Artificial Intelligence (AI) in Healthcare Market by Offering (Integrated), Function (Diagnosis, Genomic, Precision Medicine, Radiation, Immunotherapy, Pharmacy, Supply Chain), Application (Clinical), End User (Hospitals), Region - Global Forecast to 2030
The global Artificial Intelligence (AI) in healthcare market is projected to reach USD 110.61 billion by 2030 from USD 21.66 billion in 2025, at a CAGR of 38.6% during the forecast period. The market is expected to grow due to the growing investments & funding by public-private organizations, the fast proliferation of AI in the healthcare industry, and the rising focus on developing human-aware AI systems. The market has experienced growth due to increasing demand for enhanced services due to an unequal ratio between the healthcare workforce and patient numbers. However, inadequate IT infrastructure and unwillingness to adopt AI-based healthcare solutions in emerging economies are estimated to pose a challenge to market growth.
By deployment, the cloud-based segment is expected to register the highest growth during the forecast period.
The AI in healthcare market is categorized into three deployment models: on-premise, cloud-based, and hybrid models. The cloud-based models segment holds the largest share due to the scalability, cost-effectiveness, and accessibility of these models. Cloud-based models facilitate real-time data processing and collaboration. They allow for seamless integration, secure data storage, and rapid deployment, making them particularly suitable for healthcare providers and payers. They provide faster, more reliable care while maintaining high-quality standards. Cloud-based AI solutions are becoming increasingly popular due to their cost-effectiveness, scalability, and support for remote access. These solutions enable seamless integration and real-time analytics. The growing adoption of telehealth and advancements in healthcare IT infrastructure further drive the demand for cloud-based models.
By end user, the hospitals & clinics segment dominated the market in the Artificial Intelligence (AI) in healthcare market for healthcare providers in 2024.
By end user, Artificial Intelligence (AI) in healthcare market is segmented into hospitals & clinics, ambulatory surgical centers, home healthcare agencies & assisted living facilities, diagnostic & imaging centers, pharmacies, and other healthcare providers. The hospitals & clinics segment accounted for the largest share of the Artificial Intelligence (AI) in healthcare market. This is attributed to the increasing demand for personalized medicines, precise diagnostics & surgical planning, growth in minimally-invasive procedures, and the requirement for interoperability with existing systems. AI-based healthcare solutions enhance diagnostic accuracy, streamline operations, and personalize care in hospitals and clinics. They automate administrative tasks, predict patient outcomes, and enable faster decision-making with real-time data analysis. AI also supports remote monitoring, optimizes resources, and reduces costs by minimizing unnecessary treatments. AI improves patient engagement through virtual assistants and enhances security by detecting fraud, making healthcare more efficient, accessible, and cost-effective.
Asia Pacific is expected to register the highest growth during the forecast period.
The Artificial Intelligence (AI) in healthcare market is divided into North America, Europe, Asia Pacific, Latin America, and Middle East & Africa. The Asia Pacific region is expected to register the highest growth during the forecast period. The Asia Pacific (APAC) region is experiencing substantial growth in the adoption of AI technologies within the healthcare sector, driven by a combination of demographic shifts, technological advancements, and increased investments in innovation. The rising elderly population in the Asia Pacific is a key factor, with the proportion of individuals aged 65 years and above increasing significantly. According to the UN’s World Population Aging 2020 report, the global population in this age group is expected to double from 727 million in 2020 to 1.5 billion by 2050, with Eastern and Southeastern Asia contributing a large share of this growth.
The break-down of primary participants is as mentioned below:
The prominent players operating in the Artificial Intelligence (AI) in healthcare market include Koninklijke Philips N.V. (Netherlands), Microsoft Corporation (US), Siemens Healthineers AG (Germany), NVIDIA Corporation (US), Epic Systems Corporation (US), GE Healthcare (US), Medtronic (US), Oracle (US), Veradigm LLC (US), Merative (IBM) (US), Google (US), Cognizant (US), Johnson & Johnson (US), Amazon Web Services, Inc. (US), SOPHiA GENETICS (US), Riverian Technologies (US), Terarecon (ConcertAI) (US), Solventum Corporation (US), Tempus (US), Viz.ai (US). These companies adopted strategies such as product launches, product updates, expansions, partnerships, collaborations, mergers, and acquisitions to strengthen their market presence in the Artificial Intelligence (AI) in healthcare market.
Research Coverage
The report analyzes the Artificial Intelligence (AI) in healthcare market and aims to estimate the market size and future growth potential of various market segments based on offering, solution type, imaging modality, application, end user, and region. The report also analyzes factors (such as drivers, opportunities, and challenges) affecting market growth. It evaluates the opportunities and challenges in the market stakeholders. The report also studies micro markets with respect to their growth trends, prospects, and contributions to the total Artificial Intelligence (AI) in healthcare market. The report forecasts the revenue of the market segments with respect to five major regions. The report also provides a competitive analysis of the key players in this market, along with their company profiles, product offerings, recent developments, and key market strategies.
Reasons to Buy the Report
This report will enrich established firms as well as new entrants/smaller firms to gauge the pulse of the market, which, in turn, would help them garner a higher market share. Firms purchasing the report could use one or a combination of the following strategies to strengthen their positions in the market.
This report provides insights on:
By deployment, the cloud-based segment is expected to register the highest growth during the forecast period.
The AI in healthcare market is categorized into three deployment models: on-premise, cloud-based, and hybrid models. The cloud-based models segment holds the largest share due to the scalability, cost-effectiveness, and accessibility of these models. Cloud-based models facilitate real-time data processing and collaboration. They allow for seamless integration, secure data storage, and rapid deployment, making them particularly suitable for healthcare providers and payers. They provide faster, more reliable care while maintaining high-quality standards. Cloud-based AI solutions are becoming increasingly popular due to their cost-effectiveness, scalability, and support for remote access. These solutions enable seamless integration and real-time analytics. The growing adoption of telehealth and advancements in healthcare IT infrastructure further drive the demand for cloud-based models.
By end user, the hospitals & clinics segment dominated the market in the Artificial Intelligence (AI) in healthcare market for healthcare providers in 2024.
By end user, Artificial Intelligence (AI) in healthcare market is segmented into hospitals & clinics, ambulatory surgical centers, home healthcare agencies & assisted living facilities, diagnostic & imaging centers, pharmacies, and other healthcare providers. The hospitals & clinics segment accounted for the largest share of the Artificial Intelligence (AI) in healthcare market. This is attributed to the increasing demand for personalized medicines, precise diagnostics & surgical planning, growth in minimally-invasive procedures, and the requirement for interoperability with existing systems. AI-based healthcare solutions enhance diagnostic accuracy, streamline operations, and personalize care in hospitals and clinics. They automate administrative tasks, predict patient outcomes, and enable faster decision-making with real-time data analysis. AI also supports remote monitoring, optimizes resources, and reduces costs by minimizing unnecessary treatments. AI improves patient engagement through virtual assistants and enhances security by detecting fraud, making healthcare more efficient, accessible, and cost-effective.
Asia Pacific is expected to register the highest growth during the forecast period.
The Artificial Intelligence (AI) in healthcare market is divided into North America, Europe, Asia Pacific, Latin America, and Middle East & Africa. The Asia Pacific region is expected to register the highest growth during the forecast period. The Asia Pacific (APAC) region is experiencing substantial growth in the adoption of AI technologies within the healthcare sector, driven by a combination of demographic shifts, technological advancements, and increased investments in innovation. The rising elderly population in the Asia Pacific is a key factor, with the proportion of individuals aged 65 years and above increasing significantly. According to the UN’s World Population Aging 2020 report, the global population in this age group is expected to double from 727 million in 2020 to 1.5 billion by 2050, with Eastern and Southeastern Asia contributing a large share of this growth.
The break-down of primary participants is as mentioned below:
- By Company Type - Tier 1: 32%, Tier 2: 44%, and Tier 3: 24%
- By Designation - Directors: 30%, Manager: 34%, and Others: 36%
- By Region - North America: 40%, Europe: 28%, Asia Pacific: 20%, Latin America: 7% and Middle East & Africa: 5%
The prominent players operating in the Artificial Intelligence (AI) in healthcare market include Koninklijke Philips N.V. (Netherlands), Microsoft Corporation (US), Siemens Healthineers AG (Germany), NVIDIA Corporation (US), Epic Systems Corporation (US), GE Healthcare (US), Medtronic (US), Oracle (US), Veradigm LLC (US), Merative (IBM) (US), Google (US), Cognizant (US), Johnson & Johnson (US), Amazon Web Services, Inc. (US), SOPHiA GENETICS (US), Riverian Technologies (US), Terarecon (ConcertAI) (US), Solventum Corporation (US), Tempus (US), Viz.ai (US). These companies adopted strategies such as product launches, product updates, expansions, partnerships, collaborations, mergers, and acquisitions to strengthen their market presence in the Artificial Intelligence (AI) in healthcare market.
Research Coverage
The report analyzes the Artificial Intelligence (AI) in healthcare market and aims to estimate the market size and future growth potential of various market segments based on offering, solution type, imaging modality, application, end user, and region. The report also analyzes factors (such as drivers, opportunities, and challenges) affecting market growth. It evaluates the opportunities and challenges in the market stakeholders. The report also studies micro markets with respect to their growth trends, prospects, and contributions to the total Artificial Intelligence (AI) in healthcare market. The report forecasts the revenue of the market segments with respect to five major regions. The report also provides a competitive analysis of the key players in this market, along with their company profiles, product offerings, recent developments, and key market strategies.
Reasons to Buy the Report
This report will enrich established firms as well as new entrants/smaller firms to gauge the pulse of the market, which, in turn, would help them garner a higher market share. Firms purchasing the report could use one or a combination of the following strategies to strengthen their positions in the market.
This report provides insights on:
- Analysis of key drivers (exponential growth in data volume and complexity due to surging adoption of digital technologies, significant cost pressure on healthcare service providers with increasing prevalence of chronic diseases, rapid proliferation of AI in healthcare sector, growing need for improvised healthcare services, growing need for early detection and diagnosis, restraints (reluctance among medical practitioners to adopt AI-based technologies, shortage of skilled AI professionals handling AI-powered solutions, lack of standardized frameworks for AI and ML technologies), opportunities (increasing use of AI-powered solutions in elderly care, increasing focus on developing human-aware AI systems, strategic partnerships and collaborations among healthcare companies and AI technology providers), challenges (inaccurate predictions due to scarcity of high-quality healthcare data, concerns regarding data privacy, lack of interoperability between AI solutions offered by different vendors) are factors contributing the growth of the Artificial Intelligence (AI) in healthcare market
- Product Development/Innovation: Detailed insights into upcoming trends, research & development activities, and software launches in the Artificial Intelligence (AI) in healthcare market
- Market Development: Comprehensive information on the lucrative emerging markets, type of solution, component, deployment model, industry, and region
- Market Diversification: Exhaustive information about software portfolios, growing geographies, recent developments, and investments in the Artificial Intelligence (AI) in healthcare market
- Competitive Assessment: In-depth assessment of market shares, growth strategies, product offerings, company evaluation quadrant, and capabilities of leading players in the global Artificial Intelligence (AI) in healthcare market, such as Koninklijke Philips N.V. (Netherlands), Microsoft Corporation (US), Siemens Healthineers AG (Germany), NVIDIA Corporation (US), Epic Systems Corporation (US)
1 INTRODUCTION
1.1 STUDY OBJECTIVES
1.2 MARKET DEFINITION
1.3 STUDY SCOPE
1.3.1 MARKETS COVERED
1.3.2 INCLUSIONS & EXCLUSIONS
1.3.3 YEARS CONSIDERED
1.4 CURRENCY CONSIDERED
1.5 STAKEHOLDERS
2 RESEARCH METHODOLOGY
2.1 RESEARCH DATA
2.1.1 SECONDARY DATA
2.1.1.1 Key data from secondary sources
2.1.2 PRIMARY DATA
2.1.2.1 Key industry insights
2.2 MARKET SIZE ESTIMATION
2.3 DATA TRIANGULATION
2.4 MARKET SHARE ESTIMATION
2.5 STUDY ASSUMPTIONS
2.6 LIMITATIONS
2.6.1 METHODOLOGY-RELATED LIMITATIONS
2.6.2 SCOPE-RELATED LIMITATIONS
2.7 RISK ASSESSMENT
3 EXECUTIVE SUMMARY
4 PREMIUM INSIGHTS
4.1 AI IN HEALTHCARE MARKET OVERVIEW
4.2 ASIA PACIFIC: AI IN HEALTHCARE, BY OFFERING AND COUNTRY
4.3 AI IN HEALTHCARE MARKET: GEOGRAPHIC GROWTH OPPORTUNITIES
4.4 AI IN HEALTHCARE MARKET: REGIONAL MIX
4.5 AI IN HEALTHCARE: DEVELOPED VS. EMERGING MARKETS
5 MARKET OVERVIEW
5.1 INTRODUCTION
5.2 MARKET DYNAMICS
5.2.1 DRIVERS
5.2.1.1 Increase in need for early detection and diagnosis
5.2.1.2 Exponential growth in data volume and complexity due to surging adoption of digital technologies
5.2.1.3 Significant cost pressure on healthcare service providers with increasing prevalence of chronic diseases
5.2.1.4 Rapid proliferation of AI in healthcare sector
5.2.1.5 Growth in need for improvised healthcare services
5.2.2 RESTRAINTS
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 AI and ML technologies
5.2.3 OPPORTUNITIES
5.2.3.1 Increasing use of AI-powered solutions in elderly care
5.2.3.2 Increase in focus on developing human-aware AI systems
5.2.3.3 Strategic partnerships and collaborations among healthcare companies and AI technology providers
5.2.4 CHALLENGES
5.2.4.1 Inaccurate predictions due to scarcity of high-quality 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 TRENDS/DISRUPTIONS IMPACTING CUSTOMERS’ BUSINESSES
5.4 TECHNOLOGY ANALYSIS
5.4.1 KEY TECHNOLOGIES
5.4.1.1 Machine learning (ML) and deep learning
5.4.1.2 Natural language processing (NLP)
5.4.1.3 Computer vision (CV)
5.4.2 COMPLEMENTARY TECHNOLOGIES
5.4.2.1 Cloud computing
5.4.2.2 Digital twins
5.4.2.3 Robotic process automation (RPA)
5.4.3 ADJACENT TECHNOLOGIES
5.4.3.1 Blockchain
5.4.3.2 Augmented and virtual reality (AR/VR)
5.4.3.3 Internet of things (IoT)
5.5 INDUSTRY TRENDS
5.5.1 SHIFT TOWARD PERSONALIZED MEDICINE
5.5.2 AI IN DIAGNOSTICS AND IMAGING
5.6 PRICING ANALYSIS
5.6.1 INDICATIVE PRICING OF AI IN HEALTHCARE SOFTWARE, BY DEPLOYMENT MODEL (QUALITATIVE)
5.6.2 INDICATIVE PRICING OF AI IN HEALTHCARE SOFTWARE, BY REGION (QUALITATIVE)
5.7 VALUE CHAIN ANALYSIS
5.8 ECOSYSTEM ANALYSIS
5.9 PATENT ANALYSIS
5.9.1 INSIGHTS: JURISDICTION AND TOP APPLICANT ANALYSIS
5.10 KEY CONFERENCES & EVENTS
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 IN ACCELERATING DIAGNOSIS AND TREATMENT AT LOWER COSTS
5.11.4 GE HEALTHCARE IMPROVED PATIENT OUTCOMES BY REDUCING WORKFLOW PROCESSING TIME USING MEDICAL IMAGING DATA
5.12 REGULATORY LANDSCAPE
5.12.1 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
5.12.2 REGULATORY FRAMEWORK
5.12.2.1 North America
5.12.2.2 Europe
5.12.2.3 Asia Pacific
5.12.2.4 Middle East & Africa
5.12.2.5 Latin America
5.13 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 & BUYING CRITERIA
5.14.1 KEY STAKEHOLDERS IN BUYING PROCESS
5.14.2 BUYING CRITERIA
5.15 END-USER ANALYSIS
5.15.1 UNMET NEEDS
5.15.2 END-USER EXPECTATIONS
5.16 AI IN HEALTHCARE BUSINESS MODELS
5.16.1 SOFTWARE-AS-A-SERVICE (SAAS) MODEL
5.16.2 LICENSING MODEL
5.16.3 REVENUE SHARING/OUTCOME-BASED MODEL
5.16.4 FREEMIUM MODEL
5.16.5 AI-AS-A-SERVICE (AIAAS) MODEL
5.16.6 PARTNERSHIP/REVENUE-SHARING MODEL
5.16.7 HYBRID MODELS
5.16.8 PAY-PER-USE MODELS
5.17 INVESTMENT & FUNDING SCENARIO
5.18 IMPACT OF GENERATIVE AI ON AI IN HEALTHCARE MARKET
5.18.1 INTRODUCTION
5.18.2 MARKET POTENTIAL OF GEN AI IN HEALTHCARE
5.18.2.1 Key use cases of Gen AI
5.18.3 CASE STUDIES OF AI/GENERATIVE AI IMPLEMENTATION
5.18.3.1 Eka Care leveraging generative AI for improved health outcomes
5.18.4 INTERCONNECTED AND ADJACENT ECOSYSTEMS
5.18.4.1 AI in healthcare IT
5.18.4.2 AI in medical diagnostics
5.18.4.3 AI in oncology
5.18.4.4 AI in clinical trials
5.18.4.5 AI in drug discovery
5.18.5 USER READINESS & IMPACT ASSESSMENT
5.18.5.1 User readiness
5.18.5.1.1 Healthcare providers
5.18.5.1.2 Healthcare payers
5.18.5.1.3 Patients
5.18.5.2 Impact assessment
5.18.5.2.1 User A: Healthcare providers
5.18.5.2.1.1 Implementation
5.18.5.2.1.2 Impact
5.18.5.2.2 User B: Healthcare payers
5.18.5.2.2.1 Implementation
5.18.5.2.2.2 Impact
5.18.5.2.3 User C: Patients
5.18.5.2.3.1 Implementation
5.18.5.2.3.2 Impact
6 IMPACT OF 2025 US TARIFF – OVERVIEW
6.1 INTRODUCTION
6.2 KEY TARIFF RATES
6.3 PRICE IMPACT ANALYSIS
6.4 IMPACT ON COUNTRY/REGION
6.4.1 US
6.4.2 EUROPE
6.4.3 ASIA PACIFIC
6.5 IMPACT ON END-USE INDUSTRIES
7 AI IN HEALTHCARE MARKET, BY OFFERING
7.1 INTRODUCTION
7.2 INTEGRATED SOLUTIONS
7.2.1 RISE IN WORKFORCE CHALLENGES AND COST PRESSURES TO DRIVE ADOPTION
7.3 NICHE/POINT SOLUTIONS
7.3.1 TARGETED AI SOLUTIONS TRANSFORMING PRECISION CARE AND EFFICIENCY IN HEALTHCARE TO BOOST MARKET
7.4 AI TECHNOLOGIES
7.4.1 ABILITY OF CORE AI TECHNOLOGIES TO DRIVE PRECISION, EFFICIENCY, AND INNOVATION TO SUPPORT MARKET GROWTH
7.5 SERVICES
7.5.1 NEED TO EMPOWER NON-CLINICAL HEALTHCARE OPERATIONS TO FUEL MARKET GROWTH
8 AI IN HEALTHCARE MARKET, BY FUNCTION
8.1 INTRODUCTION
8.2 DIAGNOSIS & EARLY DETECTION
8.2.1 PRE-SCREENING
8.2.1.1 Early detection, better outcomes, and cost-effective care associated with pre-screening to boost market
8.2.2 IVD
8.2.2.1 IVD market, by technology
8.2.2.1.1 Immunoassays
8.2.2.1.1.1 Increase in focus on earlier disease detection & personalized treatment planning to drive market
8.2.2.1.2 Clinical chemistry
8.2.2.1.2.1 Increased demand for precision and personalized medicine and efficient healthcare systems to drive market
8.2.2.1.3 Molecular diagnostics
8.2.2.1.3.1 Improved disease detection, personalized treatments, and enhanced outcomes to fuel growth
8.2.2.2 IVD market, by application
8.2.2.2.1 Image analysis & interpretation
8.2.2.2.1.1 Advantages such as enhanced diagnostic accuracy, faster detection, and improved patient outcomes to support growth
8.2.2.2.2 Biomarker discovery & analysis
8.2.2.2.2.1 Ability of AI-based biomarker discovery to enhance disease detection, prognosis, and personalized treatment to drive adoption
8.2.2.2.3 Other IVD applications
8.2.3 DIAGNOSTIC IMAGING
8.2.3.1 Diagnostic imaging market, by application
8.2.3.1.1 Disease interpretation & report analysis
8.2.3.1.1.1 Ability of AI-driven disease interpretation to accelerate diagnosis, treatment, and outcomes to fuel growth
8.2.3.1.2 Image captioning & annotation
8.2.3.1.2.1 Enhanced diagnostic accuracy, efficiency, and consistency associated with image captioning & annotation to boost market
8.2.3.1.3 Image reconstruction
8.2.3.1.3.1 Improved diagnostic precision, efficiency, and image quality in healthcare to drive market
8.2.3.1.4 Other diagnostic imaging applications
8.2.3.2 Diagnostic imaging market, by modality
8.2.3.2.1 Magnetic resonance imaging (MRI)
8.2.3.2.1.1 Rise in technological advancements to drive adoption of AI in MRI
8.2.3.2.2 Computed tomography (CT)
8.2.3.2.2.1 Increase in availability of cardiac CT devices enabled with AI solutions to drive market
8.2.3.2.3 X-ray imaging
8.2.3.2.3.1 Innovative AI solutions for X-ray imaging by key players to drive market
8.2.3.2.4 Ultrasound
8.2.3.2.4.1 Increase in prevalence of and need for early detection of ovarian cancer to drive market
8.2.3.2.5 Nuclear imaging (PET & SPECT)
8.2.3.2.5.1 Critical role played by PET and SPECT in visualizing metabolic processes to contribute to growth
8.2.3.2.6 Other diagnostic imaging modalities
8.2.4 RISK ASSESSMENT & PATIENT STRATIFICATION
8.2.4.1 AI-driven risk assessment and patient stratification to enhance healthcare efficiency, outcomes, and personalized care
8.2.5 DRUG ALLERGY ALERTING
8.2.5.1 Ability of AI-enhanced drug allergy alerting to improve accuracy and patient safety to boost market
8.2.6 OTHER DIAGNOSIS & EARLY DETECTION FUNCTIONS
8.3 TREATMENT PLANNING & PERSONALIZATION
8.3.1 PERSONALIZED TREATMENT PLANNING
8.3.1.1 Precision medicine & genomic analysis
8.3.1.1.1 Advantages such as accurate disease predictions and improved outcomes to drive demand
8.3.1.2 Predictive models for treatment response
8.3.1.2.1 Ability to personalize therapies, improve outcomes, and minimize adverse effects to fuel growth
8.3.1.3 Treatment recommendation systems
8.3.1.3.1 Optimized healthcare delivery associated with treatment recommendation systems to support growth
8.3.2 PHARMACOLOGICAL THERAPY
8.3.2.1 Drug response prediction
8.3.2.1.1 Growth in use of drug response prediction to minimize adverse reactions to drive segment
8.3.2.2 Dosing & administration
8.3.2.2.1 Improved efficacy with highly personalized administration care to fuel adoption
8.3.2.3 Other pharmacological therapy functions
8.3.3 SURGICAL THERAPY
8.3.3.1 Preoperative imaging & 3D modeling
8.3.3.1.1 Enhanced surgical planning and precision to propel market
8.3.3.2 Intraoperative guidance & robotics
8.3.3.2.1 Faster recovery time post surgery to boost adoption
8.3.3.3 Postoperative analysis & recovery
8.3.3.3.1 Reduced complications with real-time monitoring to drive market
8.3.4 RADIATION THERAPY
8.3.4.1 Motion synchronization & auto contouring
8.3.4.1.1 Optimized radiation therapy with motion synchronization & auto contouring to boost market
8.3.4.2 Real-time adaptive treatment delivery
8.3.4.2.1 Reduced side effects with effective adaptive treatment to propel demand
8.3.4.3 Response assessment & quality assurance
8.3.4.3.1 Ability of AI to enhance response assessment and QA to spur growth
8.3.4.4 Other radiation therapy functions
8.3.5 BEHAVIORAL THERAPY & PSYCHOTHERAPY
8.3.5.1 Virtual counseling & chatbots
8.3.5.1.1 Ability to engage patients remotely to drive market demand
8.3.5.2 Progress monitoring & feedback
8.3.5.2.1 Increasing use of smart wearables and mobile health apps to drive market
8.3.5.3 Follow-up & long-term support
8.3.5.3.1 Widespread adoption of telemedicine platforms to drive market demand
8.3.6 IMMUNOTHERAPY
8.3.6.1 Real-time patient data monitoring
8.3.6.1.1 Need for real-time data analysis to make informed decisions to boost market growth
8.3.6.2 Response & side-effect prediction
8.3.6.2.1 Ability to reduce trial-and-error approach in medical treatments to drive segmental growth
8.3.6.3 Relapse prediction & long-term management
8.3.6.3.1 Need for proactive approach for long-term care to fuel market growth
8.3.7 OTHER TREATMENT PLANNING & PERSONALIZATION FUNCTIONS
8.4 PATIENT ENGAGEMENT & REMOTE MONITORING
8.4.1 SYMPTOM MANAGEMENT & VIRTUAL ASSISTANCE
8.4.1.1 Rising prevalence of chronic diseases to drive market growth
8.4.2 TELEHEALTH & REMOTE PATIENT MONITORING
8.4.2.1 Ability to monitor and evaluate patients remotely to boost market demand
8.4.3 HEALTHCARE ASSISTANCE ROBOTS
8.4.3.1 Advancements in robotics and artificial intelligence (AI) to boost market demand
8.4.4 MEDICATION REMINDERS
8.4.4.1 Increasing adoption of smartphone applications and IoT devices to surge market demand
8.4.5 PATIENT EDUCATION & EMPOWERMENT
8.4.5.1 Need to improve treatment adherence and enhance self-management to drive market growth
8.4.6 OTHER PATIENT ENGAGEMENT & REMOTE MONITORING FUNCTIONS
8.5 POST-TREATMENT SURVEILLANCE & SURVIVORSHIP CARE
8.5.1 RECURRENCE MONITORING
8.5.1.1 Need to predict recurrence of cancer to drive market
8.5.2 LONG-TERM OUTCOME PREDICTION
8.5.2.1 Ability to predict long-term health outcomes and enhance patient care to drive market
8.5.3 MENTAL HEALTH & SUPPORT SYSTEMS
8.5.3.1 Need to help patients cope with mental strain to drive market
8.6 PHARMACY MANAGEMENT
8.6.1 EPRESCRIBING
8.6.1.1 Need to improve safety and efficiency in prescription to drive market
8.6.2 MEDICATION MANAGEMENT
8.6.2.1 Ability to predict adverse drug reactions by patient data analysis to drive market
8.6.3 PHARMACY AUDIT & ANALYSIS
8.6.3.1 Need for effective and efficient operation of pharmacies to drive market
8.6.4 OTHER PHARMACY MANAGEMENT FUNCTIONS
8.7 DATA MANAGEMENT & ANALYTICS
8.7.1 ABILITY OF DATA MANAGEMENT & ANALYTICS TO HELP HEALTHCARE ORGANIZATIONS MAKE INFORMED DECISIONS TO BOOST MARKET
8.8 ADMINISTRATIVE
8.8.1 PATIENT REGISTRATION & SCHEDULING
8.8.1.1 Need to optimize patient registration process for smoother operations to drive market
8.8.2 PATIENT ELIGIBILITY & AUTHORIZATION
8.8.2.1 Need to reduce administrative burden to drive market
8.8.3 BILLING & CLAIMS MANAGEMENT
8.8.3.1 Need to make billing process more accurate, efficient, and transparent to drive market
8.8.4 WORKFORCE MANAGEMENT
8.8.4.1 Need for effective staff management to drive market
8.8.5 SUPPLY CHAIN & INVENTORY MANAGEMENT
8.8.5.1 Ability to make better procurement decisions to fuel market
8.8.6 COMPLIANCE & DOCUMENTATION
8.8.6.1 Need to reduce manual paperwork and errors to drive market
8.8.7 HEALTHCARE WORKFLOW MANAGEMENT
8.8.7.1 Need to optimize and automate processes within healthcare facilities to drive market
8.8.8 ASSET MANAGEMENT
8.8.8.1 Ability to optimize use and allocation of resources to drive market
8.8.9 CUSTOMER RELATIONSHIP MANAGEMENT
8.8.9.1 Enhancing patient engagement and retention through AI-driven CRM systems to boost market
8.8.10 FRAUD DETECTION & RISK MANAGEMENT
8.8.10.1 Need to enhance security in healthcare systems to drive market demand
8.8.11 CYBERSECURITY
8.8.11.1 Enhancing healthcare cybersecurity with AI-driven threat detection and risk management to fuel growth
8.8.12 OTHER ADMINISTRATIVE FUNCTIONS
9 AI IN HEALTHCARE MARKET, BY APPLICATION
9.1 INTRODUCTION
9.2 CLINICAL APPLICATIONS
9.2.1 ABILITY OF AI TO IMPROVE DIAGNOSTICS, TREATMENT, AND PATIENT CARE TO DRIVE MARKET GROWTH
9.3 NON-CLINICAL APPLICATIONS
9.3.1 ABILITY OF AI TO REDUCE ADMINISTRATIVE BURDEN AND ENSURE BETTER RESOURCE ALLOCATION TO FUEL GROWTH
10 AI IN HEALTHCARE MARKET, BY DEPLOYMENT MODEL
10.1 INTRODUCTION
10.2 ON-PREMISE MODELS
10.2.1 ENHANCED DATA SECURITY AND COMPLIANCE TO PROPEL ADOPTION OF ON-PREMISE AI MODELS
10.3 CLOUD-BASED MODELS
10.3.1 SCALABILITY AND AUTOMATION OF CLOUD-BASED AI PLATFORMS TO RESHAPE HEALTHCARE DELIVERY GLOBALLY
10.4 HYBRID MODELS
10.4.1 FLEXIBILITY FOR DIVERSE APPLICATIONS TO BOOST DEMAND FOR HYBRID MODELS
11 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TOOL
11.1 INTRODUCTION
11.2 MACHINE LEARNING
11.2.1 DEEP LEARNING
11.2.1.1 Convolutional neural networks (CNN)
11.2.1.1.1 Growing demand for advance medical imaging and diagnostics to drive market growth
11.2.1.2 Recurrent neural networks (RNN)
11.2.1.2.1 Ability of RNN to enhance predictive analytics in healthcare to boost market
11.2.1.3 Generative adversarial networks (GAN)
11.2.1.3.1 Ability to transform data scarcity and privacy in AI-driven healthcare to drive demand for GAN
11.2.1.4 Graph neural networks (GNN)
11.2.1.4.1 Ability of GNN to revolutionize relational insights in healthcare analytics to fuel growth
11.2.1.5 Other deep learning tools
11.2.2 SUPERVISED LEARNING
11.2.2.1 Need to drive precision and efficiency in healthcare to support adoption of supervised learning tools
11.2.3 REINFORCEMENT LEARNING
11.2.3.1 Wide usage of reinforcement learning models for real-time and adaptive healthcare solutions to support market growth
11.2.4 UNSUPERVISED LEARNING
11.2.4.1 Ability of unsupervised learning to drive innovation in healthcare analytics to propel market
11.2.5 OTHER MACHINE LEARNING TOOLS
11.3 NATURAL LANGUAGE PROCESSING (NLP)
11.3.1 SENTIMENT ANALYSIS
11.3.1.1 Enhanced patient experience and operational insights to spur demand for sentiment analysis
11.3.2 PATTERN & IMAGE RECOGNITION
11.3.2.1 Increased precision in clinical decision-making associated with pattern & image recognition to aid market
11.3.3 AUTO CODING
11.3.3.1 Advantages such as improved medical billing and administrative efficiency to boost market
11.3.4 CLASSIFICATION & CATEGORIZATION
11.3.4.1 Ability of classification & categorization to organize healthcare data for better outcomes to propel market
11.3.5 TEXT ANALYTICS
11.3.5.1 Ability to transform unstructured data into actionable healthcare insights to fuel growth
11.3.6 SPEECH RECOGNITION
11.3.6.1 Improved speech recognition advancing real-time documentation and accessibility to drive demand
11.4 CONTEXT-AWARE COMPUTING
11.4.1 DEVICE CONTEXT
11.4.1.1 Real-time monitoring and decision-making to contribute to market growth
11.4.2 USER CONTEXT
11.4.2.1 Personalized healthcare and adaptive workflows to drive demand
11.4.3 PHYSICAL CONTEXT
11.4.3.1 Location-based services associated with physical context to generate demand
11.5 GENERATIVE AI
11.5.1 ABILITY OF GENERATIVE AI MODELS TO MIMIC DISEASE PROGRESSION TO DRIVE MARKET
11.6 COMPUTER VISION
11.6.1 ABILITY TO DETECT ABNORMALITIES SUCH AS TUMORS TO FUEL ADOPTION
11.7 IMAGE ANALYSIS
11.7.1 NEED TO STREAMLINE DOCUMENT MANAGEMENT TO PROPEL MARKET
12 AI IN HEALTHCARE MARKET, BY END USER
12.1 INTRODUCTION
12.2 HEALTHCARE PROVIDERS
12.2.1 HOSPITALS & CLINICS
12.2.1.1 Growing need to improve profitability of healthcare to drive use of AI-based healthcare solutions in hospitals
12.2.2 AMBULATORY CARE CENTERS
12.2.2.1 Supportive government norms and incentives for HCIT solutions and services to drive market growth
12.2.3 HOME HEALTHCARE AGENCIES & ASSISTED LIVING FACILITIES
12.2.3.1 Growing need for long-term home care to drive market growth
12.2.4 DIAGNOSTIC & IMAGING CENTERS
12.2.4.1 Growing burden of various chronic diseases to propel market growth
12.2.5 PHARMACIES
12.2.5.1 Advantages of workflow improvements and error reduction to drive AI-based healthcare solution adoption in pharmacies
12.2.6 OTHER HEALTHCARE PROVIDERS
12.3 HEALTHCARE PAYERS
12.3.1 PUBLIC PAYERS
12.3.1.1 Focus on developing outcome-based payment models to drive demand for payer solutions
12.3.2 PRIVATE PAYERS
12.3.2.1 Possible increases in operational efficiency to boost demand among private payers
12.4 PATIENTS
12.4.1 RISE IN USE OF AI IN MENTAL HEALTH SUPPORT APPLICATIONS THROUGH CHATBOTS AND VIRTUAL THERAPISTS TO BOOST MARKET
12.5 OTHER END USERS
13 AI IN HEALTHCARE MARKET, BY REGION
13.1 INTRODUCTION
13.2 NORTH AMERICA
13.2.1 MACROECONOMIC OUTLOOK FOR NORTH AMERICA
13.2.2 US
13.2.2.1 Rising adoption of advanced medical imaging and regulatory support to drive market
13.2.3 CANADA
13.2.3.1 Growing burden of chronic diseases and availability of research grants for AI to drive market growth
13.3 EUROPE
13.3.1 MACROECONOMIC OUTLOOK FOR EUROPE
13.3.2 GERMANY
13.3.2.1 Advanced healthcare system and government efforts to drive growth
13.3.3 UK
13.3.3.1 Government support and new AI platform emergence to boost market
13.3.4 FRANCE
13.3.4.1 Thriving AI ecosystem and strategic partnerships to boost healthcare innovation
13.3.5 ITALY
13.3.5.1 Regulatory reforms, strategic investments, and aging population to propel growth
13.3.6 SPAIN
13.3.6.1 Established network of research centers to propel market
13.3.7 REST OF EUROPE
13.4 ASIA PACIFIC
13.4.1 MACROECONOMIC OUTLOOK FOR ASIA PACIFIC
13.4.2 CHINA
13.4.2.1 Expanding AI applications in medical imaging and diagnostics to drive market growth
13.4.3 JAPAN
13.4.3.1 Strong healthcare infrastructure to drive uptake of advanced AI
13.4.4 INDIA
13.4.4.1 Favorable government initiatives for R&D investments to drive market
13.4.5 REST OF ASIA PACIFIC
13.5 LATIN AMERICA
13.5.1 MACROECONOMIC OUTLOOK FOR LATIN AMERICA
13.5.2 BRAZIL
13.5.2.1 AI initiatives to drive healthcare innovation and accessibility
13.5.3 MEXICO
13.5.3.1 AI to revolutionize healthcare delivery and outcomes, focusing on accessibility, efficiency, and innovation
13.5.4 REST OF LATIN AMERICA
13.6 MIDDLE EAST & AFRICA
13.6.1 MACROECONOMIC OUTLOOK FOR MIDDLE EAST & AFRICA
13.6.2 GCC COUNTRIES
13.6.2.1 AI-driven innovations and rise in healthcare investments to propel market growth
13.6.3 REST OF THE MIDDLE EAST & AFRICA
14 COMPETITIVE LANDSCAPE
14.1 INTRODUCTION
14.2 KEY PLAYER STRATEGY/RIGHT TO WIN
14.2.1 OVERVIEW OF STRATEGIES ADOPTED BY KEY PLAYERS IN AI IN HEALTHCARE MARKET
14.3 REVENUE ANALYSIS, 2020–2024
14.4 MARKET SHARE ANALYSIS, 2024
14.4.1 RANKING OF KEY MARKET PLAYERS
14.5 COMPANY EVALUATION MATRIX: KEY PLAYERS, 2024
14.5.1 STARS
14.5.2 EMERGING LEADERS
14.5.3 PERVASIVE PLAYERS
14.5.4 PARTICIPANTS
14.5.5 COMPANY FOOTPRINT: KEY PLAYERS, 2024
14.5.5.1 Company footprint
14.5.5.2 Regional footprint
14.5.5.3 Application footprint
14.5.5.4 Tool footprint
14.5.5.5 Function footprint
14.5.5.6 Offering footprint
14.5.5.7 Deployment footprint
14.5.5.8 End-user footprint
14.6 COMPANY EVALUATION MATRIX: STARTUPS/SMES, 2024
14.6.1 PROGRESSIVE COMPANIES
14.6.2 RESPONSIVE COMPANIES
14.6.3 DYNAMIC COMPANIES
14.6.4 STARTING BLOCKS
14.6.5 COMPETITIVE BENCHMARKING: STARTUPS/SMES, 2024
14.6.5.1 Detailed list of key startups/SMEs
14.6.5.2 Company footprint (startups/SMEs)
14.7 COMPANY VALUATION AND FINANCIAL METRICS
14.7.1 FINANCIAL METRICS
14.7.2 COMPANY VALUATION
14.8 BRAND/PRODUCT COMPARISON
14.9 COMPETITIVE SCENARIO
14.9.1 PRODUCT LAUNCHES
14.9.2 DEALS
14.9.3 OTHER DEVELOPMENTS
15 COMPANY PROFILES
15.1 KEY PLAYERS
15.1.1 KONINKLIJKE PHILIPS N.V.
15.1.1.1 Business overview
15.1.1.2 Products & services offered
15.1.1.3 Recent developments
15.1.1.3.1 Product launches
15.1.1.3.2 Deals
15.1.1.3.3 Expansions
15.1.1.3.4 Other developments
15.1.1.4 MnM view
15.1.1.4.1 Right to win
15.1.1.4.2 Strategic choices
15.1.1.4.3 Weaknesses & competitive threats
15.1.2 MICROSOFT CORPORATION
15.1.2.1 Business overview
15.1.2.2 Products & services offered
15.1.2.3 Recent developments
15.1.2.3.1 Product launches
15.1.2.3.2 Deals
15.1.2.4 MnM view
15.1.2.4.1 Right to win
15.1.2.4.2 Strategic choices
15.1.2.4.3 Weaknesses & competitive threats
15.1.3 NVIDIA CORPORATION
15.1.3.1 Business overview
15.1.3.2 Products & services offered
15.1.3.3 Recent developments
15.1.3.3.1 Product launches
15.1.3.3.2 Deals
15.1.3.4 MnM view
15.1.3.4.1 Right to win
15.1.3.4.2 Strategic choices
15.1.3.4.3 Weaknesses & competitive threats
15.1.4 SIEMENS HEALTHINEERS AG
15.1.4.1 Business overview
15.1.4.2 Products & services offered
15.1.4.3 Recent developments
15.1.4.3.1 Product launches & enhancements
15.1.4.3.2 Deals
15.1.4.3.3 Other developments
15.1.4.4 MnM view
15.1.4.4.1 Right to win
15.1.4.4.2 Strategic choices
15.1.4.4.3 Weaknesses & competitive threats
15.1.5 GE HEALTHCARE
15.1.5.1 Business overview
15.1.5.2 Products & services offered
15.1.5.3 Recent developments
15.1.5.3.1 Deals
15.1.5.3.2 Other developments
15.1.5.4 MnM view
15.1.5.4.1 Right to win
15.1.5.4.2 Strategic choices
15.1.5.4.3 Weaknesses & competitive threats
15.1.6 EPIC SYSTEMS CORPORATION
15.1.6.1 Business overview
15.1.6.2 Products & services offered
15.1.6.3 Recent developments
15.1.6.3.1 Deals
15.1.7 ORACLE CORPORATION
15.1.7.1 Business overview
15.1.7.2 Products & services offered
15.1.7.3 Recent developments
15.1.7.3.1 Product launches
15.1.7.3.2 Deals
15.1.7.3.3 Expansions
15.1.8 VERADIGM INC.
15.1.8.1 Business overview
15.1.8.2 Products & services offered
15.1.8.3 Recent developments
15.1.8.3.1 Deals
15.1.9 AMAZON WEB SERVICES, INC.
15.1.9.1 Business overview
15.1.9.2 Products & services offered
15.1.9.3 Recent developments
15.1.9.3.1 Product launches
15.1.9.3.2 Deals
15.1.9.3.3 Expansions
15.1.10 MERATIVE
15.1.10.1 Business overview
15.1.10.2 Products & services offered
15.1.10.3 Recent developments
15.1.10.3.1 Product launches
15.1.10.3.2 Deals
15.1.11 IBM
15.1.11.1 Business overview
15.1.11.2 Products & services offered
15.1.11.3 Recent developments
15.1.11.3.1 Deals
15.1.12 MEDTRONIC
15.1.12.1 Business overview
15.1.12.2 Products & services offered
15.1.12.3 Recent developments
15.1.12.3.1 Product launches
15.1.12.3.2 Deals
15.1.13 GOOGLE
15.1.13.1 Business overview
15.1.13.2 Products & services offered
15.1.13.3 Recent developments
15.1.13.3.1 Product launches
15.1.13.3.2 Deals
15.1.13.3.3 Other developments
15.1.14 SOPHIA GENETICS
15.1.14.1 Business overview
15.1.14.2 Products & services offered
15.1.14.3 Recent developments
15.1.14.3.1 Product launches
15.1.14.3.2 Deals
15.1.14.3.3 Other developments
15.1.15 JOHNSON & JOHNSON SERVICES, INC.
15.1.15.1 Business overview
15.1.15.2 Products & services offered
15.1.15.3 Recent developments
15.1.15.3.1 Other developments
15.1.15.3.2 Deals
15.1.16 TEMPUS AI, INC.
15.1.16.1 Business overview
15.1.16.2 Products & services offered
15.1.16.3 Recent developments
15.1.16.3.1 Product launches
15.1.16.3.2 Deals
15.1.17 CONCERTAI
15.1.17.1 Business overview
15.1.17.2 Products & services offered
15.1.17.3 Recent developments
15.1.17.3.1 Product launches
15.1.17.3.2 Deals
15.1.18 SOLVENTUM CORPORATION
15.1.18.1 Business overview
15.1.18.2 Products & services offered
15.1.18.3 Recent developments
15.1.18.3.1 Deals
15.1.18.3.2 Other developments
15.1.19 COGNIZANT
15.1.19.1 Business overview
15.1.19.2 Products & services offered
15.1.19.3 Recent developments
15.1.19.3.1 Product launches
15.1.19.3.2 Deals
15.1.20 VIZ.AI, INC.
15.1.20.1 Business overview
15.1.20.2 Products & services offered
15.1.20.3 Recent developments
15.1.20.3.1 Product launches
15.1.20.3.2 Deals
15.1.20.3.3 Other developments
15.1.21 RIVERAIN TECHNOLOGIES
15.1.21.1 Business overview
15.1.21.2 Products & services offered
15.1.21.3 Recent developments
15.1.21.3.1 Deals
15.2 OTHER PLAYERS
15.2.1 QVENTUS
15.2.2 QURE.AI
15.2.3 ATOMWISE INC.
15.2.4 ENLITIC
15.2.5 SEGMED
16 APPENDIX
16.1 DISCUSSION GUIDE
16.2 KNOWLEDGESTORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL
16.3 CUSTOMIZATION OPTIONS
16.4 RELATED REPORTS
16.5 AUTHOR DETAILS
1.1 STUDY OBJECTIVES
1.2 MARKET DEFINITION
1.3 STUDY SCOPE
1.3.1 MARKETS COVERED
1.3.2 INCLUSIONS & EXCLUSIONS
1.3.3 YEARS CONSIDERED
1.4 CURRENCY CONSIDERED
1.5 STAKEHOLDERS
2 RESEARCH METHODOLOGY
2.1 RESEARCH DATA
2.1.1 SECONDARY DATA
2.1.1.1 Key data from secondary sources
2.1.2 PRIMARY DATA
2.1.2.1 Key industry insights
2.2 MARKET SIZE ESTIMATION
2.3 DATA TRIANGULATION
2.4 MARKET SHARE ESTIMATION
2.5 STUDY ASSUMPTIONS
2.6 LIMITATIONS
2.6.1 METHODOLOGY-RELATED LIMITATIONS
2.6.2 SCOPE-RELATED LIMITATIONS
2.7 RISK ASSESSMENT
3 EXECUTIVE SUMMARY
4 PREMIUM INSIGHTS
4.1 AI IN HEALTHCARE MARKET OVERVIEW
4.2 ASIA PACIFIC: AI IN HEALTHCARE, BY OFFERING AND COUNTRY
4.3 AI IN HEALTHCARE MARKET: GEOGRAPHIC GROWTH OPPORTUNITIES
4.4 AI IN HEALTHCARE MARKET: REGIONAL MIX
4.5 AI IN HEALTHCARE: DEVELOPED VS. EMERGING MARKETS
5 MARKET OVERVIEW
5.1 INTRODUCTION
5.2 MARKET DYNAMICS
5.2.1 DRIVERS
5.2.1.1 Increase in need for early detection and diagnosis
5.2.1.2 Exponential growth in data volume and complexity due to surging adoption of digital technologies
5.2.1.3 Significant cost pressure on healthcare service providers with increasing prevalence of chronic diseases
5.2.1.4 Rapid proliferation of AI in healthcare sector
5.2.1.5 Growth in need for improvised healthcare services
5.2.2 RESTRAINTS
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 AI and ML technologies
5.2.3 OPPORTUNITIES
5.2.3.1 Increasing use of AI-powered solutions in elderly care
5.2.3.2 Increase in focus on developing human-aware AI systems
5.2.3.3 Strategic partnerships and collaborations among healthcare companies and AI technology providers
5.2.4 CHALLENGES
5.2.4.1 Inaccurate predictions due to scarcity of high-quality 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 TRENDS/DISRUPTIONS IMPACTING CUSTOMERS’ BUSINESSES
5.4 TECHNOLOGY ANALYSIS
5.4.1 KEY TECHNOLOGIES
5.4.1.1 Machine learning (ML) and deep learning
5.4.1.2 Natural language processing (NLP)
5.4.1.3 Computer vision (CV)
5.4.2 COMPLEMENTARY TECHNOLOGIES
5.4.2.1 Cloud computing
5.4.2.2 Digital twins
5.4.2.3 Robotic process automation (RPA)
5.4.3 ADJACENT TECHNOLOGIES
5.4.3.1 Blockchain
5.4.3.2 Augmented and virtual reality (AR/VR)
5.4.3.3 Internet of things (IoT)
5.5 INDUSTRY TRENDS
5.5.1 SHIFT TOWARD PERSONALIZED MEDICINE
5.5.2 AI IN DIAGNOSTICS AND IMAGING
5.6 PRICING ANALYSIS
5.6.1 INDICATIVE PRICING OF AI IN HEALTHCARE SOFTWARE, BY DEPLOYMENT MODEL (QUALITATIVE)
5.6.2 INDICATIVE PRICING OF AI IN HEALTHCARE SOFTWARE, BY REGION (QUALITATIVE)
5.7 VALUE CHAIN ANALYSIS
5.8 ECOSYSTEM ANALYSIS
5.9 PATENT ANALYSIS
5.9.1 INSIGHTS: JURISDICTION AND TOP APPLICANT ANALYSIS
5.10 KEY CONFERENCES & EVENTS
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 IN ACCELERATING DIAGNOSIS AND TREATMENT AT LOWER COSTS
5.11.4 GE HEALTHCARE IMPROVED PATIENT OUTCOMES BY REDUCING WORKFLOW PROCESSING TIME USING MEDICAL IMAGING DATA
5.12 REGULATORY LANDSCAPE
5.12.1 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
5.12.2 REGULATORY FRAMEWORK
5.12.2.1 North America
5.12.2.2 Europe
5.12.2.3 Asia Pacific
5.12.2.4 Middle East & Africa
5.12.2.5 Latin America
5.13 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 & BUYING CRITERIA
5.14.1 KEY STAKEHOLDERS IN BUYING PROCESS
5.14.2 BUYING CRITERIA
5.15 END-USER ANALYSIS
5.15.1 UNMET NEEDS
5.15.2 END-USER EXPECTATIONS
5.16 AI IN HEALTHCARE BUSINESS MODELS
5.16.1 SOFTWARE-AS-A-SERVICE (SAAS) MODEL
5.16.2 LICENSING MODEL
5.16.3 REVENUE SHARING/OUTCOME-BASED MODEL
5.16.4 FREEMIUM MODEL
5.16.5 AI-AS-A-SERVICE (AIAAS) MODEL
5.16.6 PARTNERSHIP/REVENUE-SHARING MODEL
5.16.7 HYBRID MODELS
5.16.8 PAY-PER-USE MODELS
5.17 INVESTMENT & FUNDING SCENARIO
5.18 IMPACT OF GENERATIVE AI ON AI IN HEALTHCARE MARKET
5.18.1 INTRODUCTION
5.18.2 MARKET POTENTIAL OF GEN AI IN HEALTHCARE
5.18.2.1 Key use cases of Gen AI
5.18.3 CASE STUDIES OF AI/GENERATIVE AI IMPLEMENTATION
5.18.3.1 Eka Care leveraging generative AI for improved health outcomes
5.18.4 INTERCONNECTED AND ADJACENT ECOSYSTEMS
5.18.4.1 AI in healthcare IT
5.18.4.2 AI in medical diagnostics
5.18.4.3 AI in oncology
5.18.4.4 AI in clinical trials
5.18.4.5 AI in drug discovery
5.18.5 USER READINESS & IMPACT ASSESSMENT
5.18.5.1 User readiness
5.18.5.1.1 Healthcare providers
5.18.5.1.2 Healthcare payers
5.18.5.1.3 Patients
5.18.5.2 Impact assessment
5.18.5.2.1 User A: Healthcare providers
5.18.5.2.1.1 Implementation
5.18.5.2.1.2 Impact
5.18.5.2.2 User B: Healthcare payers
5.18.5.2.2.1 Implementation
5.18.5.2.2.2 Impact
5.18.5.2.3 User C: Patients
5.18.5.2.3.1 Implementation
5.18.5.2.3.2 Impact
6 IMPACT OF 2025 US TARIFF – OVERVIEW
6.1 INTRODUCTION
6.2 KEY TARIFF RATES
6.3 PRICE IMPACT ANALYSIS
6.4 IMPACT ON COUNTRY/REGION
6.4.1 US
6.4.2 EUROPE
6.4.3 ASIA PACIFIC
6.5 IMPACT ON END-USE INDUSTRIES
7 AI IN HEALTHCARE MARKET, BY OFFERING
7.1 INTRODUCTION
7.2 INTEGRATED SOLUTIONS
7.2.1 RISE IN WORKFORCE CHALLENGES AND COST PRESSURES TO DRIVE ADOPTION
7.3 NICHE/POINT SOLUTIONS
7.3.1 TARGETED AI SOLUTIONS TRANSFORMING PRECISION CARE AND EFFICIENCY IN HEALTHCARE TO BOOST MARKET
7.4 AI TECHNOLOGIES
7.4.1 ABILITY OF CORE AI TECHNOLOGIES TO DRIVE PRECISION, EFFICIENCY, AND INNOVATION TO SUPPORT MARKET GROWTH
7.5 SERVICES
7.5.1 NEED TO EMPOWER NON-CLINICAL HEALTHCARE OPERATIONS TO FUEL MARKET GROWTH
8 AI IN HEALTHCARE MARKET, BY FUNCTION
8.1 INTRODUCTION
8.2 DIAGNOSIS & EARLY DETECTION
8.2.1 PRE-SCREENING
8.2.1.1 Early detection, better outcomes, and cost-effective care associated with pre-screening to boost market
8.2.2 IVD
8.2.2.1 IVD market, by technology
8.2.2.1.1 Immunoassays
8.2.2.1.1.1 Increase in focus on earlier disease detection & personalized treatment planning to drive market
8.2.2.1.2 Clinical chemistry
8.2.2.1.2.1 Increased demand for precision and personalized medicine and efficient healthcare systems to drive market
8.2.2.1.3 Molecular diagnostics
8.2.2.1.3.1 Improved disease detection, personalized treatments, and enhanced outcomes to fuel growth
8.2.2.2 IVD market, by application
8.2.2.2.1 Image analysis & interpretation
8.2.2.2.1.1 Advantages such as enhanced diagnostic accuracy, faster detection, and improved patient outcomes to support growth
8.2.2.2.2 Biomarker discovery & analysis
8.2.2.2.2.1 Ability of AI-based biomarker discovery to enhance disease detection, prognosis, and personalized treatment to drive adoption
8.2.2.2.3 Other IVD applications
8.2.3 DIAGNOSTIC IMAGING
8.2.3.1 Diagnostic imaging market, by application
8.2.3.1.1 Disease interpretation & report analysis
8.2.3.1.1.1 Ability of AI-driven disease interpretation to accelerate diagnosis, treatment, and outcomes to fuel growth
8.2.3.1.2 Image captioning & annotation
8.2.3.1.2.1 Enhanced diagnostic accuracy, efficiency, and consistency associated with image captioning & annotation to boost market
8.2.3.1.3 Image reconstruction
8.2.3.1.3.1 Improved diagnostic precision, efficiency, and image quality in healthcare to drive market
8.2.3.1.4 Other diagnostic imaging applications
8.2.3.2 Diagnostic imaging market, by modality
8.2.3.2.1 Magnetic resonance imaging (MRI)
8.2.3.2.1.1 Rise in technological advancements to drive adoption of AI in MRI
8.2.3.2.2 Computed tomography (CT)
8.2.3.2.2.1 Increase in availability of cardiac CT devices enabled with AI solutions to drive market
8.2.3.2.3 X-ray imaging
8.2.3.2.3.1 Innovative AI solutions for X-ray imaging by key players to drive market
8.2.3.2.4 Ultrasound
8.2.3.2.4.1 Increase in prevalence of and need for early detection of ovarian cancer to drive market
8.2.3.2.5 Nuclear imaging (PET & SPECT)
8.2.3.2.5.1 Critical role played by PET and SPECT in visualizing metabolic processes to contribute to growth
8.2.3.2.6 Other diagnostic imaging modalities
8.2.4 RISK ASSESSMENT & PATIENT STRATIFICATION
8.2.4.1 AI-driven risk assessment and patient stratification to enhance healthcare efficiency, outcomes, and personalized care
8.2.5 DRUG ALLERGY ALERTING
8.2.5.1 Ability of AI-enhanced drug allergy alerting to improve accuracy and patient safety to boost market
8.2.6 OTHER DIAGNOSIS & EARLY DETECTION FUNCTIONS
8.3 TREATMENT PLANNING & PERSONALIZATION
8.3.1 PERSONALIZED TREATMENT PLANNING
8.3.1.1 Precision medicine & genomic analysis
8.3.1.1.1 Advantages such as accurate disease predictions and improved outcomes to drive demand
8.3.1.2 Predictive models for treatment response
8.3.1.2.1 Ability to personalize therapies, improve outcomes, and minimize adverse effects to fuel growth
8.3.1.3 Treatment recommendation systems
8.3.1.3.1 Optimized healthcare delivery associated with treatment recommendation systems to support growth
8.3.2 PHARMACOLOGICAL THERAPY
8.3.2.1 Drug response prediction
8.3.2.1.1 Growth in use of drug response prediction to minimize adverse reactions to drive segment
8.3.2.2 Dosing & administration
8.3.2.2.1 Improved efficacy with highly personalized administration care to fuel adoption
8.3.2.3 Other pharmacological therapy functions
8.3.3 SURGICAL THERAPY
8.3.3.1 Preoperative imaging & 3D modeling
8.3.3.1.1 Enhanced surgical planning and precision to propel market
8.3.3.2 Intraoperative guidance & robotics
8.3.3.2.1 Faster recovery time post surgery to boost adoption
8.3.3.3 Postoperative analysis & recovery
8.3.3.3.1 Reduced complications with real-time monitoring to drive market
8.3.4 RADIATION THERAPY
8.3.4.1 Motion synchronization & auto contouring
8.3.4.1.1 Optimized radiation therapy with motion synchronization & auto contouring to boost market
8.3.4.2 Real-time adaptive treatment delivery
8.3.4.2.1 Reduced side effects with effective adaptive treatment to propel demand
8.3.4.3 Response assessment & quality assurance
8.3.4.3.1 Ability of AI to enhance response assessment and QA to spur growth
8.3.4.4 Other radiation therapy functions
8.3.5 BEHAVIORAL THERAPY & PSYCHOTHERAPY
8.3.5.1 Virtual counseling & chatbots
8.3.5.1.1 Ability to engage patients remotely to drive market demand
8.3.5.2 Progress monitoring & feedback
8.3.5.2.1 Increasing use of smart wearables and mobile health apps to drive market
8.3.5.3 Follow-up & long-term support
8.3.5.3.1 Widespread adoption of telemedicine platforms to drive market demand
8.3.6 IMMUNOTHERAPY
8.3.6.1 Real-time patient data monitoring
8.3.6.1.1 Need for real-time data analysis to make informed decisions to boost market growth
8.3.6.2 Response & side-effect prediction
8.3.6.2.1 Ability to reduce trial-and-error approach in medical treatments to drive segmental growth
8.3.6.3 Relapse prediction & long-term management
8.3.6.3.1 Need for proactive approach for long-term care to fuel market growth
8.3.7 OTHER TREATMENT PLANNING & PERSONALIZATION FUNCTIONS
8.4 PATIENT ENGAGEMENT & REMOTE MONITORING
8.4.1 SYMPTOM MANAGEMENT & VIRTUAL ASSISTANCE
8.4.1.1 Rising prevalence of chronic diseases to drive market growth
8.4.2 TELEHEALTH & REMOTE PATIENT MONITORING
8.4.2.1 Ability to monitor and evaluate patients remotely to boost market demand
8.4.3 HEALTHCARE ASSISTANCE ROBOTS
8.4.3.1 Advancements in robotics and artificial intelligence (AI) to boost market demand
8.4.4 MEDICATION REMINDERS
8.4.4.1 Increasing adoption of smartphone applications and IoT devices to surge market demand
8.4.5 PATIENT EDUCATION & EMPOWERMENT
8.4.5.1 Need to improve treatment adherence and enhance self-management to drive market growth
8.4.6 OTHER PATIENT ENGAGEMENT & REMOTE MONITORING FUNCTIONS
8.5 POST-TREATMENT SURVEILLANCE & SURVIVORSHIP CARE
8.5.1 RECURRENCE MONITORING
8.5.1.1 Need to predict recurrence of cancer to drive market
8.5.2 LONG-TERM OUTCOME PREDICTION
8.5.2.1 Ability to predict long-term health outcomes and enhance patient care to drive market
8.5.3 MENTAL HEALTH & SUPPORT SYSTEMS
8.5.3.1 Need to help patients cope with mental strain to drive market
8.6 PHARMACY MANAGEMENT
8.6.1 EPRESCRIBING
8.6.1.1 Need to improve safety and efficiency in prescription to drive market
8.6.2 MEDICATION MANAGEMENT
8.6.2.1 Ability to predict adverse drug reactions by patient data analysis to drive market
8.6.3 PHARMACY AUDIT & ANALYSIS
8.6.3.1 Need for effective and efficient operation of pharmacies to drive market
8.6.4 OTHER PHARMACY MANAGEMENT FUNCTIONS
8.7 DATA MANAGEMENT & ANALYTICS
8.7.1 ABILITY OF DATA MANAGEMENT & ANALYTICS TO HELP HEALTHCARE ORGANIZATIONS MAKE INFORMED DECISIONS TO BOOST MARKET
8.8 ADMINISTRATIVE
8.8.1 PATIENT REGISTRATION & SCHEDULING
8.8.1.1 Need to optimize patient registration process for smoother operations to drive market
8.8.2 PATIENT ELIGIBILITY & AUTHORIZATION
8.8.2.1 Need to reduce administrative burden to drive market
8.8.3 BILLING & CLAIMS MANAGEMENT
8.8.3.1 Need to make billing process more accurate, efficient, and transparent to drive market
8.8.4 WORKFORCE MANAGEMENT
8.8.4.1 Need for effective staff management to drive market
8.8.5 SUPPLY CHAIN & INVENTORY MANAGEMENT
8.8.5.1 Ability to make better procurement decisions to fuel market
8.8.6 COMPLIANCE & DOCUMENTATION
8.8.6.1 Need to reduce manual paperwork and errors to drive market
8.8.7 HEALTHCARE WORKFLOW MANAGEMENT
8.8.7.1 Need to optimize and automate processes within healthcare facilities to drive market
8.8.8 ASSET MANAGEMENT
8.8.8.1 Ability to optimize use and allocation of resources to drive market
8.8.9 CUSTOMER RELATIONSHIP MANAGEMENT
8.8.9.1 Enhancing patient engagement and retention through AI-driven CRM systems to boost market
8.8.10 FRAUD DETECTION & RISK MANAGEMENT
8.8.10.1 Need to enhance security in healthcare systems to drive market demand
8.8.11 CYBERSECURITY
8.8.11.1 Enhancing healthcare cybersecurity with AI-driven threat detection and risk management to fuel growth
8.8.12 OTHER ADMINISTRATIVE FUNCTIONS
9 AI IN HEALTHCARE MARKET, BY APPLICATION
9.1 INTRODUCTION
9.2 CLINICAL APPLICATIONS
9.2.1 ABILITY OF AI TO IMPROVE DIAGNOSTICS, TREATMENT, AND PATIENT CARE TO DRIVE MARKET GROWTH
9.3 NON-CLINICAL APPLICATIONS
9.3.1 ABILITY OF AI TO REDUCE ADMINISTRATIVE BURDEN AND ENSURE BETTER RESOURCE ALLOCATION TO FUEL GROWTH
10 AI IN HEALTHCARE MARKET, BY DEPLOYMENT MODEL
10.1 INTRODUCTION
10.2 ON-PREMISE MODELS
10.2.1 ENHANCED DATA SECURITY AND COMPLIANCE TO PROPEL ADOPTION OF ON-PREMISE AI MODELS
10.3 CLOUD-BASED MODELS
10.3.1 SCALABILITY AND AUTOMATION OF CLOUD-BASED AI PLATFORMS TO RESHAPE HEALTHCARE DELIVERY GLOBALLY
10.4 HYBRID MODELS
10.4.1 FLEXIBILITY FOR DIVERSE APPLICATIONS TO BOOST DEMAND FOR HYBRID MODELS
11 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TOOL
11.1 INTRODUCTION
11.2 MACHINE LEARNING
11.2.1 DEEP LEARNING
11.2.1.1 Convolutional neural networks (CNN)
11.2.1.1.1 Growing demand for advance medical imaging and diagnostics to drive market growth
11.2.1.2 Recurrent neural networks (RNN)
11.2.1.2.1 Ability of RNN to enhance predictive analytics in healthcare to boost market
11.2.1.3 Generative adversarial networks (GAN)
11.2.1.3.1 Ability to transform data scarcity and privacy in AI-driven healthcare to drive demand for GAN
11.2.1.4 Graph neural networks (GNN)
11.2.1.4.1 Ability of GNN to revolutionize relational insights in healthcare analytics to fuel growth
11.2.1.5 Other deep learning tools
11.2.2 SUPERVISED LEARNING
11.2.2.1 Need to drive precision and efficiency in healthcare to support adoption of supervised learning tools
11.2.3 REINFORCEMENT LEARNING
11.2.3.1 Wide usage of reinforcement learning models for real-time and adaptive healthcare solutions to support market growth
11.2.4 UNSUPERVISED LEARNING
11.2.4.1 Ability of unsupervised learning to drive innovation in healthcare analytics to propel market
11.2.5 OTHER MACHINE LEARNING TOOLS
11.3 NATURAL LANGUAGE PROCESSING (NLP)
11.3.1 SENTIMENT ANALYSIS
11.3.1.1 Enhanced patient experience and operational insights to spur demand for sentiment analysis
11.3.2 PATTERN & IMAGE RECOGNITION
11.3.2.1 Increased precision in clinical decision-making associated with pattern & image recognition to aid market
11.3.3 AUTO CODING
11.3.3.1 Advantages such as improved medical billing and administrative efficiency to boost market
11.3.4 CLASSIFICATION & CATEGORIZATION
11.3.4.1 Ability of classification & categorization to organize healthcare data for better outcomes to propel market
11.3.5 TEXT ANALYTICS
11.3.5.1 Ability to transform unstructured data into actionable healthcare insights to fuel growth
11.3.6 SPEECH RECOGNITION
11.3.6.1 Improved speech recognition advancing real-time documentation and accessibility to drive demand
11.4 CONTEXT-AWARE COMPUTING
11.4.1 DEVICE CONTEXT
11.4.1.1 Real-time monitoring and decision-making to contribute to market growth
11.4.2 USER CONTEXT
11.4.2.1 Personalized healthcare and adaptive workflows to drive demand
11.4.3 PHYSICAL CONTEXT
11.4.3.1 Location-based services associated with physical context to generate demand
11.5 GENERATIVE AI
11.5.1 ABILITY OF GENERATIVE AI MODELS TO MIMIC DISEASE PROGRESSION TO DRIVE MARKET
11.6 COMPUTER VISION
11.6.1 ABILITY TO DETECT ABNORMALITIES SUCH AS TUMORS TO FUEL ADOPTION
11.7 IMAGE ANALYSIS
11.7.1 NEED TO STREAMLINE DOCUMENT MANAGEMENT TO PROPEL MARKET
12 AI IN HEALTHCARE MARKET, BY END USER
12.1 INTRODUCTION
12.2 HEALTHCARE PROVIDERS
12.2.1 HOSPITALS & CLINICS
12.2.1.1 Growing need to improve profitability of healthcare to drive use of AI-based healthcare solutions in hospitals
12.2.2 AMBULATORY CARE CENTERS
12.2.2.1 Supportive government norms and incentives for HCIT solutions and services to drive market growth
12.2.3 HOME HEALTHCARE AGENCIES & ASSISTED LIVING FACILITIES
12.2.3.1 Growing need for long-term home care to drive market growth
12.2.4 DIAGNOSTIC & IMAGING CENTERS
12.2.4.1 Growing burden of various chronic diseases to propel market growth
12.2.5 PHARMACIES
12.2.5.1 Advantages of workflow improvements and error reduction to drive AI-based healthcare solution adoption in pharmacies
12.2.6 OTHER HEALTHCARE PROVIDERS
12.3 HEALTHCARE PAYERS
12.3.1 PUBLIC PAYERS
12.3.1.1 Focus on developing outcome-based payment models to drive demand for payer solutions
12.3.2 PRIVATE PAYERS
12.3.2.1 Possible increases in operational efficiency to boost demand among private payers
12.4 PATIENTS
12.4.1 RISE IN USE OF AI IN MENTAL HEALTH SUPPORT APPLICATIONS THROUGH CHATBOTS AND VIRTUAL THERAPISTS TO BOOST MARKET
12.5 OTHER END USERS
13 AI IN HEALTHCARE MARKET, BY REGION
13.1 INTRODUCTION
13.2 NORTH AMERICA
13.2.1 MACROECONOMIC OUTLOOK FOR NORTH AMERICA
13.2.2 US
13.2.2.1 Rising adoption of advanced medical imaging and regulatory support to drive market
13.2.3 CANADA
13.2.3.1 Growing burden of chronic diseases and availability of research grants for AI to drive market growth
13.3 EUROPE
13.3.1 MACROECONOMIC OUTLOOK FOR EUROPE
13.3.2 GERMANY
13.3.2.1 Advanced healthcare system and government efforts to drive growth
13.3.3 UK
13.3.3.1 Government support and new AI platform emergence to boost market
13.3.4 FRANCE
13.3.4.1 Thriving AI ecosystem and strategic partnerships to boost healthcare innovation
13.3.5 ITALY
13.3.5.1 Regulatory reforms, strategic investments, and aging population to propel growth
13.3.6 SPAIN
13.3.6.1 Established network of research centers to propel market
13.3.7 REST OF EUROPE
13.4 ASIA PACIFIC
13.4.1 MACROECONOMIC OUTLOOK FOR ASIA PACIFIC
13.4.2 CHINA
13.4.2.1 Expanding AI applications in medical imaging and diagnostics to drive market growth
13.4.3 JAPAN
13.4.3.1 Strong healthcare infrastructure to drive uptake of advanced AI
13.4.4 INDIA
13.4.4.1 Favorable government initiatives for R&D investments to drive market
13.4.5 REST OF ASIA PACIFIC
13.5 LATIN AMERICA
13.5.1 MACROECONOMIC OUTLOOK FOR LATIN AMERICA
13.5.2 BRAZIL
13.5.2.1 AI initiatives to drive healthcare innovation and accessibility
13.5.3 MEXICO
13.5.3.1 AI to revolutionize healthcare delivery and outcomes, focusing on accessibility, efficiency, and innovation
13.5.4 REST OF LATIN AMERICA
13.6 MIDDLE EAST & AFRICA
13.6.1 MACROECONOMIC OUTLOOK FOR MIDDLE EAST & AFRICA
13.6.2 GCC COUNTRIES
13.6.2.1 AI-driven innovations and rise in healthcare investments to propel market growth
13.6.3 REST OF THE MIDDLE EAST & AFRICA
14 COMPETITIVE LANDSCAPE
14.1 INTRODUCTION
14.2 KEY PLAYER STRATEGY/RIGHT TO WIN
14.2.1 OVERVIEW OF STRATEGIES ADOPTED BY KEY PLAYERS IN AI IN HEALTHCARE MARKET
14.3 REVENUE ANALYSIS, 2020–2024
14.4 MARKET SHARE ANALYSIS, 2024
14.4.1 RANKING OF KEY MARKET PLAYERS
14.5 COMPANY EVALUATION MATRIX: KEY PLAYERS, 2024
14.5.1 STARS
14.5.2 EMERGING LEADERS
14.5.3 PERVASIVE PLAYERS
14.5.4 PARTICIPANTS
14.5.5 COMPANY FOOTPRINT: KEY PLAYERS, 2024
14.5.5.1 Company footprint
14.5.5.2 Regional footprint
14.5.5.3 Application footprint
14.5.5.4 Tool footprint
14.5.5.5 Function footprint
14.5.5.6 Offering footprint
14.5.5.7 Deployment footprint
14.5.5.8 End-user footprint
14.6 COMPANY EVALUATION MATRIX: STARTUPS/SMES, 2024
14.6.1 PROGRESSIVE COMPANIES
14.6.2 RESPONSIVE COMPANIES
14.6.3 DYNAMIC COMPANIES
14.6.4 STARTING BLOCKS
14.6.5 COMPETITIVE BENCHMARKING: STARTUPS/SMES, 2024
14.6.5.1 Detailed list of key startups/SMEs
14.6.5.2 Company footprint (startups/SMEs)
14.7 COMPANY VALUATION AND FINANCIAL METRICS
14.7.1 FINANCIAL METRICS
14.7.2 COMPANY VALUATION
14.8 BRAND/PRODUCT COMPARISON
14.9 COMPETITIVE SCENARIO
14.9.1 PRODUCT LAUNCHES
14.9.2 DEALS
14.9.3 OTHER DEVELOPMENTS
15 COMPANY PROFILES
15.1 KEY PLAYERS
15.1.1 KONINKLIJKE PHILIPS N.V.
15.1.1.1 Business overview
15.1.1.2 Products & services offered
15.1.1.3 Recent developments
15.1.1.3.1 Product launches
15.1.1.3.2 Deals
15.1.1.3.3 Expansions
15.1.1.3.4 Other developments
15.1.1.4 MnM view
15.1.1.4.1 Right to win
15.1.1.4.2 Strategic choices
15.1.1.4.3 Weaknesses & competitive threats
15.1.2 MICROSOFT CORPORATION
15.1.2.1 Business overview
15.1.2.2 Products & services offered
15.1.2.3 Recent developments
15.1.2.3.1 Product launches
15.1.2.3.2 Deals
15.1.2.4 MnM view
15.1.2.4.1 Right to win
15.1.2.4.2 Strategic choices
15.1.2.4.3 Weaknesses & competitive threats
15.1.3 NVIDIA CORPORATION
15.1.3.1 Business overview
15.1.3.2 Products & services offered
15.1.3.3 Recent developments
15.1.3.3.1 Product launches
15.1.3.3.2 Deals
15.1.3.4 MnM view
15.1.3.4.1 Right to win
15.1.3.4.2 Strategic choices
15.1.3.4.3 Weaknesses & competitive threats
15.1.4 SIEMENS HEALTHINEERS AG
15.1.4.1 Business overview
15.1.4.2 Products & services offered
15.1.4.3 Recent developments
15.1.4.3.1 Product launches & enhancements
15.1.4.3.2 Deals
15.1.4.3.3 Other developments
15.1.4.4 MnM view
15.1.4.4.1 Right to win
15.1.4.4.2 Strategic choices
15.1.4.4.3 Weaknesses & competitive threats
15.1.5 GE HEALTHCARE
15.1.5.1 Business overview
15.1.5.2 Products & services offered
15.1.5.3 Recent developments
15.1.5.3.1 Deals
15.1.5.3.2 Other developments
15.1.5.4 MnM view
15.1.5.4.1 Right to win
15.1.5.4.2 Strategic choices
15.1.5.4.3 Weaknesses & competitive threats
15.1.6 EPIC SYSTEMS CORPORATION
15.1.6.1 Business overview
15.1.6.2 Products & services offered
15.1.6.3 Recent developments
15.1.6.3.1 Deals
15.1.7 ORACLE CORPORATION
15.1.7.1 Business overview
15.1.7.2 Products & services offered
15.1.7.3 Recent developments
15.1.7.3.1 Product launches
15.1.7.3.2 Deals
15.1.7.3.3 Expansions
15.1.8 VERADIGM INC.
15.1.8.1 Business overview
15.1.8.2 Products & services offered
15.1.8.3 Recent developments
15.1.8.3.1 Deals
15.1.9 AMAZON WEB SERVICES, INC.
15.1.9.1 Business overview
15.1.9.2 Products & services offered
15.1.9.3 Recent developments
15.1.9.3.1 Product launches
15.1.9.3.2 Deals
15.1.9.3.3 Expansions
15.1.10 MERATIVE
15.1.10.1 Business overview
15.1.10.2 Products & services offered
15.1.10.3 Recent developments
15.1.10.3.1 Product launches
15.1.10.3.2 Deals
15.1.11 IBM
15.1.11.1 Business overview
15.1.11.2 Products & services offered
15.1.11.3 Recent developments
15.1.11.3.1 Deals
15.1.12 MEDTRONIC
15.1.12.1 Business overview
15.1.12.2 Products & services offered
15.1.12.3 Recent developments
15.1.12.3.1 Product launches
15.1.12.3.2 Deals
15.1.13 GOOGLE
15.1.13.1 Business overview
15.1.13.2 Products & services offered
15.1.13.3 Recent developments
15.1.13.3.1 Product launches
15.1.13.3.2 Deals
15.1.13.3.3 Other developments
15.1.14 SOPHIA GENETICS
15.1.14.1 Business overview
15.1.14.2 Products & services offered
15.1.14.3 Recent developments
15.1.14.3.1 Product launches
15.1.14.3.2 Deals
15.1.14.3.3 Other developments
15.1.15 JOHNSON & JOHNSON SERVICES, INC.
15.1.15.1 Business overview
15.1.15.2 Products & services offered
15.1.15.3 Recent developments
15.1.15.3.1 Other developments
15.1.15.3.2 Deals
15.1.16 TEMPUS AI, INC.
15.1.16.1 Business overview
15.1.16.2 Products & services offered
15.1.16.3 Recent developments
15.1.16.3.1 Product launches
15.1.16.3.2 Deals
15.1.17 CONCERTAI
15.1.17.1 Business overview
15.1.17.2 Products & services offered
15.1.17.3 Recent developments
15.1.17.3.1 Product launches
15.1.17.3.2 Deals
15.1.18 SOLVENTUM CORPORATION
15.1.18.1 Business overview
15.1.18.2 Products & services offered
15.1.18.3 Recent developments
15.1.18.3.1 Deals
15.1.18.3.2 Other developments
15.1.19 COGNIZANT
15.1.19.1 Business overview
15.1.19.2 Products & services offered
15.1.19.3 Recent developments
15.1.19.3.1 Product launches
15.1.19.3.2 Deals
15.1.20 VIZ.AI, INC.
15.1.20.1 Business overview
15.1.20.2 Products & services offered
15.1.20.3 Recent developments
15.1.20.3.1 Product launches
15.1.20.3.2 Deals
15.1.20.3.3 Other developments
15.1.21 RIVERAIN TECHNOLOGIES
15.1.21.1 Business overview
15.1.21.2 Products & services offered
15.1.21.3 Recent developments
15.1.21.3.1 Deals
15.2 OTHER PLAYERS
15.2.1 QVENTUS
15.2.2 QURE.AI
15.2.3 ATOMWISE INC.
15.2.4 ENLITIC
15.2.5 SEGMED
16 APPENDIX
16.1 DISCUSSION GUIDE
16.2 KNOWLEDGESTORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL
16.3 CUSTOMIZATION OPTIONS
16.4 RELATED REPORTS
16.5 AUTHOR DETAILS
