세계의 의료용 AI 시장 : 제공, 기술, 용도, 최종사용자, 지역별 - 예측(-2029년)
Artificial Intelligence in Healthcare Market by Offering, Technology, Application, End User & Region - Global Forecast to 2029
상품코드:1422236
리서치사:MarketsandMarkets
발행일:2024년 02월
페이지 정보:영문 360 Pages
라이선스 & 가격 (부가세 별도)
ㅁ Add-on 가능: 고객의 요청에 따라 일정한 범위 내에서 Customization이 가능합니다. 자세한 사항은 문의해 주시기 바랍니다.
한글목차
세계 의료용 AI 시장 규모는 2024년 209억 달러에서 2029년 1,484억 달러에 달할 것으로 예상되며, 2024-2029년 연평균 48.1% 성장할 것으로 전망됩니다.
의료용 AI에 대한 높은 관심, 고령자 치료에서 AI 기반 도구의 미래 가능성, 인간을 인식하는 AI 시스템 개발 동향 증가, 신약 개발, 게놈, 이미징 및 진단 분야의 AI 기술 가속화가 의료용 AI 시장의 성장을 가속할 것입니다.
조사 범위
조사 대상년도
2020년-2029년
기준년도
2023년
예측 기간
2024년-2029년
단위
10억 달러
부문
제품별, 기술별, 용도별, 최종사용자별, 지역별
대상 지역
북미, 유럽, 아시아태평양 및 기타 지역
"소프트웨어 시장이 예측 기간 동안 가장 큰 점유율을 차지할 것으로 예상됩니다."
소프트웨어 부문은 AI 플랫폼과 AI 솔루션으로 분류됩니다. 소프트웨어는 의료용 AI의 통합과 기능성을 촉진하는 기반 요소로, AI의 두뇌 역할을 하며 자연어 처리 및 딥러닝과 같은 복잡한 머신러닝 알고리즘을 구현할 수 있도록 하는 촉매제 역할을 합니다. 소프트웨어가 촉진하는 효율적인 데이터 수집 및 관리를 통해 이러한 알고리즘은 AI 시스템이 광범위한 의료 데이터 세트를 분석하여 가치 있는 통찰력을 도출할 수 있도록 지원합니다. 실제 사용에서 소프트웨어는 진단 도구, 치료 개인화 및 가상 비서에서 매우 중요한 역할을 수행하여 질병 발견, 치료 계획 및 환자 참여의 정확성을 높입니다. 또한, 소프트웨어는 관리 자동화 및 예측 분석을 통해 의료 업무를 최적화하여 효율성을 개선하고 적극적인 환자 치료에 기여합니다. 의료용 AI의 근간이 되는 소프트웨어는 환자 치료 강화, 조기 진단, 개인화된 치료를 위한 혁신적인 솔루션을 제공함으로써 의료 환경의 판도를 바꾸고 있습니다.
"예측 기간 동안 자연어 처리 부문 시장이 두 번째 점유율을 차지할 것으로 예상됩니다."
임상 및 연구 커뮤니티는 전자 건강 보고서, 임상 노트, 병리 보고서와 같은 비구조화, 반 구조화 된 텍스트 문서의 효율적인 관리 및 개발을 위해 의료 분야에서 NLP를 널리 사용하고 있습니다. 이 알고리즘은 내러티브 텍스트 임상 문서에서 건강 문제를 추출하여 환자의 전자 문제 목록에 포함시켜 정확하게 해석하는 것을 제안하며, NLP는 문서 전처리, 건강 문제 감지, 부정 감지, 문서 후 처리의 네 가지 단계를 포함한다, 챗봇이 대면 진료에서 의사가 하는 것과 같은 질문을 할 수 있는 앱과 NLP 알고리즘을 개발했습니다. 이 앱은 공식적인 진단 개요를 설명하는 대신 음성 및 언어 처리를 사용하여 증상을 추출하고 프로파일 정보를 의사에게 전달하며, NLP는 임상 데이터를보다 정확하게 구조화하고 해석하기 위해 의료 기관에서 큰 수요가 있습니다. 또한, 커넥티드 기기의 사용 증가와 더불어 방대한 양의 환자 데이터가 이 시장의 성장을 가속화하고 있습니다.
"환자 데이터 위험 분석 부문이 예측 기간 동안 큰 시장 점유율을 차지할 것입니다."
의료 분야에서 머신러닝(ML)과 자연어 처리(NLP)의 결합은 환자의 건강에 대한 예측적 고려에 큰 진전을 가져왔습니다. 다양한 데이터 소스를 활용하는 ML 모델은 의료 기록, 임상 검사, 인구 통계, 사회적 결정 요인을 분석하여 특정 질병에 걸릴 위험이 있는 환자를 식별하고, NLP 알고리즘은 임상 기록에서 통찰력을 추출하여 질병의 초기 징후를 발견합니다. ML은 잠재적 악화를 예측하고 적극적인 개입을 가능하게 하며, NLP는 원격 모니터링을 위해 실시간 데이터를 해석하는 등 이 시너지를 통해 치료 효과와 생활습관 등의 요소를 고려한 개인화된 치료 계획을 수립할 수 있습니다. 그 장점은 환자 결과 개선, 비용 절감, 의료 의사결정 강화 등의 이점이 있습니다. 그러나 데이터 프라이버시, 알고리즘의 편향성, 투명성의 필요성 등의 문제는 의료 분야에서 윤리적이고 책임감 있는 AI 도입의 중요성을 보여줍니다.
본 보고서에서는 세계 의료용 AI 시장에 대해 조사 분석했으며, 주요 촉진요인과 억제요인, 경쟁 구도, 향후 동향 등의 정보를 전해드립니다.
목차
제1장 서론
제2장 조사 방법
제3장 주요 요약
제4장 프리미엄 인사이트
의료용 AI 시장 기업에 있어서의 매력적인 기회
의료용 AI 시장 : 제공 제품별
의료용 AI 시장 : 기술별
의료용 AI 시장 : 최종사용자별
의료용 AI 시장 : 용도별
의료용 AI 시장 : 국가별
제5장 시장 개요
서론
시장 역학
성장 촉진요인
성장 억제요인
기회
과제
고객의 비즈니스에 영향을 미치는 동향/혼란
가격 분석
주요 기업이 제공하는 컴포넌트 평균 판매 가격(ASP) 동향(2020년-2029년)
프로세서 컴포넌트 평균 판매 가격(ASP) 동향 : 지역별(2020년-2029년)
밸류체인 분석
에코시스템 매핑
기술 분석
클라우드 컴퓨팅
클라우드 GPU
생성형 AI
클라우드 기반 PACS
멀티클라우드
특허 분석
무역 분석
주요 컨퍼런스 및 이벤트(2024년-2025년)
사례 연구 분석
관세, 기준 및 규제 상황
규제기관, 정부기관, 기타 조직
기준
정부 규제
Porter의 Five Forces 분석
주요 이해관계자와 구입 기준
제6장 의료용 AI 시장 : 제공별
서론
하드웨어
프로세서
메모리
네트워크
소프트웨어
AI 솔루션
AI 플랫폼
서비스
전개 및 통합
지원 및 유지관리
제7장 의료용 AI 시장 : 기술별
서론
머신러닝
딥러닝
지도 학습
강화 학습
비지도 학습
기타
자연언어처리
IVR
OCR
패턴 및 영상 인식
자동 코딩
분류
텍스트 분석
음성 분석
상황인식 컴퓨팅
디바이스 콘텍스트
사용자 상황
피지컬 상황
컴퓨터 비전
제8장 의료용 AI 시장 : 용도별
서론
환자 데이터 및 리스크 분석
입원 환자 케어 및 병원 관리
의료 영상 및 진단
라이프스타일 관리 및 원격 환자 모니터링
가상 비서
Drug Discovery
연구
의료 지원 로봇
정밀의료
응급실 및 수술
웨어러블
정신건강
사이버 보안
제9장 의료용 AI 시장 : 최종사용자별
서론
병원 및 의료 제공업체
환자
제약 기업 및 바이오테크놀러지 기업
의료보험자
기타
제10장 의료용 AI 시장 : 지역별
서론
북미
북미 불황의 영향
미국
캐나다
멕시코
유럽
유럽 불황의 영향
독일
영국
프랑스
이탈리아
스페인
기타 유럽
아시아태평양
아시아태평양 불황의 영향
중국
일본
한국
인도
기타 아시아태평양
기타 지역
기타 지역 불황의 영향
남미
GCC
기타 중동 및 아프리카
제11장 경쟁 구도
개요
주요 기업이 채택한 전략(2020년-2023년)
매출 분석(2019년-2023년)
시장 점유율 분석(2023년)
기업 평가 매트릭스(2023년)
스타트업/중소기업(SME) 평가 매트릭스(2023년)
경쟁 시나리오와 동향
제12장 기업 개요
주요 기업
KONINKLIJKE PHILIPS N.V.
MICROSOFT
SIEMENS HEALTHINEERS AG
INTEL CORPORATION
NVIDIA CORPORATION
GOOGLE INC.
GE HEALTHCARE
MEDTRONIC
MICRON TECHNOLOGY, INC.
AMAZON.COM, INC.
ORACLE
JOHNSON & JOHNSON SERVICES, INC.
기타 기업
MERATIVE
GENERAL VISION INC.
CLOUDMEDX
ONCORA MEDICAL
ENLITIC, INC.
LUNIT INC.
QURE.AI
TEMPUS
COTA
FDNA INC.
RECURSION
ATOMWISE INC.
VIRGIN PULSE
BABYLON HEALTHCARE SERVICES LTD
MDLIVE(EVERNORTH GROUP)
STRYKER
QVENTUS
SWEETCH
SIRONA MEDICAL, INC.
GINGER
BIOBEAT
제13장 부록
LSH
영문 목차
영문목차
The AI in Healthcare market is projected to grow from USD 20.9 billion in 2024 and is projected to reach USD 148.4 billion by 2029; it is expected to grow at a CAGR of 48.1% from 2024 to 2029. Strong focus on AI in Healthcare rising potential of AI-based tools for elderly care, increasing trend towards developing human-aware AI systems, and acceleration of AI technology in drug discovery, genomics, and imaging & diagnostics to fuel the growth of AI in Healthcare market.
Scope of the Report
Years Considered for the Study
2020-2029
Base Year
2023
Forecast Period
2024-2029
Units Considered
Value (USD Billion)
Segments
By Offering, Technology, Application, End User & Region
Regions covered
North America, Europe, Asia Pacific and RoW
"Market for Software to hold the largest share during the forecast period."
The software segment is categorized into AI Platform and AI Solution. Software is the foundational element driving the integration and functionality of AI in healthcare. Acting as the catalyst for the AI brain, it enables the implementation of intricate machine learning algorithms such as natural language processing and deep learning. These algorithms, supported by efficient data ingestion and management facilitated by software, empower AI systems to analyze extensive medical datasets and derive valuable insights. In practical application, software plays a pivotal role in diagnostic tools, treatment personalization, and virtual assistants, enhancing the accuracy in disease detection, treatment planning, and patient engagement. Additionally, the software optimizes healthcare operations through administrative automation and predictive analytics, contributing to improved efficiency and proactive patient care. As the backbone of AI in healthcare, software transforms the landscape by offering innovative solutions for enhanced patient care, early diagnosis, and personalized treatment.
"Market for Natural Language Processing segment is projected to hold for second-largest share during the forecast timeline."
The clinical and research community widely uses NLP in healthcare for efficient managing and development of unstructured and semi-structured textual documents, including electronic health reports, clinical notes, and pathology reports. The algorithm extracts health problems from narrative text clinical documents and proposes inclusion in a patient's electronic problem list to interpret accurately. NLP involves four steps: document pre-processing, health problem detection, negation detection, and document post-processing. Babylon Health (UK) has developed an app and NLP algorithms to help a chatbot ask the same questions a doctor would ask during in-person examination. The app does not outline an official diagnosis; rather it uses speech and language processing to extract symptoms and forward the profile information to a doctor. NLP is experiencing significant demand from healthcare institutions for structuring and interpretation of clinical data more accurately. Moreover, the rising usage of connected devices, along with the massive volume of patients' data, accelerates the growth of this market.
"Market for patient data & risk analysis segment holds for major market share during the forecast period."
The convergence of machine learning (ML) and natural language processing (NLP) in healthcare offers significant advancements in predictive insights for patient health. Utilizing diverse data sources, ML models analyze medical records, lab tests, demographics, and social determinants to identify patients at risk of specific diseases, while NLP algorithms extract insights from clinical notes to spot early signs of illness. This synergy enables personalized treatment plans, considering factors like treatment response and lifestyle. ML predicts potential exacerbations, allowing proactive interventions, and NLP interprets real-time data for remote monitoring. The benefits include improved patient outcomes, reduced costs, and enhanced medical decision-making. However, challenges like data privacy, algorithmic bias, and the need for transparency underscore the importance of ethical and responsible AI implementation in healthcare.
"North America is expected to have the largest market share during the forecast period."
The healthcare sector in North America is witnessing an influx of new entrants into the Artificial Intelligence (AI) landscape, driven by cross-industry involvement and a substantial rise in venture capital investments. An example is Navina (US), a startup dedicated to an AI-driven primary care platform, securing a substantial USD 44 million in its series B funding round in October 2022. These investments propel Navina's AI and Machine Learning (ML) technology advancements. Another illustration is Tempus (US), specializing in AI-based precision medicine solutions, securing a notable USD 1.3 billion from 11 investors, including Ares Management and Google, in the same month.
Extensive primary interviews were conducted with key industry experts in the AI in Healthcare market space to determine and verify the market size for various segments and subsegments gathered through secondary research. The break-up of primary participants for the report has been shown below:
The break-up of the profile of primary participants in the AI in Healthcare market:
By Company Type: Tier 1 - 50%, Tier 2 - 30%, and Tier 3 - 20%
By Designation: C Level - 60%, Director Level - 30%, Others-10%
By Region: North America - 40%, Europe - 20%, Asia Pacific - 30%, ROW- 10%
The report profiles key players in the AI in Healthcare market with their respective market ranking analysis. Prominent players profiled in this report are Koninklijke Philips N.V. (Netherlands), Microsoft (US), Siemens Healthineers AG (Germany), Intel Corporation (US), NVIDIA Corporation (US), Google Inc. (US), GE HealthCare Technologies Inc. (US), Oracle (US), and Johnson & Johnson Services, Inc. (US) among others.
Apart from this, Merative (US), General Vision, Inc., (US), CloudMedx (US), Oncora Medical (US), Enlitic (US), Lunit Inc., (South Korea), Qure.ai (India), Tempus (US), COTA (US), FDNA INC. (US), Recursion (US), Atomwise (US), Virgin Pulse (US), Babylon Health (UK), MDLIVE (US), Stryker (US), Qventus (US), Sweetch (Israel), Sirona Medical, Inc. (US), Ginger (US), Biobeat (Israel) are among a few emerging companies in the AI in Healthcare market.
Research Coverage: This research report categorizes the AI in Healthcare market based on offering, technology, application, end user, and region. The report describes the major drivers, restraints, challenges, and opportunities pertaining to the AI in Healthcare market and forecasts the same till 2029. Apart from these, the report also consists of leadership mapping and analysis of all the companies included in the AI in Healthcare ecosystem.
Key Benefits of Buying the Report The report will help the market leaders/new entrants in this market with information on the closest approximations of the revenue numbers for the overall AI in Healthcare market and the subsegments. This report will help stakeholders understand the competitive landscape and gain more insights to position their businesses better and plan suitable go-to-market strategies. The report also helps stakeholders understand the pulse of the market and provides them with information on key market drivers, restraints, challenges, and opportunities.
The report provides insights on the following pointers:
Analysis of key drivers (Generation of large and complex healthcare datasets, Pressing need to reduce healthcare costs, Improving computing power and declining hardware cost, Rising number of partnerships and collaborations among different domains in healthcare sector, and Growing need for improvised healthcare services due to imbalance between healthcare workforce and patients) influencing the growth of the AI in Healthcare market.
Product Development/Innovation: Detailed insights on upcoming technologies, research & development activities, and new product & service launches in the AI in Healthcare market.
Market Development: Comprehensive information about lucrative markets - the report analysis the AI in Healthcare market across varied regions
Market Diversification: Exhaustive information about new products & services, untapped geographies, recent developments, and investments in the AI in Healthcare market
Competitive Assessment: In-depth assessment of market shares, growth strategies, and service offerings of leading players like Koninklijke Philips N.V. (Netherlands), Microsoft (US), Siemens Healthineers AG (Germany), Intel Corporation (US), NVIDIA Corporation (US) among others in the AI in Healthcare market.
TABLE OF CONTENTS
1 INTRODUCTION
1.1 STUDY OBJECTIVES
1.2 MARKET DEFINITION
1.2.1 INCLUSIONS AND EXCLUSIONS
1.3 STUDY SCOPE
1.3.1 MARKETS COVERED
1.3.2 REGIONAL SCOPE
1.3.3 YEARS CONSIDERED
1.4 CURRENCY CONSIDERED
1.5 UNITS CONSIDERED
1.6 LIMITATIONS
1.7 STAKEHOLDERS
1.8 SUMMARY OF CHANGES
1.9 IMPACT OF RECESSION
FIGURE 1 GDP GROWTH PROJECTION DATA FOR MAJOR ECONOMIES, 2021-2023
1.10 GDP GROWTH PROJECTION UNTIL 2024 FOR MAJOR ECONOMIES
2 RESEARCH METHODOLOGY
2.1 RESEARCH DATA
FIGURE 2 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: RESEARCH DESIGN
2.1.1 SECONDARY DATA
2.1.1.1 List of major secondary sources
2.1.1.2 Key data from secondary sources
2.1.2 PRIMARY DATA
2.1.2.1 List of key interview participants
2.1.2.2 Key data from primary sources
2.1.2.3 Key industry insights
2.1.2.4 Breakdown of primaries
2.1.3 SECONDARY AND PRIMARY RESEARCH
2.2 MARKET SIZE ESTIMATION
FIGURE 3 RESEARCH FLOW: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET SIZE ESTIMATION
FIGURE 4 MARKET SIZE ESTIMATION METHODOLOGY (SUPPLY SIDE): REVENUE GENERATED BY COMPANIES FROM ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET
2.2.1 BOTTOM-UP APPROACH
2.2.1.1 Approach to estimate market size using bottom-up analysis (demand side)
FIGURE 6 MARKET SIZE ESTIMATION METHODOLOGY: BOTTOM-UP APPROACH (DEMAND SIDE): REVENUE GENERATED FROM ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER
2.2.2 TOP-DOWN APPROACH
2.2.2.1 Approach to estimate market size using top-down analysis (supply side)
5.4.1 AVERAGE SELLING PRICE (ASP) TREND OF COMPONENTS OFFERED BY KEY PLAYERS, 2020-2029
FIGURE 28 AVERAGE SELLING PRICE (ASP) OF PROCESSOR COMPONENTS OFFERED BY KEY PLAYERS
TABLE 1 AVERAGE SELLING PRICE (ASP) OF PROCESSOR COMPONENTS OFFERED BY KEY PLAYERS
5.4.2 AVERAGE SELLING PRICE (ASP) TREND OF PROCESSOR COMPONENTS, BY REGION, 2020-2029
FIGURE 29 AVERAGE SELLING PRICE (ASP) TREND OF PROCESSOR COMPONENTS, BY REGION, 2020-2029
5.5 VALUE CHAIN ANALYSIS
FIGURE 30 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: VALUE CHAIN ANALYSIS
5.6 ECOSYSTEM MAPPING
FIGURE 31 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: ECOSYSTEM MAPPING
TABLE 2 COMPANIES AND THEIR ROLES IN ARTIFICIAL INTELLIGENCE IN HEALTHCARE ECOSYSTEM
5.7 TECHNOLOGY ANALYSIS
5.7.1 CLOUD COMPUTING
5.7.2 CLOUD GPU
5.7.3 GENERATIVE AI
5.7.4 CLOUD-BASED PACS
5.7.5 MULTI-CLOUD
5.8 PATENT ANALYSIS
TABLE 3 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: INNOVATIONS AND PATENT REGISTRATIONS
FIGURE 32 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: PATENTS GRANTED, 2013-2023
FIGURE 33 TOP 10 PATENT OWNERS IN LAST 10 YEARS, 2013-2023
TABLE 4 TOP PATENT OWNERS IN ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET IN LAST 10 YEARS
5.9 TRADE ANALYSIS
FIGURE 34 IMPORT DATA FOR HS CODE 854231-COMPLIANT PRODUCTS, BY COUNTRY, 2018-2022 (USD MILLION)
FIGURE 35 EXPORT DATA FOR HS CODE 854231-COMPLIANT PRODUCTS, BY COUNTRY, 2018-2022 (USD MILLION)
5.10 KEY CONFERENCES AND EVENTS, 2024-2025
TABLE 5 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: LIST OF CONFERENCES AND EVENTS, 2024-2025
5.11 CASE STUDY ANALYSIS
5.11.1 BIOBEAT LAUNCHED HOME-BASED REMOTE PATIENT MONITORING KIT DURING PEAK WAVE OF COVID-19
5.11.2 MICROSOFT COLLABORATED WITH CLEVELAND CLINIC TO APPLY PREDICTIVE AND ADVANCED ANALYTICS TO IDENTIFY POTENTIAL AT-RISK PATIENTS UNDER ICU CARE
5.11.3 TGEN COLLABORATED WITH INTEL CORPORATION AND DELL TECHNOLOGIES TO ASSIST PHYSICIANS AND RESEARCHERS ACCELERATE DIAGNOSIS AND TREATMENT AT LOWER COST
5.11.4 INSILICO DEVELOPED ML-POWERED TOOLS FOR DRUG IDENTIFICATION AND CHEMISTRY42 FOR NOVEL COMPOUND DESIGN
5.11.5 GE HEALTHCARE IMPROVED PATIENT OUTCOMES BY REDUCING WORKFLOW PROCESSING TIME USING MEDICAL IMAGING DATA
5.12 TARIFFS, STANDARDS, AND REGULATORY LANDSCAPE
TABLE 6 MFN TARIFF FOR HS CODE 854231-COMPLIANT PRODUCTS EXPORTED BY US, 2022
TABLE 7 MFN TARIFF FOR HS CODE 854231-COMPLIANT PRODUCTS EXPORTED BY CHINA, 2022
TABLE 8 MFN TARIFF FOR HS CODE 854231-COMPLIANT PRODUCTS EXPORTED BY GERMANY, 2022
5.12.1 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
TABLE 9 NORTH AMERICA: REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
TABLE 10 EUROPE: REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
TABLE 11 ASIA PACIFIC: REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
TABLE 12 ROW: REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
5.12.2 STANDARDS
5.12.2.1 ISO 22399:2020
5.12.2.2 IEC 62366:2015
5.12.2.3 Health Insurance Portability and Accountability Act (HIPAA)
5.12.2.4 EU General Data Protection Regulation (GDPR)
5.12.2.5 Fast Healthcare Interoperability Resources (HL7 FHIR)
5.12.2.6 Medical Device Regulation
5.12.2.7 World Health Organization Artificial intelligence for Health Guide
5.12.2.8 Algorithmic Justice League framework for assessing AI in healthcare
5.12.3 GOVERNMENT REGULATIONS
5.12.3.1 US
5.12.3.2 Europe
5.12.3.3 China
5.12.3.4 Japan
5.12.3.5 India
5.13 PORTER'S FIVE FORCES ANALYSIS
TABLE 13 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: PORTER'S FIVE FORCES ANALYSIS
FIGURE 36 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: PORTER'S FIVE FORCES ANALYSIS
5.13.1 THREAT OF NEW ENTRANTS
5.13.2 THREAT OF SUBSTITUTES
5.13.3 BARGAINING POWER OF SUPPLIERS
5.13.4 BARGAINING POWER OF BUYERS
5.13.5 INTENSITY OF COMPETITIVE RIVALRY
5.14 KEY STAKEHOLDERS AND BUYING CRITERIA
5.14.1 KEY STAKEHOLDERS IN BUYING PROCESS
FIGURE 37 INFLUENCE OF KEY STAKEHOLDERS ON BUYING PROCESS FOR TOP THREE END USERS
TABLE 14 INFLUENCE OF STAKEHOLDERS ON BUYING PROCESS FOR TOP THREE END USERS
5.14.2 BUYING CRITERIA
FIGURE 38 KEY BUYING CRITERIA FOR TOP THREE END USERS
TABLE 15 KEY BUYING CRITERIA FOR TOP THREE END USERS
6 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY OFFERING
6.1 INTRODUCTION
FIGURE 39 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY OFFERING
FIGURE 40 SOFTWARE SEGMENT TO DOMINATE MARKET DURING FORECAST PERIOD
TABLE 16 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY OFFERING, 2020-2023 (USD MILLION)
TABLE 17 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY OFFERING, 2024-2029 (USD MILLION)
6.2 HARDWARE
TABLE 18 HARDWARE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2020-2023 (USD MILLION)
TABLE 19 HARDWARE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2024-2029 (USD MILLION)
TABLE 20 HARDWARE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020-2023 (USD MILLION)
TABLE 21 HARDWARE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024-2029 (USD MILLION)
6.2.1 PROCESSOR
6.2.1.1 Need for real-time processing of patient data to boost demand
TABLE 22 PROCESSOR: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2020-2023 (MILLION UNITS)
TABLE 23 PROCESSOR: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2024-2029 (MILLION UNITS)
TABLE 24 PROCESSOR: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2020-2023 (USD MILLION)
TABLE 25 PROCESSOR: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2024-2029 (USD MILLION)
6.2.1.2 MPUs/CPUs
TABLE 26 CASE STUDY: PHILIPS COLLABORATED WITH INTEL CORPORATION TO OPTIMIZE AI INFERENCING HEALTHCARE WORKLOADS ON INTEL XEON SCALABLE PROCESSORS USING OPENVINO TOOLKIT
6.2.1.3 GPUs
TABLE 27 CASE STUDY: DEEPPHARMA PLATFORM, OFFERED BY INSILICO, EQUIPPED WITH ADVANCED DEEP LEARNING TECHNIQUES, HELPS ANALYZE MULTI-OMICS DATA AND TISSUE-SPECIFIC PATHWAY ACTIVATION PROFILES
6.2.1.4 FPGAs
TABLE 28 CASE STUDY: INTEL CORPORATION, IN COLLABORATION WITH BROAD INSTITUTE, DEVELOPED BIGSTACK** 2.0 TO MEET EVOLVING DEMANDS OF GENOMICS RESEARCH
6.2.1.5 ASICs
6.2.2 MEMORY
6.2.2.1 Increasing demand for real-time medical image analysis and diagnosis support systems to drive market
TABLE 29 CASE STUDY: HUAWEI ASSISTED TOULOUSE UNIVERSITY HOSPITAL WITH OCEANSTOR ALL-FLASH SOLUTION THAT OFFERS LOW LATENCY AND SIMPLIFIED OPERATIONS AND MAINTENANCE MANAGEMENT
6.2.3 NETWORK
6.2.3.1 Growing need for remote patient monitoring and precision medicine to foster segmental growth
TABLE 30 NETWORK: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2020-2023 (USD MILLION)
TABLE 31 NETWORK: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2024-2029 (USD MILLION)
6.3 SOFTWARE
TABLE 32 SOFTWARE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2020-2023 (USD MILLION)
TABLE 33 SOFTWARE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2024-2029 (USD MILLION)
TABLE 34 SOFTWARE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020-2023 (USD MILLION)
TABLE 35 SOFTWARE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024-2029 (USD MILLION)
6.3.1 AI SOLUTION
6.3.1.1 Integration of non-procedural languages into AI solutions to accelerate segmental growth
TABLE 36 CASE STUDY: COGNIZANT LEVERAGED AZURE PLATFORM OF MICROSOFT AND DEVELOPED RESOLV, THAT EMPLOYS NATURAL LANGUAGE PROCESSING TO PROVIDE REAL-TIME RESPONSE TO ANALYTICAL QUERIES
TABLE 37 SOFTWARE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET FOR AI SOLUTIONS, BY DEPLOYMENT TYPE, 2020-2023 (USD MILLION)
TABLE 38 SOFTWARE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET FOR AI SOLUTIONS, BY DEPLOYMENT TYPE, 2024-2029 (USD MILLION)
6.3.1.2 On-premises
TABLE 39 CASE STUDY: GE HEALTHCARE ENHANCED ON-PREMISES CAPABILITY WITH SCYLLADB'S PROJECT ALTERNATOR
6.3.1.3 Cloud
TABLE 40 CASE STUDY: TAKEDA COLLABORATED WITH DELOITTE TO EMPLOY DEEP MINER TOOLKIT FOR RAPID DEVELOPMENT AND TESTING OF PREDICTIVE MODELS
6.3.2 AI PLATFORM
6.3.2.1 Increasing applications in development of toolkits for healthcare solutions to drive market
TABLE 41 CASE STUDY: CAYUGA MEDICAL CENTER SOUGHT SIMPLE CDI SOFTWARE SOLUTION TO IMPROVE WORKFLOWS AND REDUCE COSTS
TABLE 42 SOFTWARE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET FOR AI PLATFORMS, BY TYPE, 2020-2023 (USD MILLION)
TABLE 43 SOFTWARE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET FOR AI PLATFORMS, BY TYPE, 2024-2029 (USD MILLION)
6.3.2.2 Machine learning framework
6.3.2.3 Application program interface
6.4 SERVICES
TABLE 44 SERVICES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2020-2023 (USD MILLION)
TABLE 45 SERVICES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2024-2029 (USD MILLION)
TABLE 46 SERVICES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020-2023 (USD MILLION)
TABLE 47 SERVICES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024-2029 (USD MILLION)
6.4.1 DEPLOYMENT & INTEGRATION
6.4.1.1 Enhanced patient care along with streamlines workflows to drive demand
6.4.2 SUPPORT & MAINTENANCE
6.4.2.1 Need to evaluate performance and maintain operational stability to drive market
7 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TECHNOLOGY
7.1 INTRODUCTION
FIGURE 41 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TECHNOLOGY
FIGURE 42 MACHINE LEARNING TECHNOLOGY TO LEAD MARKET DURING FORECAST PERIOD
TABLE 48 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TECHNOLOGY, 2020-2023 (USD MILLION)
TABLE 49 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TECHNOLOGY, 2024-2029 (USD MILLION)
7.2 MACHINE LEARNING
TABLE 50 CASE STUDY: IN COLLABORATION WITH INTEL AND APOQLAR, THEBLUE.AI INTRODUCED BLUW.GDPR. EQUIPPED WITH ML ALGORITHMS ACCELERATED BY OPENVINO TOOLKIT
TABLE 51 MACHINE LEARNING: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2020-2023 (USD MILLION)
TABLE 52 MACHINE LEARNING: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2024-2029 (USD MILLION)
7.2.1 DEEP LEARNING
7.2.1.1 Rising applications in voice recognition, fraud detection, and recommendation engines to drive market
TABLE 53 WINNING HEALTH TECHNOLOGY INTRODUCED AI MEDICAL IMAGING SOLUTION BASED ON AMAX DEEP LEARNING ALL-IN-ONE TO REDUCE OVERALL MODEL INFERENCE TIME FROM OVER 0.5 HOURS TO LESS THAN 2 MINUTES FOR AI-AIDED DIAGNOSTIC IMAGING OF PULMONARY NODULES
7.2.2 SUPERVISED LEARNING
7.2.2.1 Contribution to clinical decision-making and enhancing personalized medications to boost demand
7.2.3 REINFORCEMENT LEARNING
7.2.3.1 Enhanced diagnostic accuracy in medical imaging analysis to fuel market growth
7.2.4 UNSUPERVISED LEARNING
7.2.4.1 Ability to uncover hidden patterns and handle unlabeled data challenges to boost demand
7.2.5 OTHERS
7.3 NATURAL LANGUAGE PROCESSING
TABLE 54 CASE STUDY: MARUTI TECHLABS ASSISTED UKHEALTH WITH ML MODEL FOR AUTOMATIC DATA EXTRACTION AND CLASSIFICATION
TABLE 55 NATURAL LANGUAGE PROCESSING: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2020-2023 (USD MILLION)
TABLE 56 NATURAL LANGUAGE PROCESSING: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2024-2029 (USD MILLION)
7.3.1 IVR
7.3.1.1 Enhanced operational efficiency and optimized clinical support to drive market
7.3.2 OCR
7.3.2.1 Reduced errors in data entry and streamlined administrative processes to spur demand
7.3.3 PATTERN AND IMAGE RECOGNITION
7.3.3.1 Optimized therapeutic outcomes and development of personal medication to foster segmental growth
7.3.4 AUTO CODING
7.3.4.1 Contribution to cost-saving and optimization of coding processes to drive market
7.3.5 CLASSIFICATION AND CATEGORIZATION
7.3.5.1 Accurate prediction of disease outcomes to boost demand
7.3.6 TEXT ANALYTICS
7.3.6.1 Significant contribution to drug discovery by examining extensive datasets of scientific literature to boost demand
7.3.7 SPEECH ANALYTICS
7.3.7.1 Contribution to sentiment analysis by assessing tone of patient conversations to boost demand
7.4 CONTEXT-AWARE COMPUTING
TABLE 57 CONTEXT-AWARE COMPUTING: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2020-2023 (USD MILLION)
TABLE 58 CONTEXT-AWARE COMPUTING: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2024-2029 (USD MILLION)
7.4.1 DEVICE CONTEXT
7.4.1.1 Ability to offer comprehensive view of patient data to boost demand
7.4.2 USER CONTEXT
7.4.2.1 Better predictive analysis for disease prevention to foster segmental growth
7.4.3 PHYSICAL CONTEXT
7.4.3.1 Ability to address individualized needs based on surrounding environment to boost market
7.5 COMPUTER VISION
7.5.1 ENHANCED PRECISION WITH 3D VISUALIZATIONS AND PERSONALIZED PROCEDURES TO FOSTER SEGMENTAL GROWTH
TABLE 59 CASE STUDY: PUNKTUM COLLABORATED WITH MAYO CLINIC TO DEVELOP CUTTING-EDGE DEEP LEARNING-BASED MODEL FOCUSED ON COMPUTER VISION FOR ACCURATE CLASSIFICATION OF ISCHEMIC STROKE ORIGINS
8 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION
8.1 INTRODUCTION
FIGURE 43 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION
FIGURE 44 MEDICAL IMAGING & DIAGNOSTICS SEGMENT TO ACCOUNT FOR LARGEST MARKET SHARE IN 2029
TABLE 60 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2020-2023 (USD MILLION)
TABLE 61 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2024-2029 (USD MILLION)
8.2 PATIENT DATA & RISK ANALYSIS
8.2.1 CONVERGENCE OF ML AND NLP TO OFFER LUCRATIVE GROWTH OPPORTUNITIES FOR PLAYERS
TABLE 62 CASE STUDY: MAYO CLINIC PARTNERED WITH GOOGLE TO IMPLEMENT AI MODELS AND ENHANCE PATIENT CARE
TABLE 63 PATIENT DATA & RISK ANALYSIS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020-2023 (USD MILLION)
TABLE 64 PATIENT DATA & RISK ANALYSIS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024-2029 (USD MILLION)
TABLE 65 PATIENT DATA & RISK ANALYSIS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020-2023 (USD MILLION)
TABLE 66 PATIENT DATA & RISK ANALYSIS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024-2029 (USD MILLION)
8.3 IN-PATIENT CARE & HOSPITAL MANAGEMENT
8.3.1 EASE OF PATIENT SCHEDULING WITH CHATBOTS AND VIRTUAL ASSISTANTS TO DRIVE MARKET
TABLE 67 CASE STUDY: PROMINENT MULTISPECIALTY HOSPITAL EMPLOYED ADOBE XD TO PREVENT RESOURCE WASTAGE AND ENHANCE EFFICIENCY
TABLE 68 IN-PATIENT CARE & HOSPITAL MANAGEMENT: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020-2023 (USD MILLION)
TABLE 69 IN-PATIENT CARE & HOSPITAL MANAGEMENT: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024-2029 (USD MILLION)
TABLE 70 IN-PATIENT CARE & HOSPITAL MANAGEMENT: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020-2023 (USD MILLION)
TABLE 71 IN-PATIENT CARE & HOSPITAL MANAGEMENT: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024-2029 (USD MILLION)
8.4 MEDICAL IMAGING & DIAGNOSTICS
8.4.1 ACCESSIBILITY IN MEDICAL IMAGING AND WORKFLOW OPTIMIZATION TO FOSTER SEGMENTAL GROWTH
TABLE 72 CASE STUDY: PHILIPS TRANSFORMED HEALTHCARE WITH AWS-POWERED AI SOLUTIONS
TABLE 73 MEDICAL IMAGING & DIAGNOSTICS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020-2023 (USD MILLION)
TABLE 74 MEDICAL IMAGING & DIAGNOSTICS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024-2029 (USD MILLION)
TABLE 75 MEDICAL IMAGING & DIAGNOSTICS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020-2023 (USD MILLION)
TABLE 76 MEDICAL IMAGING & DIAGNOSTICS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024-2029 (USD MILLION)
8.5.1 ENHANCED PATIENT COMPLIANCE THROUGH BEHAVIORAL ANALYSIS TO BOOST DEMAND
TABLE 77 LIFESTYLE MANAGEMENT & REMOTE PATIENT MONITORING: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020-2023 (USD MILLION)
TABLE 78 LIFESTYLE MANAGEMENT & REMOTE PATIENT MONITORING: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024-2029 (USD MILLION)
TABLE 79 LIFESTYLE MANAGEMENT & REMOTE PATIENT MONITORING: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020-2023 (USD MILLION)
TABLE 80 LIFESTYLE MANAGEMENT & REMOTE PATIENT MONITORING: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024-2029 (USD MILLION)
8.6 VIRTUAL ASSISTANTS
8.6.1 ABILITY TO OFFER SIMPLIFIED COMPLEX MEDICAL INFORMATION TO DRIVE MARKET
TABLE 81 CASE STUDY: OSF COLLABORATED WITH GYANT TO IMPLEMENT CLARE, AI VIRTUAL CARE NAVIGATION ASSISTANT, BOOSTING DIGITAL HEALTH TRANSFORMATION
TABLE 82 VIRTUAL ASSISTANT: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020-2023 (USD MILLION)
TABLE 83 VIRTUAL ASSISTANT: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024-2029 (USD MILLION)
TABLE 84 VIRTUAL ASSISTANT: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020-2023 (USD MILLION)
TABLE 85 VIRTUAL ASSISTANT: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024-2029 (USD MILLION)
8.7 DRUG DISCOVERY
8.7.1 ACCELERATED IDENTIFICATION OF POTENTIAL DRUG CANDIDATES TO BOOST DEMAND
TABLE 86 CASE STUDY: AZOTHBIO UTILIZED RESCALE'S PLATFORM TO ENHANCE R&D AGILITY
TABLE 87 DRUG DISCOVERY: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020-2023 (USD MILLION)
TABLE 88 DRUG DISCOVERY: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024-2029 (USD MILLION)
TABLE 89 DRUG DISCOVERY: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020-2023 (USD MILLION)
TABLE 90 DRUG DISCOVERY: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024-2029 (USD MILLION)
8.8 RESEARCH
8.8.1 GROWING IMPORTANCE IN ANALYSIS OF SEQUENCE AND FUNCTIONAL PATTERNS FROM SEQUENCE DATABASES TO ACCELERATE DEMAND
TABLE 91 RESEARCH: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020-2023 (USD MILLION)
TABLE 92 RESEARCH: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024-2029 (USD MILLION)
TABLE 93 RESEARCH: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020-2023 (USD MILLION)
TABLE 94 RESEARCH: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024-2029 (USD MILLION)
8.9 HEALTHCARE ASSISTANCE ROBOTS
8.9.1 USE TO REVOLUTIONIZE PATIENT CARE BY STREAMLINING TASKS AND ENABLING REAL-TIME DATA ANALYSIS AND ENHANCE HEALTHCARE EXPERIENCES TO DRIVE MARKET
TABLE 95 HEALTHCARE ASSISTANCE ROBOTS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020-2023 (USD MILLION)
TABLE 96 HEALTHCARE ASSISTANCE ROBOTS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024-2029 (USD MILLION)
TABLE 97 HEALTHCARE ASSISTANCE ROBOTS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020-2023 (USD MILLION)
TABLE 98 HEALTHCARE ASSISTANCE ROBOTS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024-2029 (USD MILLION)
8.10 PRECISION MEDICINES
8.10.1 PERSONALIZED HEALTHCARE BY STREAMLINING CLINICAL TRIALS TO ACCELERATE DEMAND
TABLE 99 PRECISION MEDICINE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020-2023 (USD MILLION)
TABLE 100 PRECISION MEDICINE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024-2029 (USD MILLION)
TABLE 101 PRECISION MEDICINE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020-2023 (USD MILLION)
TABLE 102 PRECISION MEDICINE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024-2029 (USD MILLION)
8.11 EMERGENCY ROOMS & SURGERIES
8.11.1 QUICK IDENTIFICATION OF LIFE-THREATENING PATHOLOGIES TO FOSTER SEGMENTAL GROWTH
TABLE 103 EMERGENCY ROOMS & SURGERIES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020-2023 (USD MILLION)
TABLE 104 EMERGENCY ROOMS & SURGERIES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024-2029 (USD MILLION)
TABLE 105 EMERGENCY ROOMS & SURGERIES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020-2023 (USD MILLION)
TABLE 106 EMERGENCY ROOMS & SURGERIES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024-2029 (USD MILLION)
8.12 WEARABLES
8.12.1 PERSONALIZED TREATMENT STRATEGIES AND REAL-TIME INSIGHTS TO BOOST DEMAND
TABLE 107 CASE STUDY: KENSCI COLLABORATED WITH MICROSOFT TO ASSIST US NATIONAL GOVERNMENT IN IDENTIFYING PATIENTS WITH COPD
TABLE 108 WEARABLES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020-2023 (USD MILLION)
TABLE 109 WEARABLES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024-2029 (USD MILLION)
TABLE 110 WEARABLES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020-2023 (USD MILLION)
TABLE 111 WEARABLES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024-2029 (USD MILLION)
8.13 MENTAL HEALTH
8.13.1 PRESSING NEED TO DETECT DEPRESSION AND IDENTIFY SUICIDE RISKS THROUGH TEXT ANALYSIS TO DRIVE MARKET
TABLE 112 MENTAL HEALTH: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020-2023 (USD MILLION)
TABLE 113 MENTAL HEALTH: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024-2029 (USD MILLION)
TABLE 114 MENTAL HEALTH: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020-2023 (USD MILLION)
TABLE 115 MENTAL HEALTH: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024-2029 (USD MILLION)
8.14 CYBERSECURITY
8.14.1 PREVENTION OF INFILTRATION ATTEMPTS AND ENHANCED SPEED OF THREAT DETECTION TO BOOST DEMAND
TABLE 116 CASE STUDY: SNORKEL FLOW CREATED HIGH-ACCURACY ML MODELS TO OVERCOME HAND-LABELING CHALLENGES
TABLE 117 CYBERSECURITY: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020-2023 (USD MILLION)
TABLE 118 CYBERSECURITY: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024-2029 (USD MILLION)
TABLE 119 CYBERSECURITY: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020-2023 (USD MILLION)
TABLE 120 CYBERSECURITY: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024-2029 (USD MILLION)
9 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER
9.1 INTRODUCTION
FIGURE 45 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER
FIGURE 46 HOSPITALS & HEALTHCARE PROVIDERS TO HOLD LARGEST MARKET SHARE IN 2029
TABLE 121 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020-2023 (USD MILLION)
TABLE 122 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024-2029 (USD MILLION)
9.2 HOSPITALS & HEALTHCARE PROVIDERS
9.2.1 INCREASING USE IN MINING MEDICAL DATA AND STUDYING GENOMICS-BASED DATA FOR PERSONALIZED MEDICINE TO BOOST MARKET GROWTH
TABLE 123 CASE STUDY: UNIVERSITY COLLEGE LONDON, KING'S COLLEGE LONDON, AND NATIONAL HEALTH SERVICE COLLABORATION RESULTED IN DEVELOPMENT OF COGSTACK, THAT REVOLUTIONIZED HEALTHCARE DATA UTILIZATION
TABLE 124 HOSPITALS & HEALTHCARE PROVIDERS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2020-2023 (USD MILLION)
TABLE 125 HOSPITALS & HEALTHCARE PROVIDERS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2024-2029 (USD MILLION)
TABLE 126 HOSPITALS & HEALTHCARE PROVIDERS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020-2023 (USD MILLION)
TABLE 127 HOSPITALS & HEALTHCARE PROVIDERS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024-2029 (USD MILLION)
9.3 PATIENTS
9.3.1 RISE IN USE OF AI IN MENTAL HEALTH SUPPORT APPLICATIONS THROUGH CHATBOTS AND VIRTUAL THERAPISTS TO BOOST MARKET GROWTH
TABLE 128 CASE STUDY: COGNIZANT PARTNERED WITH ONE OF CLIENTS TO ENHANCE CALLER SELF-SERVICE AND IMPROVE MEMBER EXPERIENCE METRICS
TABLE 129 PATIENTS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2020-2023 (USD MILLION)
TABLE 130 PATIENTS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2024-2029 (USD MILLION)
TABLE 131 PATIENTS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020-2023 (USD MILLION)
TABLE 132 PATIENTS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024-2029 (USD MILLION)
9.4 PHARMACEUTICALS & BIOTECHNOLOGY COMPANIES
9.4.1 GROWING PARTNERSHIPS AMONG PLAYERS TO OFFER LUCRATIVE GROWTH OPPORTUNITIES TO PLAYERS
TABLE 133 CASE STUDY: AZURE MACHINE LEARNING-BASED INTELLIGENT SYSTEM ASSISTED LEADING PHARMA COMPANY TO AUTO-CLASSIFY PRODUCTS INTO MARKET-RELATED CATEGORIES THAT BOOSTED OPERATIONAL EFFICIENCY
TABLE 134 PHARMACEUTICALS & BIOTECHNOLOGY COMPANIES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2020-2023 (USD MILLION)
TABLE 135 PHARMACEUTICALS & BIOTECHNOLOGY COMPANIES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2024-2029 (USD MILLION)
TABLE 136 PHARMACEUTICALS & BIOTECHNOLOGY COMPANIES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020-2023 (USD MILLION)
TABLE 137 PHARMACEUTICALS & BIOTECHNOLOGY COMPANIES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024-2029 (USD MILLION)
9.5 HEALTHCARE PAYERS
9.5.1 FAST AND ACCURATE CLAIM PROCESSING AND ENHANCED FRAUD DETECTION BENEFITS TO BOOST DEMAND
TABLE 138 HEALTHCARE PAYERS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2020-2023 (USD MILLION)
TABLE 139 HEALTHCARE PAYERS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2024-2029 (USD MILLION)
TABLE 140 HEALTHCARE PAYERS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020-2023 (USD MILLION)
TABLE 141 HEALTHCARE PAYERS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024-2029 (USD MILLION)
9.6 OTHERS
TABLE 142 OTHERS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2020-2023 (USD MILLION)
TABLE 143 OTHERS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2024-2029 (USD MILLION)
TABLE 144 OTHERS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020-2023 (USD MILLION)
TABLE 145 OTHERS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024-2029 (USD MILLION)
10 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION
10.1 INTRODUCTION
FIGURE 47 ASIA PACIFIC TO REGISTER HIGHEST CAGR DURING FORECAST PERIOD
TABLE 146 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020-2023 (USD MILLION)
TABLE 147 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024-2029 (USD MILLION)
10.2 NORTH AMERICA
10.2.1 NORTH AMERICA: RECESSION IMPACT
FIGURE 48 NORTH AMERICA: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET SNAPSHOT
FIGURE 49 US TO DOMINATE NORTH AMERICAN ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET IN 2029
TABLE 148 NORTH AMERICA: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY COUNTRY, 2020-2023 (USD MILLION)
TABLE 149 NORTH AMERICA: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY COUNTRY, 2024-2029 (USD MILLION)
TABLE 150 NORTH AMERICA: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY OFFERING, 2020-2023 (USD MILLION)
TABLE 151 NORTH AMERICA: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY OFFERING, 2024-2029 (USD MILLION)
TABLE 152 NORTH AMERICA: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2020-2023 (USD MILLION)
TABLE 153 NORTH AMERICA: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2024-2029 (USD MILLION)
TABLE 154 NORTH AMERICA: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020-2023 (USD MILLION)
TABLE 155 NORTH AMERICA: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024-2029 (USD MILLION)
10.2.2 US
10.2.2.1 High healthcare spending in US to drive market
TABLE 156 US: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020-2023 (USD MILLION)
TABLE 157 US: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024-2029 (USD MILLION)
10.2.3 CANADA
10.2.3.1 Government-led initiatives to support deployment of AI in healthcare sector to boost demand
TABLE 158 CANADA: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020-2023 (USD MILLION)
TABLE 159 CANADA: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024-2029 (USD MILLION)
10.2.4 MEXICO
10.2.4.1 Increasing private sector investments in AI healthcare technologies to drive market
TABLE 160 MEXICO: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020-2023 (USD MILLION)
TABLE 161 MEXICO: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024-2029 (USD MILLION)
10.3 EUROPE
10.3.1 EUROPE: RECESSION IMPACT
FIGURE 50 EUROPE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET SNAPSHOT
FIGURE 51 REST OF EUROPE TO EXHIBIT HIGHEST CAGR IN EUROPEAN ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET DURING FORECAST PERIOD
TABLE 162 EUROPE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY COUNTRY, 2020-2023 (USD MILLION)
TABLE 163 EUROPE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY COUNTRY, 2024-2029 (USD MILLION)
TABLE 164 EUROPE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY OFFERING, 2020-2023 (USD MILLION)
TABLE 165 EUROPE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY OFFERING, 2024-2029 (USD MILLION)
TABLE 166 EUROPE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2020-2023 (USD MILLION)
TABLE 167 EUROPE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2024-2029 (USD MILLION)
TABLE 168 EUROPE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020-2023 (USD MILLION)
TABLE 169 EUROPE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024-2029 (USD MILLION)
10.3.2 GERMANY
10.3.2.1 Rising healthcare data generation to drive market
TABLE 170 GERMANY: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020-2023 (USD MILLION)
TABLE 171 GERMANY: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024-2029 (USD MILLION)
10.3.3 UK
10.3.3.1 Targeted treatment with increased success rates to fuel market growth
TABLE 172 UK: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020-2023 (USD MILLION)
TABLE 173 UK: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024-2029 (USD MILLION)
10.3.4 FRANCE
10.3.4.1 Focus on telemedicine and chronic disease management to drive market
TABLE 174 FRANCE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020-2023 (USD MILLION)
TABLE 175 FRANCE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024-2029 (USD MILLION)
10.3.5 ITALY
10.3.5.1 Rising geriatric population to drive market
TABLE 176 ITALY: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020-2023 (USD MILLION)
TABLE 177 ITALY: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024-2029 (USD MILLION)
10.3.6 SPAIN
10.3.6.1 Growing partnerships between technology firms and healthcare providers to boost demand
TABLE 178 SPAIN: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020-2023 (USD MILLION)
TABLE 179 SPAIN: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024-2029 (USD MILLION)
10.3.7 REST OF EUROPE
TABLE 180 REST OF EUROPE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020-2023 (USD MILLION)
TABLE 181 REST OF EUROPE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024-2029 (USD MILLION)
10.4 ASIA PACIFIC
10.4.1 ASIA PACIFIC: RECESSION IMPACT
FIGURE 52 ASIA PACIFIC: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET SNAPSHOT
FIGURE 53 CHINA TO EXHIBIT HIGHEST CAGR IN ASIA PACIFIC ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET DURING FORECAST PERIOD
TABLE 182 ASIA PACIFIC: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY COUNTRY, 2020-2023 (USD MILLION)
TABLE 183 ASIA PACIFIC: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY COUNTRY, 2024-2029 (USD MILLION)
TABLE 184 ASIA PACIFIC: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY OFFERING, 2020-2023 (USD MILLION)
TABLE 185 ASIA PACIFIC: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY OFFERING, 2024-2029 (USD MILLION)
TABLE 186 ASIA PACIFIC: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2020-2023 (USD MILLION)
TABLE 187 ASIA PACIFIC: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2024-2029 (USD MILLION)
TABLE 188 ASIA PACIFIC: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020-2023 (USD MILLION)
TABLE 189 ASIA PACIFIC: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024-2029 (USD MILLION)
10.4.2 CHINA
10.4.2.1 Government-led measures to expedite integration of AI into healthcare sector to drive market
TABLE 190 CHINA: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020-2023 (USD MILLION)
TABLE 191 CHINA: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024-2029 (USD MILLION)
10.4.3 JAPAN
10.4.3.1 Increasing number of AI-driven start-ups manufacturing diagnostic and therapeutic tools to fuel market growth
TABLE 192 JAPAN: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020-2023 (USD MILLION)
TABLE 193 JAPAN: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024-2029 (USD MILLION)
10.4.4 SOUTH KOREA
10.4.4.1 Increasing incidence of cancer to drive market
TABLE 194 SOUTH KOREA: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020-2023 (USD MILLION)
TABLE 195 SOUTH KOREA: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024-2029 (USD MILLION)
10.4.5 INDIA
10.4.5.1 Developing IT infrastructure and AI-friendly government initiatives to spur market growth
TABLE 196 INDIA: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020-2023 (USD MILLION)
TABLE 197 INDIA: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024-2029 (USD MILLION)
10.4.6 REST OF ASIA PACIFIC
TABLE 198 REST OF ASIA PACIFIC: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020-2023 (USD MILLION)
TABLE 199 REST OF ASIA PACIFIC: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024-2029 (USD MILLION)
10.5 ROW
10.5.1 ROW: RECESSION IMPACT
FIGURE 54 ROW: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET SNAPSHOT
FIGURE 55 SOUTH AMERICA TO DOMINATE ROW MARKET IN 2029
TABLE 200 ROW: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020-2023 (USD MILLION)
TABLE 201 ROW: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024-2029 (USD MILLION)
TABLE 202 ROW: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY OFFERING, 2020-2023 (USD MILLION)
TABLE 203 ROW: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY OFFERING, 2024-2029 (USD MILLION)
TABLE 204 ROW: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2020-2023 (USD MILLION)
TABLE 205 ROW: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2024-2029 (USD MILLION)
TABLE 206 ROW: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020-2023 (USD MILLION)
TABLE 207 ROW: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024-2029 (USD MILLION)
10.5.2 SOUTH AMERICA
10.5.2.1 High investments in healthcare IT to drive market
TABLE 208 SOUTH AMERICA: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020-2023 (USD MILLION)
TABLE 209 SOUTH AMERICA: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024-2029 (USD MILLION)
10.5.3 GCC
10.5.3.1 Rising focus on technological advancements in healthcare sector to drive market
TABLE 210 GCC: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020-2023 (USD MILLION)
TABLE 211 GCC: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024-2029 (USD MILLION)
10.5.4 REST OF MIDDLE EAST & AFRICA
10.5.4.1 Growing investments in information and communication technologies to boost demand
TABLE 212 REST OF MIDDLE EAST & AFRICA: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020-2023 (USD MILLION)
TABLE 213 REST OF MEA: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024-2029 (USD MILLION)
11 COMPETITIVE LANDSCAPE
11.1 OVERVIEW
11.2 STRATEGIES ADOPTED BY MAJOR PLAYERS, 2020-2023
TABLE 214 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: OVERVIEW OF STRATEGIES DEPLOYED BY KEY PLAYERS, 2020-2023
11.3 REVENUE ANALYSIS, 2019-2023
FIGURE 56 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: REVENUE ANALYSIS OF TOP FIVE PLAYERS, 2019-2023
11.4 MARKET SHARE ANALYSIS, 2023
FIGURE 57 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET SHARE ANALYSIS, 2023
TABLE 215 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET SHARE ANALYSIS, 2023
11.5 COMPANY EVALUATION MATRIX, 2023
11.5.1 STARS
11.5.2 EMERGING LEADERS
11.5.3 PERVASIVE PLAYERS
11.5.4 PARTICIPANTS
FIGURE 58 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: COMPANY EVALUATION MATRIX, 2023
11.5.5 COMPANY FOOTPRINT
TABLE 216 OVERALL COMPANY FOOTPRINT
TABLE 217 COMPANY OFFERING FOOTPRINT
TABLE 218 COMPANY END USER FOOTPRINT
TABLE 219 COMPANY REGION FOOTPRINT
11.6 START-UP/SMALL AND MEDIUM-SIZED ENTERPRISE (SME) EVALUATION MATRIX, 2023
TABLE 273 JOHNSON & JOHNSON SERVICES, INC.: COMPANY OVERVIEW
FIGURE 71 JOHNSON & JOHNSON SERVICES, INC.: COMPANY SNAPSHOT
TABLE 274 JOHNSON & JOHNSON SERVICES, INC.: PRODUCTS/SOLUTIONS/SERVICES OFFERED
TABLE 275 JOHNSON & JOHNSON SERVICES, INC.: DEALS
12.2 OTHER PLAYERS
12.2.1 MERATIVE
12.2.2 GENERAL VISION INC.
12.2.3 CLOUDMEDX
12.2.4 ONCORA MEDICAL
12.2.5 ENLITIC, INC.
12.2.6 LUNIT INC.
12.2.7 QURE.AI
12.2.8 TEMPUS
12.2.9 COTA
12.2.10 FDNA INC.
12.2.11 RECURSION
12.2.12 ATOMWISE INC.
12.2.13 VIRGIN PULSE
12.2.14 BABYLON HEALTHCARE SERVICES LTD
12.2.15 MDLIVE (EVERNORTH GROUP)
12.2.16 STRYKER
12.2.17 QVENTUS
12.2.18 SWEETCH
12.2.19 SIRONA MEDICAL, INC.
12.2.20 GINGER
12.2.21 BIOBEAT
*Details on Business overview, Products /Solutions/Services offered, Recent developments, Product launches, MnM view, Key strengths/Right to win, Strategic choices made, and Weaknesses and Competitive threats might not be captured in case of unlisted companies.
