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
세계 헬스케어 분야 인공지능(AI) 시장 규모는 2025년 216억 6,000만 달러에서 2030년 1,106억 1,000만 달러에 달할 것으로 예상되며, 예측 기간 동안 38.6%의 CAGR을 기록할 것으로 예상됩니다.
이 시장은 민관 조직의 투자 및 자금 조달 증가, 헬스케어 산업에서 AI의 급속한 보급, 인간을 의식한 AI 시스템 개발에 대한 관심 증가로 인해 성장이 예상됩니다. 이 시장은 의료 인력 대비 환자 수의 불균형한 비율로 인해 더 나은 서비스에 대한 수요가 증가함에 따라 성장하고 있습니다. 그러나 신흥국의 경우 IT 인프라가 부족하고 AI 기반 헬스케어 솔루션 도입에 소극적인 점이 시장 성장의 걸림돌로 작용할 것으로 추정됩니다.
| 조사 범위 |
| 조사 대상 연도 |
2024-2030년 |
| 기준 연도 |
2024년 |
| 예측 기간 |
2024-2030년 |
| 검토 단위 |
금액(10억 달러) |
| 부문 |
제공별, 기능별, 용도별, 전개 방식별, 툴별, 최종사용자별, 지역별 |
| 대상 지역 |
북미, 유럽, 아시아태평양, 라틴아메리카, 중동 및 아프리카 |
헬스케어 AI 시장은 온프레미스, 클라우드 기반, 하이브리드 모델의 세 가지 전개 모델로 분류됩니다. 클라우드 기반 모델은 확장성, 비용 효율성, 접근 용이성으로 인해 가장 큰 점유율을 차지하고 있습니다. 클라우드 기반 모델은 실시간 데이터 처리와 협업을 용이하게 합니다. 원활한 통합, 안전한 데이터 저장, 빠른 배포가 가능하기 때문에 특히 의료 서비스 제공자 및 지불자에게 적합합니다. 고품질 표준을 유지하면서 보다 신속하고 신뢰할 수 있는 의료 서비스를 제공합니다. 클라우드 기반 AI 솔루션은 비용 효율성, 확장성 및 원격 액세스 지원으로 인해 점점 더 많은 인기를 얻고 있습니다. 이러한 솔루션은 원활한 통합과 실시간 분석을 가능하게 합니다. 원격의료의 확산과 헬스케어 IT 인프라의 발전은 클라우드 기반 모델에 대한 수요를 더욱 증가시키고 있습니다.
최종사용자별로 헬스케어 인공지능(AI) 시장은 병원 및 클리닉, 외래 수술 센터, 재택의료기관 및 복지시설, 진단 및 영상 진단센터, 약국, 기타 헬스케어 제공업체로 구분됩니다. 헬스케어 인공지능(AI) 시장에서는 병원 및 클리닉 분야가 가장 큰 비중을 차지하고 있습니다. AI 기반 헬스케어 솔루션은 병원 및 클리닉에서 진단 정확도를 높이고, 업무를 간소화하며, 병원 및 클리닉의 관리를 자동화하고, 관리 업무를 자동화하며, 환자 맞춤형 치료를 가능하게 합니다. AI는 관리 업무를 자동화하고, 환자 결과를 예측하며, 실시간 데이터 분석을 통해 신속한 의사결정을 내릴 수 있도록 돕고, 원격 모니터링을 지원하고, 자원을 최적화하고, 불필요한 치료를 최소화하여 비용을 절감할 수 있습니다. 환자 참여를 향상시키고, 부정행위를 감지하여 보안을 강화하며, 의료 서비스를 보다 효율적이고 접근성이 높으며, 비용 효율적으로 만듭니다.
헬스케어 분야 인공지능(AI) 시장은 북미, 유럽, 아시아태평양, 라틴아메리카, 중동 및 아프리카로 나뉘어져 있습니다. 아시아태평양은 예측 기간 동안 가장 높은 성장률을 기록할 것으로 예상됩니다. 아시아태평양은 인구통계학적 변화, 기술 발전, 혁신에 대한 투자 증가로 인해 헬스케어 분야에서 AI 기술 채택이 크게 증가하고 있습니다. 아시아태평양의 노인 인구 증가는 중요한 요인으로, 65세 이상 노인의 비율이 크게 증가하고 있습니다. 유엔의 '세계 인구 고령화 2020' 보고서에 따르면 이 연령대의 세계 인구는 2020년 7억 2,700만 명에서 2050년 15억 명으로 두 배로 증가할 것으로 예상되며, 그 중 동아시아와 동남아시아가 큰 비중을 차지할 것으로 예상됩니다.
세계의 헬스케어 분야 인공지능(AI) 시장에 대해 조사했으며, 제공별, 기능별, 용도별, 전개 방식별, 도구별, 최종사용자별, 지역별 동향, 시장 진입 기업 프로파일 등의 정보를 정리하여 전해드립니다.
목차
제1장 소개
제2장 조사 방법
제3장 주요 요약
제4장 주요 인사이트
제5장 시장 개요
- 소개
- 시장 역학
- 고객의 비즈니스에 영향을 미치는 동향/혼란
- 기술 분석
- 업계 동향
- 가격 분석
- 밸류체인 분석
- 생태계 분석
- 특허 분석
- 주요 회의와 이벤트
- 사례 연구 분석
- 규제 상황
- Porter's Five Forces 분석
- 주요 이해관계자와 구입 기준
- 최종사용자 분석
- 헬스케어 비즈니스 모델의 AI
- 투자와 자금 조달 시나리오
- 생성형 AI가 헬스케어 시장의 AI에 미치는 영향
제6장 2025년 미국 관세의 영향 - 개요
- 소개
- 주요 관세율
- 가격 영향 분석
- 국가/지역에 대한 영향
- 최종 이용 산업에 대한 영향
제7장 헬스케어 분야 인공지능(AI) 시장, 제공별
- 소개
- 통합 솔루션
- 틈새/포인트 솔루션
- AI 테크놀러지
- 서비스
제8장 헬스케어 분야 인공지능(AI) 시장, 기능별
- 소개
- 진단과 조기 발견
- 치료 계획과 개인화
- 환자 참여와 원격 모니터링
- 치료 후 감시와 생존자 케어
- 약국 경영
- 데이터 관리와 분석
- 행정
제9장 헬스케어 분야 인공지능(AI) 시장, 용도별
제10장 헬스케어 분야 인공지능(AI) 시장, 전개 방식별
- 소개
- 온프레미스 모델
- 클라우드 기반 모델
- 하이브리드 모델
제11장 헬스케어 분야 인공지능(AI) 시장, 툴별
- 소개
- 머신러닝
- 자연어 처리(NLP)
- 상황인식 컴퓨팅
- 생성형 AI
- 컴퓨터 비전
- 이미지 분석
제12장 헬스케어 분야 인공지능(AI) 시장, 최종사용자별
제13장 헬스케어 분야 인공지능(AI) 시장, 지역별
- 소개
- 북미
- 유럽
- 유럽의 거시경제 전망
- 독일
- 영국
- 프랑스
- 이탈리아
- 스페인
- 기타
- 아시아태평양
- 아시아태평양의 거시경제 전망
- 중국
- 일본
- 인도
- 기타
- 라틴아메리카
- 라틴아메리카의 거시경제 전망
- 브라질
- 멕시코
- 기타
- 중동 및 아프리카
- 중동 및 아프리카의 거시경제 전망
- GCC 국가
- 기타
제14장 경쟁 구도
- 소개
- 주요 진출 기업 전략/강함
- 매출 분석, 2020-2024년
- 시장 점유율 분석, 2024년
- 기업 평가 매트릭스 : 주요 진출 기업, 2024년
- 기업 평가 매트릭스 : 스타트업/중소기업, 2024년
- 기업 평가와 재무 지표
- 브랜드/제품 비교
- 경쟁 시나리오
제15장 기업 개요
- 주요 진출 기업
- KONINKLIJKE PHILIPS N.V.
- MICROSOFT CORPORATION
- NVIDIA CORPORATION
- SIEMENS HEALTHINEERS AG
- GE HEALTHCARE
- EPIC SYSTEMS CORPORATION
- ORACLE CORPORATION
- VERADIGM INC.
- AMAZON WEB SERVICES, INC.
- MERATIVE
- IBM
- MEDTRONIC
- GOOGLE
- SOPHIA GENETICS
- JOHNSON & JOHNSON SERVICES, INC.
- TEMPUS AI, INC.
- CONCERTAI
- SOLVENTUM CORPORATION
- COGNIZANT
- VIZ.AI, INC.
- RIVERAIN TECHNOLOGIES
- 기타 기업
- QVENTUS
- QURE.AI
- ATOMWISE INC.
- ENLITIC
- SEGMED
제16장 부록
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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.
| Scope of the Report |
|---|
| Years Considered for the Study | 2024-2030 |
| Base Year | 2024 |
| Forecast Period | 2024-2030 |
| Units Considered | Value (USD billion) |
| Segments | Offering, Function, Application, Deployment, Tools, End User |
| Regions covered | North America, Europe, Asia Pacific, Latin America, and Middle East & Africa |
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%
Key Players
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)
TABLE OF CONTENTS
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
- 15.1.7 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
- 15.1.9 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
- 15.1.12 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
- 15.2 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
