DEVELOPMENT
TABLE 178 REST OF ASIA PACIFIC: RECENT DEVELOPMENT
TABLE 179 REST OF THE WORLD: MARKET, BY REGION, 2019–2022 (USD MILLION)
TABLE 180 REST OF THE WORLD:MARKET, BY REGION, 2023–2028 (USD MILLION)
TABLE 181 REST OF THE WORLD: MARKET, BY END USER, 2019–2022 (USD MILLION)
TABLE 182 REST OF THE WORLD:MARKET, BY END USER, 2023–2028 (USD MILLION)
TABLE 183 REST OF THE WORLD: MARKET, BY APPLICATION, 2019–2022 (USD MILLION)
TABLE 184 REST OF THE WORLD:  MARKET, BY APPLICATION, 2023–2028 (USD MILLION)
TABLE 185 MIDDLE EAST AND AFRICA: RECENT DEVELOPMENT
TABLE 186 OVERVIEW OF STRATEGIES DEPLOYED BY KEY PLAYERS IN AI IN HEALTHCARE MARKET
TABLE 187 MARKET: DEGREE OF COMPETITION
TABLE 188 COMPANY FOOTPRINT
TABLE 189 COMPANY OFFERING FOOTPRINT
TABLE 190 END-USER FOOTPRINT OF COMPANIES
TABLE 191 REGIONAL FOOTPRINT OF COMPANIES
TABLE 192 STARTUPS/SMES MATRIX: DETAILED LIST OF KEY STARTUPS
TABLE 193 AI IN HEALTHCARE MARKET: COMPETITIVE BENCHMARKING OF KEY STARTUPS/SMES
TABLE 194 MARKET: PRODUCT LAUNCHES & DEVELOPMENTS, JANUARY 2019–DECEMBER 2022
TABLE 195  MARKET: DEALS, JANUARY 2019–DECEMBER 2022
TABLE 196 INTEL CORPORATION: BUSINESS OVERVIEW
TABLE 197 KONINKLIJKE PHILIPS N.V.: BUSINESS OVERVIEW
TABLE 198 MICROSOFT: BUSINESS OVERVIEW
TABLE 199 SIEMENS HEALTHINEERS: BUSINESS OVERVIEW
TABLE 200 NVIDIA CORPORATION: BUSINESS OVERVIEW
TABLE 201 GOOGLE INC.: BUSINESS OVERVIEW
TABLE 202 GENERAL ELECTRIC COMPANY: BUSINESS OVERVIEW
TABLE 203 MEDTRONIC: BUSINESS OVERVIEW
TABLE 204 MICRON TECHNOLOGY, INC.: BUSINESS OVERVIEW
TABLE 205 AMAZON WEB SERVICES (AWS): BUSINESS OVERVIEW
TABLE 206 JOHNSON & JOHNSON SERVICES, INC.: BUSINESS OVERVIEW
 
 
LIST OF FIGURES (70 FIGURES)
 
FIGURE 1 AI IN HEALTHCARE MARKET: RESEARCH DESIGN
FIGURE 2  MARKET: RESEARCH APPROACH
FIGURE 3 MARKET SIZE ESTIMATION METHODOLOGY: BOTTOM-UP APPROACH
FIGURE 4 MARKET SIZE ESTIMATION METHODOLOGY: BOTTOM-UP (SUPPLY SIDE) —ILLUSTRATION OF REVENUE ESTIMATION OF COMPANIES FROM SALES OF AI IN HEALTHCARE OFFERING
FIGURE 5 MARKET SIZE ESTIMATION METHODOLOGY: BOTTOM-UP (DEMAND SIDE) —ESTIMATION OF SIZE OF MARKET, BY END USER
FIGURE 6 AI IN HEALTHCARE MARKET SIZE ESTIMATION METHODOLOGY: TOP-DOWN APPROACH
FIGURE 7 MARKET SIZE ESTIMATION METHODOLOGY: (SUPPLY SIDE)—REVENUE GENERATED FROM AI IN HEALTHCARE OFFERINGS
FIGURE 8 DATA TRIANGULATION
FIGURE 9 ASSUMPTIONS FOR RESEARCH STUDY
FIGURE 10 RECESSION IMPACT: GDP GROWTH PROJECTION TILL 2023 FOR MAJOR ECONOMIES
FIGURE 11 RECESSION IMPACT ON MARKET, 2019–2028 (USD MILLION)
FIGURE 12 SOFTWARE SEGMENT TO HOLD SECOND-LARGEST SHARE OF AI IN HEALTHCARE MARKET FROM 2023 TO 2028
FIGURE 13 MACHINE LEARNING SEGMENT TO HOLD LARGEST SHARE OF MARKET FROM 2023 TO 2028
FIGURE 14 PATIENTS SEGMENT TO REGISTER HIGHEST CAGR MARKET DURING FORECAST PERIOD
FIGURE 15 MEDICAL IMAGING & DIAGNOSTICS APPLICATION TO GROW AT HIGHEST CAGR MARKET DURING FORECAST PERIOD
FIGURE 16 NORTH AMERICA ACCOUNTED FOR LARGEST SHARE OF MARKET IN 2022
FIGURE 17 INCREASING ADOPTION OF AI-BASED TOOLS IN HEALTHCARE FACILITIES TO DRIVE MARKET GROWTH DURING 2023–2028
FIGURE 18 SOFTWARE TO ACCOUNT FOR LARGEST SHARE OF MARKET FROM 2023 TO 2028
FIGURE 19 MACHINE LEARNING TECHNOLOGY TO BE LARGEST SHAREHOLDER OF MARKET FROM 2023 TO 2028
FIGURE 20 MEDICAL IMAGING & DIAGNOSTICS SEGMENT TO REGISTER HIGHEST CAGR DURING 2023–2028
FIGURE 21 HOSPITALS & HEALTHCARE PROVIDERS TO BE LARGEST SHAREHOLDERS OF MARKET IN 2023 TO 2028
FIGURE 22 AI IN HEALTHCARE MARKET IN CHINA AND MEXICO TO GROW AT HIGHEST CAGR FROM 2023 TO 2028
FIGURE 23 MARKET: DRIVERS, RESTRAINTS, OPPORTUNITIES, AND CHALLENGES
FIGURE 24 ANALYSIS OF IMPACT OF DRIVERS ON MARKET
FIGURE 25 ANALYSIS OF IMPACT OF RESTRAINTS ONMARKET
FIGURE 26 ANALYSIS OF IMPACT OF OPPORTUNITIES ON MARKET
FIGURE 27 ANALYSIS OF IMPACT OF CHALLENGES ON AI IN HEALTHCARE MARKET
FIGURE 28 HEALTHCARE BREACHES REPORTED TO US DEPARTMENT OF HEALTH AND HUMAN SERVICES, 2019 TO 2021
FIGURE 29 CHALLENGES OF HEALTHCARE DATA INTEROPERABILITY
FIGURE 30  AI IN HEALTHCARE MARKET VALUE CHAIN
FIGURE 31 ECOSYSTEM OF AI IN HEALTHCARE
FIGURE 32 TRENDS/DISRUPTION IMPACTING CUSTOMER BUSINESS
FIGURE 33 AVERAGE SELLING PRICE OF PROCESSOR COMPONENTS IN MARKET, 2019–2028 (USD)
FIGURE 34 AVERAGE SELLING PRICE OF PROCESSOR COMPONENTS OFFERED BY KEY COMPANIES
FIGURE 35 EXPORT DATA FOR HS CODE 854231 FOR TOP COUNTRIES IN  MARKET, 2017–2021 (USD THOUSAND)
FIGURE 36 IMPORT DATA FOR HS CODE 854231 FOR TOP COUNTRIES IN MARKET, 2017–2021 (USD THOUSAND)
FIGURE 37 COMPANIES WITH HIGHEST NUMBER OF PATENT APPLICATIONS IN LAST 10 YEARS
FIGURE 38 NUMBER OF PATENTS GRANTED PER YEAR, 2012–2021
FIGURE 39 MARKET, BY OFFERING
FIGURE 40 SOFTWARE TO HOLD LARGEST SHARE OF MARKET DURING FORECAST PERIOD
FIGURE 41 MARKET, BY TECHNOLOGY
FIGURE 42 MACHINE LEARNING TECHNOLOGY TO HOLD LARGEST SHARE OF MARKET DURING FORECAST PERIOD
FIGURE 43  MARKET, BY APPLICATION
FIGURE 44 MEDICAL IMAGING & DIAGNOSTICS TO ACCOUNT FOR LARGEST SHARE OF MARKET IN 2028
FIGURE 45 AI IN HEALTHCARE MARKET, BY END USER
FIGURE 46 HOSPITALS & HEALTHCARE PROVIDERS TO HOLD LARGEST MARKET SHARE DURING FORECAST PERIOD
FIGURE 47 CHINA AND MEXICO TO EMERGE AS NEW HOTSPOTS FOR MARKET
FIGURE 48 ASIA PACIFIC TO REGISTER HIGHEST CAGR DURING FORECAST PERIOD
FIGURE 49 NORTH AMERICA: MARKET SNAPSHOT
FIGURE 50 US TO DOMINATE  MARKET IN NORTH AMERICA IN 2028
FIGURE 51 EUROPE: MARKET SNAPSHOT
FIGURE 52 REST OF EUROPE TO EXHIBIT HIGHEST CAGR IN EUROPEAN AI IN HEALTHCARE MARKET IN 2028
FIGURE 53 ASIA PACIFIC: MARKET SNAPSHOT
FIGURE 54 CHINA TO EXHIBIT HIGHEST CAGR IN MARKET ASIA PACIFIC IN 2028
FIGURE 55 REST OF THE WORLD: SNAPSHOT OF MARKET
FIGURE 56 SOUTH AMERICA TO DOMINATE MARKET IN ROW IN 2028
FIGURE 57 FIVE-YEAR REVENUE ANALYSIS OF TOP PLAYERS IN MARKET
FIGURE 58 MARKET: COMPANY EVALUATION QUADRANT, 2022
FIGURE 59  AI IN HEALTHCARE MARKET: STARTUP/SME EVALUATION QUADRANT, 2022
FIGURE 60 INTEL CORPORATION: COMPANY SNAPSHOT
FIGURE 61 KONINKLIJKE PHILIPS N.V.: COMPANY SNAPSHOT
FIGURE 62 MICROSOFT: COMPANY SNAPSHOT
FIGURE 63 SIEMENS HEALTHINEERS: COMPANY SNAPSHOT
FIGURE 64 NVIDIA CORPORATION: COMPANY SNAPSHOT
FIGURE 65 GOOGLE INC.: COMPANY SNAPSHOT
FIGURE 66 GENERAL ELECTRIC COMPANY: COMPANY SNAPSHOT
FIGURE 67 MEDTRONIC: COMPANY SNAPSHOT
FIGURE 68 MICRON TECHNOLOGY, INC.: COMPANY SNAPSHOT
FIGURE 69 AMAZON WEB SERVICES: COMPANY SNAPSHOT
FIGURE 70 JOHNSON & JOHNSON SERVICES, INC.: COMPANY SNAPSHOT

 

 

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, industry 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 size 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 industry 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 size , 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 size .

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Report Code
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Published ON
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