의료 분야 연합 학습 시장 규모, 점유율, 동향 분석 : 전개 모드별, 최종 용도별, 용도별, 지역별, 전망과 예측(2025-2032년)
Global Federated Learning In Healthcare Market Size, Share & Trends Analysis Report By Deployment Mode (On-Premise and Cloud-Based) By End Use, By Application, By Regional Outlook and Forecast, 2025 - 2032
상품코드:1767814
리서치사:KBV Research
발행일:2025년 04월
페이지 정보:영문 332 Pages
라이선스 & 가격 (부가세 별도)
한글목차
세계의 의료 분야 연합 학습 시장 규모는 예측 기간 중에 15.5%의 연평균 복합 성장률(CAGR)로 성장하여 2032년까지 877억 7,000만 달러에 이를 것으로 예상되고 있습니다.
또한, 프라이버시에 대한 관심은 의료 분야에서 항상 중요하며, 특히 환자 데이터 공유에 있어서는 더욱 그러합니다. 프라이버시 보호 기술에 대한 수요 증가는 의료 분야에서의 연합학습 도입의 중요한 원동력이 되고 있습니다. 기존 머신러닝은 데이터를 중앙에서 관리해야 하지만, 연합학습은 데이터를 로컬에 안전하게 보관하면서 분산된 데이터 소스를 통해 모델을 훈련시킬 수 있습니다. 따라서 환자 데이터 공유에서 프라이버시 보호 기술에 대한 수요가 증가하면서 시장 성장을 주도하고 있습니다.
코로나19 사태는 의료 분야 시장 성장에 큰 긍정적인 영향을 미쳤습니다. 팬데믹 기간 동안 전 세계 의료 기관들이 프라이버시 규정을 위반하지 않고 민감한 환자 데이터를 연계하고 분석해야 한다는 압박을 받으면서 고급 프라이버시 보호 머신러닝 기술에 대한 수요가 급증했습니다. 연합학습은 여러 기관에 걸친 AI 모델의 분산 학습을 가능하게 하고, 환자의 원시 데이터를 공유하지 않아도 되므로 중요한 솔루션으로 떠올랐습니다. 이처럼 코로나19 팬데믹은 시장에 긍정적인 영향을 미쳤습니다.
그러나 시장 도입을 가로막는 큰 요인 중 하나는 필요한 인프라와 기술 구축에 따른 초기 투자 및 지속적인 유지보수 비용이 높다는 점입니다. 의료기관, 특히 규모가 작거나 자원이 한정된 의료기관은 페더럴 러닝 시스템 도입에 소요되는 초기 비용이 너무 비싸다고 느낄 수 있습니다. 따라서 이러한 접근성의 불평등은 의료 분야에서 페더럴 러닝의 광범위한 도입을 저해하고, 환자 치료와 의료 발전에 미치는 영향을 제한적으로 만들 수 있습니다.
전개 모드의 전망
배포 모드에 따라 시장은 On-Premise 및 클라우드 기반으로 분류됩니다. 클라우드 기반 부문은 2024년 시장 매출의 47%를 차지할 것으로 예측됩니다. 이러한 성장은 확장성, 유연성, 비용 효율성에 대한 수요 증가로 인해 페더레이티드 러닝 솔루션 도입에 대한 수요가 증가함에 따라 주도되고 있습니다. 클라우드 기반 도입으로 의료 제공업체와 연구자들은 지리적으로 멀리 떨어져 있는 의료진과 연구자들이 서로 협력하고 분산된 데이터 소스에 원활하게 접근할 수 있게 되었습니다.
최종 용도 전망
최종 용도별로 시장은 병원 및 의료 제공업체, 제약 및 생명공학 기업, 연구 기관, 정부 및 규제 기관으로 분류됩니다. 제약 및 생명공학 기업 부문은 2024년 시장 수익의 31%를 차지할 것으로 예측됩니다. 이들 기업은 경쟁력 있는 데이터의 기밀성을 유지하면서 연구개발을 가속화하기 위해 연합학습을 도입하는 사례가 늘고 있습니다. 페더레이티드 러닝은 독점 데이터나 기밀 데이터를 공유하지 않고도 여러 소스의 데이터 세트를 사용하여 협력적인 모델 학습을 가능하게 합니다.
응용 전망
용도별로 시장은 의료 영상, 신약 개발, 전자 건강 기록(EHR) 분석, 원격 환자 모니터링, 임상시험으로 분류됩니다. 원격 환자 모니터링 부문은 2024년 시장 점유율의 14%를 차지할 것으로 예측됩니다. 지속적인 건강 모니터링 솔루션에 대한 수요 증가, 특히 만성 질환 관리에 대한 수요 증가로 인해 연합 학습의 도입이 촉진되고 있습니다. 이 기술을 통해 의료 서비스 제공업체는 웨어러블 기기, 모바일 건강 앱, 원격 센서에서 데이터를 수집하고 분석하면서 환자 정보의 기밀성과 국소성을 보장할 수 있습니다.
지역 전망
지역별로는 북미, 유럽, 아시아태평양, LAMEA로 시장을 분석했습니다. 아시아태평양은 2024년 시장의 30%의 매출 점유율을 차지할 것으로 예측됩니다. 이 지역의 성장은 의료 부문의 확대, 의료 기록의 디지털화 발전, AI 기반 의료 기술에 대한 투자 증가에 기인합니다. 중국, 인도, 일본, 한국 등의 국가들은 데이터 프라이버시 문제를 해결하는 동시에 임상 연구 및 개인 맞춤형 치료를 위한 환자 데이터의 효과적인 활용을 위해 페더럴 러닝을 적극적으로 도입하고 있습니다.
목차
제1장 시장 범위와 조사 방법
시장의 정의
목적
시장 범위
세분화
조사 방법
제2장 시장 개관
주요 하이라이트
제3장 시장 개요
서론
개요
시장 구성과 시나리오
시장에 영향을 미치는 주요 요인
시장 성장 촉진요인
시장 성장 억제요인
시장 기회
시장이 해결해야 할 과제
제4장 경쟁 분석 : 세계
시장 점유율 분석, 2024년
Porter의 Five Forces 분석
제5장 세계 시장 : 전개 모드별
세계의 On-Premise 시장 : 지역별
세계의 클라우드 기반 시장 : 지역별
제6장 세계 시장 : 최종 용도별
세계의 병원 및 의료 제공업체 시장 : 지역별
세계의 제약 기업 및 바이오테크놀러지 기업 시장 : 지역별
세계의 연구기관 시장 : 지역별
세계의 정부 및 규제기관 시장 : 지역별
제7장 세계 시장 : 용도별
세계의 Drug Discovery & Development 시장 : 지역별
세계의 의료용 이미징 시장 : 지역별
세계의 전자건강기록(EHR) 분석 시장 : 지역별
세계의 원격 환자 모니터링 시장 : 지역별
세계의 임상시험 및 기타 용도 시장 : 지역별
제8장 세계 시장 : 지역별
북미
북미 시장 : 국가별
미국
캐나다
멕시코
기타 북미
유럽
유럽 시장 : 국가별
독일
영국
프랑스
러시아
스페인
이탈리아
기타 유럽
아시아태평양
아시아태평양 시장 : 국가별
중국
일본
인도
한국
싱가포르
말레이시아
기타 아시아태평양
라틴아메리카/중동 및 아프리카
라틴아메리카/중동 및 아프리카 시장 : 국가별
브라질
아르헨티나
아랍에미리트(UAE)
사우디아라비아
남아프리카공화국
나이지리아
기타 라틴아메리카/중동 및 아프리카
제9장 기업 개요
GE HealthCare Technologies, Inc
Google LLC(Alphabet Inc)
IBM Corporation
Microsoft Corporation
Siemens Healthineers AG(Siemens AG)
Medtronic PLC
NVIDIA Corporation
Intel Corporation
Health Catalyst, Inc
Owkin
제10장 의료 분야 연합 학습 시장을 위한 성공 필수 조건
LSH
영문 목차
영문목차
The Global Federated Learning In Healthcare Market size is expected to reach $87.77 billion by 2032, rising at a market growth of 15.5% CAGR during the forecast period.
The North America segment recorded 33% revenue share in the market in 2024. This leadership is primarily driven by the region's advanced healthcare infrastructure, strong presence of key technology providers, and high investment in AI and machine learning innovations. Federated learning has gained momentum across hospitals, research centers, and pharmaceutical companies in the U.S. and Canada due to its ability to enhance data collaboration while upholding stringent privacy standards like HIPAA.
The healthcare sector increasingly emphasizes preventive care and early disease detection to reduce the long-term burden on healthcare systems. Federated learning, an advanced machine learning technique that allows data to remain securely within its local environment, aligns perfectly with this shift toward preventive healthcare. In conclusion, as healthcare systems worldwide embrace preventive measures and early detection methods, federated learning will transform healthcare delivery into a more efficient and proactive system.
Additionally, Privacy concerns have always been critical in healthcare, particularly regarding patient data sharing. The increasing demand for privacy-preserving technologies has become a key driver for adopting federated learning in healthcare. Unlike traditional machine learning, which requires data to be centralized in one location, federated learning allows models to be trained across decentralized data sources while keeping the data local and secure. Thus, increasing demand for privacy-preserving technologies in patient data sharing drives the market's growth.
The outbreak of COVID-19 had a significant positive impact on the growth of the market in the healthcare sector. During the pandemic, the demand for advanced and privacy-preserving machine learning techniques surged, as healthcare organizations worldwide were under immense pressure to collaborate and analyze sensitive patient data without violating privacy regulations. Federated learning emerged as a crucial solution by enabling decentralized training of AI models across multiple institutions, eliminating the need to share raw patient data. Thus, the COVID-19 pandemic had a positive impact on the market.
However, One of the major restraints for adopting market is the high initial investment and ongoing maintenance costs associated with setting up the necessary infrastructure and technology. Healthcare organizations, especially those in smaller or resource-limited settings, may find the upfront costs of deploying federated learning systems prohibitive. Therefore, this uneven access could hinder the widespread implementation of federated learning in healthcare, limiting its impact on patient care and medical advancements.
Deployment Mode Outlook
On the basis of deployment mode, the market is classified into on-premise and cloud-based. The cloud-based segment recorded 47% revenue share in the market in 2024. This growth is driven by the increasing demand for scalability, flexibility, and cost-efficiency in deploying federated learning solutions. Cloud-based deployment enables healthcare providers and researchers to collaborate across geographies, facilitating seamless access to decentralized data sources.
End Use Outlook
By end use, the market is divided into hospitals & healthcare providers, pharmaceutical and biotechnology companies, research institutions, and government & regulatory bodies. The pharmaceutical and biotechnology companies segment garnered 31% revenue share in the market in 2024. These companies increasingly adopt federated learning to accelerate research and development while maintaining competitive data confidentiality. Federated learning allows for collaborative model training using datasets from multiple sources without sharing proprietary or sensitive data.
Application Outlook
Based on application, the market is characterized into medical imaging, drug discovery & development, electronic health records (EHR) analysis, remote patient monitoring, and clinical trials. The remote patient monitoring segment procured 14% revenue share in the market in 2024. The rising demand for continuous health monitoring solutions, especially in managing chronic conditions, has fuelled the adoption of federated learning. This technology allows healthcare providers to gather and analyze data from wearable devices, mobile health apps, and remote sensors while ensuring that patient information remains confidential and localized.
Regional Outlook
Region-wise, the market is analyzed across North America, Europe, Asia Pacific, and LAMEA. The Asia Pacific segment witnessed 30% revenue share in the market in 2024. The region's growth is attributed to the expanding healthcare sector, increasing digitization of medical records, and rising investments in AI-based healthcare technologies. Countries such as China, India, Japan, and South Korea are actively embracing federated learning to address data privacy challenges while enabling effective use of patient data for clinical research and personalized treatment.
List of Key Companies Profiled
GE HealthCare Technologies, Inc.
Google LLC (Alphabet Inc.)
IBM Corporation
Microsoft Corporation
Siemens Healthineers AG (Siemens AG)
Medtronic PLC
NVIDIA Corporation
Intel Corporation
Health Catalyst, Inc.
Owkin
Global Federated Learning In Healthcare Market Report Segmentation
By Deployment Mode
On-Premise
Cloud-Based
By End Use
Hospitals & Healthcare Providers
Pharmaceutical & Biotechnology Companies
Research Institutions
Government & Regulatory Bodies
By Application
Drug Discovery & Development
Medical Imaging
Electronic Health Records (EHR) Analysis
Remote Patient Monitoring
Clinical Trials & Other Application
By Geography
North America
US
Canada
Mexico
Rest of North America
Europe
Germany
UK
France
Russia
Spain
Italy
Rest of Europe
Asia Pacific
China
Japan
India
South Korea
Singapore
Malaysia
Rest of Asia Pacific
LAMEA
Brazil
Argentina
UAE
Saudi Arabia
South Africa
Nigeria
Rest of LAMEA
Table of Contents
Chapter 1. Market Scope & Methodology
1.1 Market Definition
1.2 Objectives
1.3 Market Scope
1.4 Segmentation
1.4.1 Global Federated Learning In Healthcare Market, by Deployment Mode
1.4.2 Global Federated Learning In Healthcare Market, by End Use
1.4.3 Global Federated Learning In Healthcare Market, by Application
1.4.4 Global Federated Learning In Healthcare Market, by Geography
1.5 Methodology for the research
Chapter 2. Market at a Glance
2.1 Key Highlights
Chapter 3. Market Overview
3.1 Introduction
3.1.1 Overview
3.1.1.1 Market Composition and Scenario
3.2 Key Factors Impacting the Market
3.2.1 Market Drivers
3.2.2 Market Restraints
3.2.3 Market Opportunities
3.2.4 Market Challenges
Chapter 4. Competition Analysis - Global
4.1 Market Share Analysis, 2024
4.2 Porter Five Forces Analysis
Chapter 5. Global Federated Learning In Healthcare Market by Deployment Mode
5.1 Global On-Premise Market by Region
5.2 Global Cloud-Based Market by Region
Chapter 6. Global Federated Learning In Healthcare Market by End Use
6.1 Global Hospitals & Healthcare Providers Market by Region
6.2 Global Pharmaceutical & Biotechnology Companies Market by Region
6.3 Global Research Institutions Market by Region
6.4 Global Government & Regulatory Bodies Market by Region
Chapter 7. Global Federated Learning In Healthcare Market by Application
7.1 Global Drug Discovery & Development Market by Region
7.2 Global Medical Imaging Market by Region
7.3 Global Electronic Health Records (EHR) Analysis Market by Region
7.4 Global Remote Patient Monitoring Market by Region
7.5 Global Clinical Trials & Other Application Market by Region
Chapter 8. Global Federated Learning In Healthcare Market by Region
8.1 North America Federated Learning In Healthcare Market
8.1.1 North America Federated Learning In Healthcare Market by Deployment Mode
8.1.1.1 North America On-Premise Market by Country
8.1.1.2 North America Cloud-Based Market by Country
8.1.2 North America Federated Learning In Healthcare Market by End Use
8.1.2.1 North America Hospitals & Healthcare Providers Market by Country
8.1.2.2 North America Pharmaceutical & Biotechnology Companies Market by Country
8.1.2.3 North America Research Institutions Market by Country
8.1.2.4 North America Government & Regulatory Bodies Market by Country
8.1.3 North America Federated Learning In Healthcare Market by Application
8.1.3.1 North America Drug Discovery & Development Market by Country
8.1.3.2 North America Medical Imaging Market by Country
8.1.3.3 North America Electronic Health Records (EHR) Analysis Market by Country
8.1.3.4 North America Remote Patient Monitoring Market by Country
8.1.3.5 North America Clinical Trials & Other Application Market by Country
8.1.4 North America Federated Learning In Healthcare Market by Country
8.1.4.1 US Federated Learning In Healthcare Market
8.1.4.1.1 US Federated Learning In Healthcare Market by Deployment Mode
8.1.4.1.2 US Federated Learning In Healthcare Market by End Use
8.1.4.1.3 US Federated Learning In Healthcare Market by Application
8.1.4.2 Canada Federated Learning In Healthcare Market
8.1.4.2.1 Canada Federated Learning In Healthcare Market by Deployment Mode
8.1.4.2.2 Canada Federated Learning In Healthcare Market by End Use
8.1.4.2.3 Canada Federated Learning In Healthcare Market by Application
8.1.4.3 Mexico Federated Learning In Healthcare Market
8.1.4.3.1 Mexico Federated Learning In Healthcare Market by Deployment Mode
8.1.4.3.2 Mexico Federated Learning In Healthcare Market by End Use
8.1.4.3.3 Mexico Federated Learning In Healthcare Market by Application
8.1.4.4 Rest of North America Federated Learning In Healthcare Market
8.1.4.4.1 Rest of North America Federated Learning In Healthcare Market by Deployment Mode
8.1.4.4.2 Rest of North America Federated Learning In Healthcare Market by End Use
8.1.4.4.3 Rest of North America Federated Learning In Healthcare Market by Application
8.2 Europe Federated Learning In Healthcare Market
8.2.1 Europe Federated Learning In Healthcare Market by Deployment Mode
8.2.1.1 Europe On-Premise Market by Country
8.2.1.2 Europe Cloud-Based Market by Country
8.2.2 Europe Federated Learning In Healthcare Market by End Use
8.2.2.1 Europe Hospitals & Healthcare Providers Market by Country
8.2.2.2 Europe Pharmaceutical & Biotechnology Companies Market by Country
8.2.2.3 Europe Research Institutions Market by Country
8.2.2.4 Europe Government & Regulatory Bodies Market by Country
8.2.3 Europe Federated Learning In Healthcare Market by Application
8.2.3.1 Europe Drug Discovery & Development Market by Country
8.2.3.2 Europe Medical Imaging Market by Country
8.2.3.3 Europe Electronic Health Records (EHR) Analysis Market by Country
8.2.3.4 Europe Remote Patient Monitoring Market by Country
8.2.3.5 Europe Clinical Trials & Other Application Market by Country
8.2.4 Europe Federated Learning In Healthcare Market by Country
8.2.4.1 Germany Federated Learning In Healthcare Market
8.2.4.1.1 Germany Federated Learning In Healthcare Market by Deployment Mode
8.2.4.1.2 Germany Federated Learning In Healthcare Market by End Use
8.2.4.1.3 Germany Federated Learning In Healthcare Market by Application
8.2.4.2 UK Federated Learning In Healthcare Market
8.2.4.2.1 UK Federated Learning In Healthcare Market by Deployment Mode
8.2.4.2.2 UK Federated Learning In Healthcare Market by End Use
8.2.4.2.3 UK Federated Learning In Healthcare Market by Application
8.2.4.3 France Federated Learning In Healthcare Market
8.2.4.3.1 France Federated Learning In Healthcare Market by Deployment Mode
8.2.4.3.2 France Federated Learning In Healthcare Market by End Use
8.2.4.3.3 France Federated Learning In Healthcare Market by Application
8.2.4.4 Russia Federated Learning In Healthcare Market
8.2.4.4.1 Russia Federated Learning In Healthcare Market by Deployment Mode
8.2.4.4.2 Russia Federated Learning In Healthcare Market by End Use
8.2.4.4.3 Russia Federated Learning In Healthcare Market by Application
8.2.4.5 Spain Federated Learning In Healthcare Market
8.2.4.5.1 Spain Federated Learning In Healthcare Market by Deployment Mode
8.2.4.5.2 Spain Federated Learning In Healthcare Market by End Use
8.2.4.5.3 Spain Federated Learning In Healthcare Market by Application
8.2.4.6 Italy Federated Learning In Healthcare Market
8.2.4.6.1 Italy Federated Learning In Healthcare Market by Deployment Mode
8.2.4.6.2 Italy Federated Learning In Healthcare Market by End Use
8.2.4.6.3 Italy Federated Learning In Healthcare Market by Application
8.2.4.7 Rest of Europe Federated Learning In Healthcare Market
8.2.4.7.1 Rest of Europe Federated Learning In Healthcare Market by Deployment Mode
8.2.4.7.2 Rest of Europe Federated Learning In Healthcare Market by End Use
8.2.4.7.3 Rest of Europe Federated Learning In Healthcare Market by Application
8.3 Asia Pacific Federated Learning In Healthcare Market
8.3.1 Asia Pacific Federated Learning In Healthcare Market by Deployment Mode
8.3.1.1 Asia Pacific On-Premise Market by Country
8.3.1.2 Asia Pacific Cloud-Based Market by Country
8.3.2 Asia Pacific Federated Learning In Healthcare Market by End Use
8.3.2.1 Asia Pacific Hospitals & Healthcare Providers Market by Country
8.3.2.2 Asia Pacific Pharmaceutical & Biotechnology Companies Market by Country
8.3.2.3 Asia Pacific Research Institutions Market by Country
8.3.2.4 Asia Pacific Government & Regulatory Bodies Market by Country
8.3.3 Asia Pacific Federated Learning In Healthcare Market by Application
8.3.3.1 Asia Pacific Drug Discovery & Development Market by Country
8.3.3.2 Asia Pacific Medical Imaging Market by Country
8.3.3.3 Asia Pacific Electronic Health Records (EHR) Analysis Market by Country
8.3.3.4 Asia Pacific Remote Patient Monitoring Market by Country
8.3.3.5 Asia Pacific Clinical Trials & Other Application Market by Country
8.3.4 Asia Pacific Federated Learning In Healthcare Market by Country
8.3.4.1 China Federated Learning In Healthcare Market
8.3.4.1.1 China Federated Learning In Healthcare Market by Deployment Mode
8.3.4.1.2 China Federated Learning In Healthcare Market by End Use
8.3.4.1.3 China Federated Learning In Healthcare Market by Application
8.3.4.2 Japan Federated Learning In Healthcare Market
8.3.4.2.1 Japan Federated Learning In Healthcare Market by Deployment Mode
8.3.4.2.2 Japan Federated Learning In Healthcare Market by End Use
8.3.4.2.3 Japan Federated Learning In Healthcare Market by Application
8.3.4.3 India Federated Learning In Healthcare Market
8.3.4.3.1 India Federated Learning In Healthcare Market by Deployment Mode
8.3.4.3.2 India Federated Learning In Healthcare Market by End Use
8.3.4.3.3 India Federated Learning In Healthcare Market by Application
8.3.4.4 South Korea Federated Learning In Healthcare Market
8.3.4.4.1 South Korea Federated Learning In Healthcare Market by Deployment Mode
8.3.4.4.2 South Korea Federated Learning In Healthcare Market by End Use
8.3.4.4.3 South Korea Federated Learning In Healthcare Market by Application
8.3.4.5 Singapore Federated Learning In Healthcare Market
8.3.4.5.1 Singapore Federated Learning In Healthcare Market by Deployment Mode
8.3.4.5.2 Singapore Federated Learning In Healthcare Market by End Use
8.3.4.5.3 Singapore Federated Learning In Healthcare Market by Application
8.3.4.6 Malaysia Federated Learning In Healthcare Market
8.3.4.6.1 Malaysia Federated Learning In Healthcare Market by Deployment Mode
8.3.4.6.2 Malaysia Federated Learning In Healthcare Market by End Use
8.3.4.6.3 Malaysia Federated Learning In Healthcare Market by Application
8.3.4.7 Rest of Asia Pacific Federated Learning In Healthcare Market
8.3.4.7.1 Rest of Asia Pacific Federated Learning In Healthcare Market by Deployment Mode
8.3.4.7.2 Rest of Asia Pacific Federated Learning In Healthcare Market by End Use
8.3.4.7.3 Rest of Asia Pacific Federated Learning In Healthcare Market by Application
8.4 LAMEA Federated Learning In Healthcare Market
8.4.1 LAMEA Federated Learning In Healthcare Market by Deployment Mode
8.4.1.1 LAMEA On-Premise Market by Country
8.4.1.2 LAMEA Cloud-Based Market by Country
8.4.2 LAMEA Federated Learning In Healthcare Market by End Use
8.4.2.1 LAMEA Hospitals & Healthcare Providers Market by Country
8.4.2.2 LAMEA Pharmaceutical & Biotechnology Companies Market by Country
8.4.2.3 LAMEA Research Institutions Market by Country
8.4.2.4 LAMEA Government & Regulatory Bodies Market by Country
8.4.3 LAMEA Federated Learning In Healthcare Market by Application
8.4.3.1 LAMEA Drug Discovery & Development Market by Country
8.4.3.2 LAMEA Medical Imaging Market by Country
8.4.3.3 LAMEA Electronic Health Records (EHR) Analysis Market by Country
8.4.3.4 LAMEA Remote Patient Monitoring Market by Country
8.4.3.5 LAMEA Clinical Trials & Other Application Market by Country
8.4.4 LAMEA Federated Learning In Healthcare Market by Country
8.4.4.1 Brazil Federated Learning In Healthcare Market
8.4.4.1.1 Brazil Federated Learning In Healthcare Market by Deployment Mode
8.4.4.1.2 Brazil Federated Learning In Healthcare Market by End Use
8.4.4.1.3 Brazil Federated Learning In Healthcare Market by Application
8.4.4.2 Argentina Federated Learning In Healthcare Market
8.4.4.2.1 Argentina Federated Learning In Healthcare Market by Deployment Mode
8.4.4.2.2 Argentina Federated Learning In Healthcare Market by End Use
8.4.4.2.3 Argentina Federated Learning In Healthcare Market by Application
8.4.4.3 UAE Federated Learning In Healthcare Market
8.4.4.3.1 UAE Federated Learning In Healthcare Market by Deployment Mode
8.4.4.3.2 UAE Federated Learning In Healthcare Market by End Use
8.4.4.3.3 UAE Federated Learning In Healthcare Market by Application
8.4.4.4 Saudi Arabia Federated Learning In Healthcare Market
8.4.4.4.1 Saudi Arabia Federated Learning In Healthcare Market by Deployment Mode
8.4.4.4.2 Saudi Arabia Federated Learning In Healthcare Market by End Use
8.4.4.4.3 Saudi Arabia Federated Learning In Healthcare Market by Application
8.4.4.5 South Africa Federated Learning In Healthcare Market
8.4.4.5.1 South Africa Federated Learning In Healthcare Market by Deployment Mode
8.4.4.5.2 South Africa Federated Learning In Healthcare Market by End Use
8.4.4.5.3 South Africa Federated Learning In Healthcare Market by Application
8.4.4.6 Nigeria Federated Learning In Healthcare Market
8.4.4.6.1 Nigeria Federated Learning In Healthcare Market by Deployment Mode
8.4.4.6.2 Nigeria Federated Learning In Healthcare Market by End Use
8.4.4.6.3 Nigeria Federated Learning In Healthcare Market by Application
8.4.4.7 Rest of LAMEA Federated Learning In Healthcare Market
8.4.4.7.1 Rest of LAMEA Federated Learning In Healthcare Market by Deployment Mode
8.4.4.7.2 Rest of LAMEA Federated Learning In Healthcare Market by End Use
8.4.4.7.3 Rest of LAMEA Federated Learning In Healthcare Market by Application
Chapter 9. Company Profiles
9.1 GE HealthCare Technologies, Inc.
9.1.1 Company Overview
9.1.2 Financial Analysis
9.1.3 Segmental and Regional Analysis
9.1.4 Research & Development Expenses
9.1.5 Recent strategies and developments:
9.1.5.1 Product Launches and Product Expansions:
9.1.6 SWOT Analysis
9.2 Google LLC (Alphabet Inc.)
9.2.1 Company Overview
9.2.2 Financial Analysis
9.2.3 Segmental and Regional Analysis
9.2.4 Research & Development Expenses
9.2.5 SWOT Analysis
9.3 IBM Corporation
9.3.1 Company Overview
9.3.2 Financial Analysis
9.3.3 Regional & Segmental Analysis
9.3.4 Research & Development Expenses
9.3.5 SWOT Analysis
9.4 Microsoft Corporation
9.4.1 Company Overview
9.4.2 Financial Analysis
9.4.3 Segmental and Regional Analysis
9.4.4 Research & Development Expenses
9.4.5 SWOT Analysis
9.5 Siemens Healthineers AG (Siemens AG)
9.5.1 Company Overview
9.5.2 Financial Analysis
9.5.3 Segmental and Regional Analysis
9.5.4 Research & Development Expense
9.5.5 SWOT Analysis
9.6 Medtronic PLC
9.6.1 Company overview
9.6.2 Financial Analysis
9.6.3 Segmental and Regional Analysis
9.6.4 Research & Development Expenses
9.6.5 SWOT Analysis
9.7 NVIDIA Corporation
9.7.1 Company Overview
9.7.2 Financial Analysis
9.7.3 Segmental and Regional Analysis
9.7.4 Research & Development Expenses
9.7.5 SWOT Analysis
9.8 Intel Corporation
9.8.1 Company Overview
9.8.2 Financial Analysis
9.8.3 Segmental and Regional Analysis
9.8.4 Research & Development Expenses
9.8.5 SWOT Analysis
9.9 Health Catalyst, Inc.
9.9.1 Company Overview
9.9.2 Financial Analysis
9.9.3 Segmental and Regional Analysis
9.9.4 Research & Development Expenses
9.9.5 SWOT Analysis
9.10. Owkin
9.10.1 Company Overview
Chapter 10. Winning Imperatives of Federated Learning In Healthcare Market
