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医疗保健数位孪生的全球市场-依治疗领域,数位孪生类型,应用领域,最终用户和主要区域划分:行业趋势和全球预测（截至 2035 年）
市场调查报告书
商品编码
1479823

医疗保健数位孪生的全球市场-依治疗领域,数位孪生类型,应用领域,最终用户和主要区域划分:行业趋势和全球预测（截至 2035 年）

Digital Twins in Healthcare Market: Industry Trends and Global Forecasts, till 2035 - Distribution by Therapeutic Area, Type of Digital Twin, Areas of Application, End Users and Key Geographical Regions
出版日期: | 出版商: Roots Analysis | 英文 210 Pages | 商品交期: 最快1-2个工作天内

价格

预计到 2024 年，医疗保健领域数位孪生的市场规模将达到 19 亿美元，并预计在 2024 年至 2035 年的预测期内将以 30% 的复合年增长率增长。

数位孪生是指透过即时资料或模拟模型产生的实体实体或系统的虚拟副本。这些数位复製品在製药领域具有广泛的应用，包括加快临床试验、在更广泛的受众中进行模拟试验、提高治疗效果以及降低药物设计和测试成本。工业 4.0 技术的兴起促进了无缝数据整合以及实体资产、流程甚至人体生理学虚拟副本的创建，从而刺激了医疗保健领域数位孪生市场的成长。

最近的一项调查显示，医疗保健公司高层预计製药公司的投资将大幅增加，预计未来三年将增加超过65%。数位孪生在加快临床试验时间表和促进涉及大量人群的模拟研究方面也发挥着至关重要的作用。值得注意的是，研究团队设计了一种风险评估工具，利用虚拟模拟、机器学习和其他方法来估计各种药物的心臟毒性潜力。这项创新可能有助于减轻每年估计 25 亿美元的药物设计和测试成本。此类技术可以部署预测性维护策略、模拟製药流程和即时监控，增加了製药公司探索潜力的兴趣。近 90% 的医疗保健主管将数位孪生视为促进组织内不同部门之间协作的重要技术。此外，对虚拟模拟、个人化医疗和预测性维护不断增长的需求预计将推动製药业数位孪生应用的进一步探索。

该报告调查了全球医疗保健数位孪生市场，并依治疗领域、数位孪生类型、应用领域、最终用户、地区和进入市场的公司提供了市场概况、趋势等。

目录

第一章 前言

第二章 研究方法

第 3 章. 经济和其他专案特定考虑因素

  • 章节概述
  • 市场动态

第 4 章执行摘要

第 5 章 简介

第六章 市场状况

  • 章节概述
  • 医疗保健中的数位孪生：市场情势
  • 医疗保健中的数位孪生：开发者格局

第 7 章主要见解

第八章企业竞争分析

  • 章节概述
  • 假设和关键参数
  • 调查方法
  • 医疗保健中的数位孪生：企业竞争分析

第九章 公司详细简介

  • 章节概述
  • BigBear.ai
  • Certara
  • Dassault Systemes
  • NavvTrack
  • Unlearn.ai

第十章 公司简介一览表

  • 章节概述
  • 总部位于北美的公司
  • 总部位于欧洲的公司
  • 总部位于亚洲的公司

第 11 章 伙伴关係与合作

  • 章节概述
  • 医疗保健中的数位孪生：伙伴关係与协作

第十二章 融资与投资分析

  • 章节概述
  • 融资类型
  • 医疗保健中的数位孪生：资金和投资清单
  • 结论

第十三章 BERKUS 新创公司评估分析

第14章市场影响分析：驱动因素、限制因素、机会、课题

  • 章节概述
  • 市场驱动力
  • 市场限制
  • 市场机会
  • 市场课题
  • 结论

第十五章医疗保健数位孪生的全球市场

  • 章节概述
  • 假设和研究方法
  • 医疗保健数位孪生的全球市场、历史趋势（2018-2023）和预测（2024-2035）
  • 主要市场区隔

第 16 章医疗保健数位孪生市场（依治疗领域）

第十七章医疗保健数位孪生市场（依数位孪生类型）

第 18 章医疗保健数位孪生市场（依应用领域）

第 19 章医疗保健数位孪生市场（依最终用户）

第 20 章 医疗保健数位孪生市场（依地区）

第21章结论

第22章高阶主管洞察

第23章附录一：表格数据

第二十四章 附录二：公司与组织名单

Product Code: RA100482

Digital Twins in Healthcare Market: Industry Trends and Global Forecasts, till 2035 - Distribution by Therapeutic Area (Cardiovascular Disorders, Metabolic Disorders, Orthopedic Disorders, and Other Disorders), Type of Digital Twin (Process Twins, System Twins, Whole Body Twins and Body Part Twins), Areas of Application (Asset / Process Management, Personalized Treatment, Surgical Planning, Diagnosis and Other Applications), End Users (Pharmaceutical Companies, Medical Device Manufacturers, Healthcare Providers, Patients and Other End Users) and Key Geographical Regions (North America, Europe, Asia, Latin America, Middle East and North Africa, and Rest of the World)

The Digital Twins in Healthcare Market is valued at USD 1.9 billion in 2024 growing at a CAGR of 30% during the forecast period 2024-2035.

Digital twins represent virtual counterparts of physical entities or systems, generated through real-time data and simulation models. These digital replicas offer diverse applications within the pharmaceutical sector, including expediting clinical trials, conducting simulated studies for broader demographics, enhancing treatment efficacy, and reducing costs in drug design and testing. The rise of Industry 4.0 technologies has fueled growth in the healthcare sector's digital twins market, facilitating seamless data integration and the creation of virtual replicas of physical assets, processes, and even human physiology.

A recent study suggests that healthcare executives foresee a significant uptick in investment from pharmaceutical firms, with a projected increase of over 65% in the next three years. Digital twins also play a pivotal role in accelerating clinical trial timelines and facilitating simulated studies involving larger populations. Notably, a team of researchers has devised a risk assessment tool utilizing virtual simulation, machine learning, and other methodologies to estimate the cardiotoxicity potential of various drugs. This innovation stands to mitigate some of the expenses associated with the estimated $2.5 billion spent annually on drug design and testing. Such technologies empower organizations to deploy predictive maintenance strategies, simulate pharmaceutical processes, and enable real-time monitoring, prompting increased interest from pharmaceutical companies in exploring their potential. Nearly 90% of healthcare executives recognize digital twins as indispensable technologies for fostering collaboration among various units within their organizations. Moreover, the escalating demand for virtual simulation, personalized medicine, and predictive maintenance is poised to fuel further exploration of digital twin applications in the pharmaceutical industry.

Key Market Segments

Therapeutic Area

Cardiovascular Disorders

Metabolic Disorders

Orthopedic Disorders

Other Disorders

Type of Digital Twin

Process Twins

System Twins

Whole Body Twins

Body Part Twins

Area of Application

Asset / Process Management

Personalized Treatment

Surgical Planning

Diagnosis

Other Applications

End Users

Pharmaceutical Companies

Medical Device Manufacturers

Healthcare Providers

Patients

Other End Users

Key Geographical Regions

North America

Europe

Asia

Latin America

Middle East and North Africa

Rest of the World

Research Coverage:

This chapter offers a succinct overview of key digital twin concepts, including a discussion on the diverse types of digital twins and how they are primarily utilized in healthcare. Additionally, it delves into recent advancements observed within this market sector.

This chapter provides an in-depth examination of the current market landscape concerning entities involved in digital twin development. It includes details such as their establishment year, company size, and headquarters location. Furthermore, it presents a comprehensive assessment of the broader digital twins market within healthcare, focusing on various pertinent parameters. The assessment encompasses the status of development, categorized as commercially available or under development, and explores therapeutic areas like cardiovascular disorders, metabolic disorders, orthopedic disorders, and others. Additionally, it examines diverse areas of application such as asset/process management, personalized treatment, surgical planning, diagnosis, health monitoring, clinical trials, and medical training. Moreover, the analysis considers the type of technology utilized, including artificial intelligence, virtual reality, augmented reality, blockchain, and others. It also distinguishes between different types of digital twins, such as body part twin, whole body twin, process twin, and system twin. Furthermore, the evaluation extends to end users, including healthcare providers, pharmaceutical companies, medical device manufacturers, patients, and others.

This section provides an extensive examination of the digital twins market within healthcare, focusing on current trends. It utilizes five schematic representations to illustrate key aspects: areas of application and development status, technology types and digital twin categories, end user types and digital twin categories, application areas and headquarters locations, and company sizes and headquarters locations. These representations offer valuable insights into the dynamic landscape of digital twins in healthcare, highlighting trends and key factors shaping the market.

A comprehensive analysis of competitiveness among stakeholders engaged in the production and development of digital twins in the healthcare sector is presented in this section. It evaluates various parameters including years of experience, portfolio strength (encompassing the number of products, development status, areas of application, technology employed, end users targeted, and types of twins created), partnership effectiveness (measured by the number of partnerships, their duration, and the nature of collaborations), and funding capacity (evaluated by the frequency, amount, timing, and type of funding received). Through this analysis, a detailed understanding of the competitive landscape in the healthcare digital twins market is provided, shedding light on the strengths and capabilities of key players in the industry.

Comprehensive company profiles of various prominent players that are currently involved in the digital twins in healthcare market. Each company profile features a brief overview of the company (including information on its year of establishment, number of employees, location of headquarters and key members of the executive team), financial information (if available), details related to its recent developments and an informed future outlook.

A thorough analysis is conducted on the partnerships forged among diverse stakeholders within the timeframe of 2018-2023. This analysis encompasses a spectrum of collaborative endeavors including acquisitions, mergers, commercialization agreements, licensing agreements, product development agreements, research agreements, service agreements, service alliances, technology development agreements, technology integration agreements, technology utilization agreements, and other forms of partnerships. Through this examination, a comprehensive understanding of the evolving landscape of collaborations within the specified period is provided, shedding light on the dynamics and implications of these strategic alliances in the industry.

This section conducts an analysis of funding and investments garnered by players operating in the digital twin domain within the timeframe of 2018-2023. It encompasses various sources of financial backing, such as grants, seed funding, venture capital investments, initial public offerings, secondary offerings, private placements, debt financing, and other equity-related mechanisms. Through this examination, insights into the financial landscape of the digital twin sector during the specified period are provided, highlighting trends, patterns, and significant investment activities within the industry.

A proprietary analysis is conducted to assess start-ups operating within this market segment. This evaluation entails assigning monetary values to differentiating factors using the Berkus start-up valuation parameters. These parameters include the strength of the business idea, the development of a prototype, the experience of the management team, and the strategic relationships established by market players. Through this analysis, a quantitative assessment of start-up competitiveness is provided, offering insights into their potential for growth and success within the market.

This section offers a comprehensive analysis of the factors influencing the growth trajectory of the digital twin market within the healthcare sector. It encompasses the identification and analysis of key drivers propelling market expansion, potential restraints hindering growth, emerging opportunities for market advancement, and existing challenges that need to be addressed. Through this in-depth examination, a holistic understanding of the dynamics shaping the digital twin market in healthcare is provided, facilitating informed decision-making and strategic planning for stakeholders within the industry.

Comprehensive projection of the current market size, opportunity and the future growth potential of the digital twins in healthcare market, over the next decade. Based on multiple parameters, likely adoption trends and through primary validations, we have provided an informed estimate on the market evolution during the forecast period 2024-2035. The report also features likely distribution of the current and forecasted opportunity. Further, in order to account for future uncertainties and to add robustness to our model, we have provided three forecast scenarios, namely conservative, base and optimistic scenarios, representing different tracks of the industry's growth.

Elaborate projections of the current and future digital twins in healthcare market across different therapeutic areas, such as cardiovascular disorders, metabolic disorders, orthopedic disorders, and other disorders.

Detailed projection of the current and future digital twin in healthcare market across different types of digital twins, such as process twins, system twins, whole body twins and body part twins.

Comprehensive projection of the current and future digital twins in healthcare market across different areas of applications, such as asset / process management, personalized treatment, surgical planning, diagnosis and other applications.

Comprehensive projection of the current and future digital twins in healthcare market across different end users, such as pharmaceutical companies, medical device manufacturers, healthcare providers, patients and other end users.

Elaborate projection of the current and future digital twin in healthcare market across key geographical regions, such as North America, Europe, Asia, Latin America, Middle East and North Africa and Rest of the World.

Key Benefits of Buying this Report

The report offers market leaders and newcomers valuable insights into revenue estimations for both the overall market and its sub-segments.

Stakeholders can utilize the report to enhance their understanding of the competitive landscape, allowing for improved business positioning and more effective go-to-market strategies.

The report provides stakeholders with a pulse on the Digital Twins in Healthcare Market, furnishing them with essential information on significant market drivers, barriers, opportunities, and challenges.

Leading Market Companies

BigBear.ai

Certara

Dassault Systemes

DEO

Mesh Bio

NavvTrack

OnScale

Phesi

PrediSurge

SingHealth

Twin Health

Unlearn

Verto

VictoryXR

Virtonomy

TABLE OF CONTENTS

1. PREFACE

  • 1.1. Digital Twins in Healthcare Market: Market Overview
  • 1.2. Market Share Insights
  • 1.3. Market Segmentation Overview
  • 1.4. Key Market Insights
  • 1.5. Report Coverage
  • 1.6. Key Questions Answered
  • 1.7. Chapter Outlines

2. RESEARCH METHODOLOGY

  • 2.1. Chapter Overview
  • 2.2. Research Assumptions
  • 2.3. Project Methodology
  • 2.4. Forecast Methodology
  • 2.5. Robust Quality Control
  • 2.6. Key Market Segmentations
  • 2.7. Key Considerations
    • 2.7.1. Demographics
    • 2.7.2. Economic Factors
    • 2.7.3. Government Regulations
    • 2.7.4. Supply Chain
    • 2.7.5. COVID Impact / Related Factors
    • 2.7.6. Market Access
    • 2.7.7. Healthcare Policies
    • 2.7.8. Industry Consolidation

3. ECONOMIC AND OTHER PROJECT SPECIFIC CONSIDERATIONS

  • 3.1. Chapter Overview
  • 3.2. Market Dynamics
    • 3.2.1. Time Period
      • 3.2.1.1. Historical Trends
      • 3.2.1.2. Current and Forecasted Estimates
    • 3.2.2. Currency Coverage
      • 3.2.2.1. Overview of Major Currencies Affecting the Market
      • 3.2.2.2. Impact of Currency Fluctuations on the Industry
    • 3.2.3. Foreign Exchange Impact
      • 3.2.3.1. Evaluation of Foreign Exchange Rates and their Impact on Market
      • 3.2.3.2. Strategies for Mitigating Foreign Exchange Risk
    • 3.2.4. Recession
      • 3.2.4.1. Historical Trends Analysis of Past Recessions and Lessons Learnt
      • 3.2.4.2. Assessment of Current Economic Conditions and Potential Impact on the Market
    • 3.2.5. Inflation
      • 3.2.5.1. Measurement and Analysis of Inflationary Pressures in the Economy
      • 3.2.5.2. Potential Impact of Inflation on the Market Evolution

4. EXECUTIVE SUMMARY

5. INTRODUCTION

  • 5.1. Chapter Overview
  • 5.2. Overview of Digital Twins in Healthcare
  • 5.3. Types of Digital Twins Used in Healthcare
    • 5.3.1. System Twin
    • 5.3.2. Process Twin
    • 5.3.3. Human Digital Twin
  • 5.4. Applications of Digital Twins in the Healthcare Domain
    • 5.4.1. Asset / Process Management
    • 5.4.2. Clinical Trial Evaluation
    • 5.4.3. Personalized Medicine
    • 5.4.4. Surgical Planning
  • 5.5. Challenges Associated with the Adoption of Digital Twins
  • 5.6. Concluding Remarks

6. MARKET LANDSCAPE

  • 6.1. Chapter Overview
  • 6.2. Digital Twins in Healthcare: Overall Market Landscape
    • 6.2.1. Analysis by Development Status
    • 6.2.2. Analysis by Therapeutic Area
    • 6.2.3. Analysis by Area of Application
    • 6.2.4. Analysis by Type of Technology Used
    • 6.2.5. Analysis by End Users
    • 6.2.6. Analysis by Type of Digital Twin
  • 6.3. Digital Twins in Healthcare: Developer Landscape
    • 6.3.1. Analysis by Year of Establishment
    • 6.3.2. Analysis by Company Size
    • 6.3.3. Analysis by Location of Headquarters

7. KEY INSIGHTS

  • 7.1. Chapter Overview
  • 7.2. Analysis by Area of Application and Development Status
  • 7.3. Analysis by Type of Technology Used and Type of Digital Twin
  • 7.4. Analysis by Type of End User and Type of Digital Twin
  • 7.5. Analysis by Location of Headquarters and Area of Application
  • 7.6. Analysis by Company Size and Location of Headquarters

8. COMPANY COMPETITIVENESS ANALYSIS

  • 8.1. Chapter Overview
  • 8.2. Assumptions and Key Parameters
  • 8.3. Methodology
  • 8.4. Digital Twins in Healthcare: Company Competitiveness Analysis
    • 8.4.1. Company Competitiveness Analysis: Benchmarking of Portfolio Strength
    • 8.4.2. Company Competitiveness Analysis: Benchmarking of Partnership Activity
    • 8.4.3. Company Competitiveness Analysis: Benchmarking of Funding Activity
    • 8.4.4. Company Competitiveness Analysis: Players Based in North America
    • 8.4.5. Company Competitiveness Analysis: Players Based in Europe
    • 8.4.6. Company Competitiveness Analysis: Players Based in Asia and Rest of the World

9. DETAILED COMPANY PROFILES

  • 9.1. Chapter Overview
  • 9.2. BigBear.ai
    • 9.2.1. Company Overview
    • 9.2.2. Financial Information
    • 9.2.3. Recent Developments and Future Outlook
  • 9.3. Certara
    • 9.3.1. Company Overview
    • 9.3.2. Financial Information
    • 9.3.3. Recent Developments and Future Outlook
  • 9.4. Dassault Systemes
    • 9.4.1. Company Overview
    • 9.4.2. Financial Information
    • 9.4.3. Recent Developments and Future Outlook
  • 9.5. NavvTrack
    • 9.5.1. Company Overview
    • 9.5.2. Recent Developments and Future Outlook
  • 9.6. Unlearn.ai
    • 9.6.1. Company Overview
    • 9.6.2. Recent Developments and Future Outlook

10. TABULATED COMPANY PROFILES

  • 10.1. Chapter Overview
  • 10.2. Players Based in North America
    • 10.2.1. OnScale
    • 10.2.2. Phesi
    • 10.2.3. Twin Health
    • 10.2.4. Verto
    • 10.2.5. VictoryXR
  • 10.3. Players Based in Europe
    • 10.3.1. DEO
    • 10.3.2. PrediSurge
    • 10.3.3. Virtonomy
  • 10.4. Players Based in Asia
    • 10.4.1. Mesh Bio
    • 10.4.2. SingHealth

11. PARTNERSHIPS AND COLLABORATIONS

  • 11.1. Chapter Overview
  • 11.2. Digital Twins in Healthcare: Partnerships and Collaborations
    • 11.2.1. Partnership Models
    • 11.2.2. List of Partnerships and Collaborations
    • 11.2.3. Analysis by Year of Partnership
    • 11.2.4. Analysis by Type of Partnership
    • 11.2.5. Analysis by Year and Type of Partnership
    • 11.2.6. Analysis by Type of Partnership and Company Size
    • 11.2.7. Most Active Players: Analysis by Number of Partnerships
    • 11.2.8. Local and International Agreements
    • 11.2.9. Intercontinental and Intracontinental Agreements

12. FUNDING AND INVESTMENTS ANALYSIS

  • 12.1. Chapter Overview
  • 12.2. Types of Funding
  • 12.3. Digital Twins in Healthcare: List of Funding and Investments
    • 12.3.1. Analysis by Number of Funding Instances
    • 12.3.2. Analysis by Amount Invested
    • 12.3.3. Analysis by Type of Funding
    • 12.3.4. Analysis by Geography
    • 12.3.5. Most Active Players: Analysis by Number of Funding Instances
    • 12.3.6. Most Active Players: Analysis by Amount of Funding
    • 12.3.7. Most Active Investors: Analysis by Number of Funding Instances
  • 12.4. Concluding Remarks

13. BERKUS START-UP VALUATION ANALYSIS

  • 13.1. Chapter Overview
  • 13.2. Key Assumptions and Methodology
  • 13.3. Berkus Start-Up Valuation: Total Valuation of Players
  • 13.4. Digital Twins in Healthcare: Benchmarking of Berkus Start-Up Valuation Parameters
    • 13.4.1. AI Body: Benchmarking of Berkus Start-Up Valuation Parameters
    • 13.4.2. AnatoScope: Benchmarking of Berkus Start-Up Valuation Parameters
    • 13.4.3. Antleron: Benchmarking of Berkus Start-Up Valuation Parameters
    • 13.4.4. EmbodyBio: Benchmarking of Berkus Start-Up Valuation Parameters
    • 13.4.5. Klinik Sankt Moritz: Benchmarking of Berkus Start-Up Valuation Parameters
    • 13.4.6. KYDEA: Benchmarking of Berkus Start-Up Valuation Parameters
    • 13.4.7. MAI: Benchmarking of Berkus Start-Up Valuation Parameters
    • 13.4.8. Mindback AI: Benchmarking of Berkus Start-Up Valuation Parameters
    • 13.4.9. Neo PLM: Benchmarking of Berkus Start-Up Valuation Parameters
    • 13.4.10. Twinsight: Benchmarking of Berkus Start-Up Valuation Parameters
    • 13.4.11. Yokogawa Insilico Biotechnology: Benchmarking of Berkus Start-Up Valuation Parameters
  • 13.5. Digital Twins in Healthcare: Benchmarking of Players
    • 13.5.1. Sound Idea: Benchmarking of Players
    • 13.5.2. Prototype: Benchmarking of Players
    • 13.5.3. Management Experience: Benchmarking of Players
    • 13.5.4. Strategic Relationships: Benchmarking of Players
    • 13.5.5. Total Valuation: Benchmarking of Players

14. MARKET IMPACT ANALYSIS: DRIVERS, RESTRAINTS, OPPORTUNITIES AND CHALLENGES

  • 14.1. Chapter Overview
  • 14.2. Market Drivers
  • 14.3. Market Restraints
  • 14.4. Market Opportunities
  • 14.5. Market Challenges
  • 14.6. Conclusion

15. GLOBAL DIGITAL TWIN IN HEALTHCARE MARKET

  • 15.1. Chapter Overview
  • 15.2. Assumptions and Methodology
  • 15.3. Global Digital Twin in Healthcare Market, Historical Trends (2018-2023) and Forecasted Estimates (2024-2035)
    • 15.3.1. Scenario Analysis
  • 15.4. Key Market Segmentations

16. DIGITAL TWIN IN HEALTHCARE MARKET, BY THERAPEUTIC AREA

  • 16.1. Chapter Overview
  • 16.2. Key Assumptions and Methodology
  • 16.3. Cardiovascular Disorders: Historical Trends (2018-2023) and Forecasted Estimates (2024-2035)
  • 16.4. Metabolic Disorders: Historical Trends (2018-2023) and Forecasted Estimates (2024-2035)
  • 16.5. Orthopedic Disorders: Historical Trends (2018-2023) and Forecasted Estimates (2024-2035)
  • 16.6. Other Disorders: Historical Trends (2018-2023) and Forecasted Estimates (2024-2035)
  • 16.7. Data Triangulation and Validation

17. DIGITAL TWIN IN HEALTHCARE MARKET, BY TYPE OF DIGITAL TWINS

  • 17.1. Chapter Overview
  • 17.2. Key Assumptions and Methodology
  • 17.3. Process Twins: Historical Trends (2018-2023) and Forecasted Estimates (2024-2035)
  • 17.4. System Twins: Historical Trends (2018-2023) and Forecasted Estimates (2024-2035)
  • 17.5. Whole Body Twins: Historical Trends (2018-2023) and Forecasted Estimates (2024-2035)
  • 17.6. Body Part Twins: Historical Trends (2018-2023) and Forecasted Estimates (2024-2035)
  • 17.7. Data Triangulation and Validation

18. DIGITAL TWIN IN HEALTHCARE MARKET, BY AREA OF APPLICATION

  • 18.1. Chapter Overview
  • 18.2. Key Assumptions and Methodology
  • 18.3. Asset / Process Management: Historical Trends (2018-2023) and Forecasted Estimates (2024-2035)
  • 18.4. Personalized Treatment: Historical Trends (2018-2023) and Forecasted Estimates (2024-2035)
  • 18.5. Surgical Planning: Historical Trends (2018-2023) and Forecasted Estimates (2024-2035)
  • 18.6. Diagnosis: Historical Trends (2018-2023) and Forecasted Estimates (2024-2035)
  • 18.7. Other Applications: Historical Trends (2018-2023) and Forecasted Estimates (2024-2035)
  • 18.8. Data Triangulation and Validation

19. DIGITAL TWIN IN HEALTHCARE MARKET, BY END USERS

  • 19.1. Chapter Overview
  • 19.2. Key Assumptions and Methodology
  • 19.3. Pharmaceutical Companies: Historical Trends (2018-2023) and Forecasted Estimates (2024-2035)
  • 19.4. Medical Device Manufacturers: Historical Trends (2018-2023) and Forecasted Estimates (2024-2035)
  • 19.5. Healthcare Providers: Historical Trends (2018-2023) and Forecasted Estimates (2024-2035)
  • 19.6. Patients: Historical Trends (2018-2023) and Forecasted Estimates (2024-2035)
  • 19.7. Other End Users: Historical Trends (2018-2023) and Forecasted Estimates (2024-2035)
  • 19.8. Data Triangulation and Validation

20. DIGITAL TWIN IN HEALTHCARE MARKET, BY GEOGRAPHY

  • 20.1. Chapter Overview
  • 20.2. Key Assumptions and Methodology
  • 20.3. North America: Historical Trends (2018-2023) and Forecasted Estimates (2024-2035)
    • 20.3.1. US: Historical Trends (2018-2023) and Forecasted Estimates (2024-2035)
    • 20.3.2. Canada: Historical Trends (2018-2023) and Forecasted Estimates (2024-2035)
  • 20.4. Europe: Historical Trends (2018-2023) and Forecasted Estimates (2024-2035)
    • 20.4.1. France: Historical Trends (2018-2023) and Forecasted Estimates (2024-2035)
    • 20.4.2. Germany: Historical Trends (2018-2023) and Forecasted Estimates (2024-2035)
    • 20.4.3. Italy: Historical Trends (2018-2023) and Forecasted Estimates (2024-2035)
    • 20.4.4. Spain: Historical Trends (2018-2023) and Forecasted Estimates (2024-2035)
    • 20.4.5. UK: Historical Trends (2018-2023) and Forecasted Estimates (2024-2035)
    • 20.4.6. Rest of Europe: Historical Trends (2018-2023) and Forecasted Estimates (2024-2035)
  • 20.5. Asia: Historical Trends (2018-2023) and Forecasted Estimates (2024-2035)
    • 20.5.1. China: Historical Trends (2018-2023) and Forecasted Estimates (2024-2035)
    • 20.5.2. India: Historical Trends (2018-2023) and Forecasted Estimates (2024-2035)
    • 20.5.3. Japan: Historical Trends (2018-2023) and Forecasted Estimates (2024-2035)
    • 20.5.4. Singapore: Historical Trends (2018-2023) and Forecasted Estimates (2024-2035)
    • 20.5.5. South Korea: Historical Trends (2018-2023) and Forecasted Estimates (2024-2035)
    • 20.5.6. Rest of Asia: Historical Trends (2018-2023) and Forecasted Estimates (2024-2035)
  • 20.6. Latin America: Historical Trends (2018-2023) and Forecasted Estimates (2024-2035)
    • 20.6.1. Brazil: Historical Trends (2018-2023) and Forecasted Estimates (2024-2035)
  • 20.7. Middle East and North Africa: Historical Trends (2018-2023) and Forecasted Estimates (2024-2035)
    • 20.7.1. UAE: Historical Trends (2018-2023) and Forecasted Estimates (2024-2035)
  • 20.8. Rest of the World: Historical Trends (2018-2023) and Forecasted Estimates (2024-2035)
    • 20.8.1. Australia: Historical Trends (2018-2023) and Forecasted Estimates (2024-2035)
    • 20.8.2. New Zealand: Historical Trends (2018-2023) and Forecasted Estimates (2024-2035)
  • 20.9. Data Triangulation and Validation

21. CONCLUSION

22. EXECUTIVE INSIGHTS

  • 22.1. Chapter Overview
  • 22.2. Dassault Systemes
    • 22.2.1. Company Snapshot
    • 22.2.2. Interview Transcript: Barbara Holtz, Business Consultant
  • 22.3. TwInsight
    • 22.3.1. Company Snapshot
    • 22.3.2. Interview Transcript: Marek Bucki, Co-Founder and Chief Scientific Officer
  • 22.4. Unlearn.AI
    • 22.4.1. Company Snapshot
    • 22.4.2. Interview Transcript: Andrew Stelzer, Business Development Executive
  • 22.5. Yokogawa Insilico Biotechnology
    • 22.5.1. Company Snapshot
    • 22.5.2. Interview Transcript: Klaus Mauch, Managing Director and Chief Executive Officer

23. APPENDIX I: TABULATED DATA

24. APPENDIX II: LIST OF COMPANIES AND ORGANIZATIONS

List of Tables

  • Table 6.1 Digital Twins in Healthcare: Information on Development Status
  • Table 6.2 Digital Twins in Healthcare: Information on Therapeutic Area
  • Table 6.3 Digital Twins in Healthcare: Information on Areas of Application
  • Table 6.4 Digital Twins in Healthcare: Information on Type of Technology Used
  • Table 6.5 Digital Twins in Healthcare: Information on End Users
  • Table 6.6 Digital Twins in Healthcare: Information on Type of Digital Twin
  • Table 6.7 Digital Twins Developers: Information on Year of Establishment, Company Size, Location of Headquarters, Region of Headquarters and Number of Products
  • Table 9.1 List of Companies Profiled
  • Table 9.2 BigBear.ai: Company Overview
  • Table 9.3 BigBear.ai: Recent Developments and Future Outlook
  • Table 9.4 Certara: Company Overview
  • Table 9.5 Certara: Recent Developments and Future Outlook
  • Table 9.6 Dassault Systemes: Company Overview
  • Table 9.7 Dassault Systemes: Recent Developments and Future Outlook
  • Table 9.8 NavvTrack: Company Overview
  • Table 9.9 NavvTrack: Recent Developments and Future Outlook
  • Table 9.10 Unlearn.ai: Company Overview
  • Table 9.11 Unlearn.ai: Recent Developments and Future Outlook
  • Table 10.1 List of Companies Profiled
  • Table 10.2 OnScale: Company Overview
  • Table 10.3 Phesi: Company Overview
  • Table 10.5 Twin Health: Company Overview
  • Table 10.6 Verto: Company Overview
  • Table 10.7 VictoryXR: Recent Developments and Future Outlook
  • Table 10.8 DEO: Company Overview
  • Table 10.9 PrediSurge: Company Overview
  • Table 10.10 Virtonomy: Company Overview
  • Table 10.11 Mesh Bio: Company Overview
  • Table 10.12 SingHealth: Company Overview
  • Table 11.1 Digital Twins in Healthcare: List of Partnerships and Collaborations, 2018-2023
  • Table 11.2 Partnerships and Collaborations: Information on Type of Agreement (Country-wise and Continent-wise), 2018-2023
  • Table 12.1 Funding and Investments: Information on Year of Investment, Type of Funding, Amount and Investor, 2018-2023
  • Table 12.2 Funding and Investment Analysis: Regional Distribution by Total Amount Invested, 2018-2023
  • Table 13.1 Berkus Start-Up Valuation: Total Valuation of Players
  • Table 22.1 Dassault Systemes: Company Snapshot
  • Table 22.2 TwInsight: Company Snapshot
  • Table 22.3 Unlearn.AI: Company Snapshot
  • Table 22.4 Yokogawa Insilico Biotechnology: Company Snapshot
  • Table 23.1 Digital Twins: Distribution by Development Status
  • Table 23.2 Digital Twins: Distribution by Therapeutic Area
  • Table 23.3 Digital Twins: Distribution by Areas of Application
  • Table 23.4 Digital Twins: Distribution by Type of Technology Used
  • Table 23.5 Digital Twins: Distribution by End Users
  • Table 23.6 Digital Twins in Healthcare: Distribution by Type of Digital Twin
  • Table 23.7 Digital Twin Developers: Distribution by Year of Establishment
  • Table 23.8 Digital Twin Developers: Distribution by Company Size
  • Table 23.9 Digital Twin Developers: Distribution by Location of Headquarters
  • Table 23.10 BigBear.ai: Annual Revenues, 2021-Q3 2023 (USD Million)
  • Table 23.11 Certara: Annual Revenues, 2020-Q3 2023 (USD Million)
  • Table 23.12 Dassault Systemes: Annual Revenues, 2019-Q3 2023 (EUR Billion)
  • Table 23.13 Partnerships and Collaborations: Cumulative Year-wise Trend, 2018-2023
  • Table 23.14 Partnerships and Collaborations: Distribution by Type of Partnership
  • Table 23.15 Partnerships and Collaborations: Distribution by Year and Type of Partnership
  • Table 23.16 Partnerships and Collaborations: Distribution by Type of Partnership and Company Size
  • Table 23.17 Most Active Players: Distribution by Number of Partnerships
  • Table 23.18 Partnerships and Collaborations: Local and International Agreements
  • Table 23.19 Partnerships and Collaborations: Intercontinental and Intracontinental Agreements
  • Table 23.20 Funding and Investment Analysis: Cumulative Year-wise Trend, 2018-2023
  • Table 23.21 Funding and Investment Analysis: Cumulative Amount Invested (USD Million), 2018-2023
  • Table 23.22 Funding and Investment Analysis: Distribution of Instances by Type of Funding, 2018-2023
  • Table 23.23 Funding and Investment Analysis: Year-Wise Distribution by Type of Funding, 2018-2023
  • Table 23.24 Funding and Investment Analysis: Distribution of Total Amount Invested (USD Million) by Type of Funding, 2018-2023
  • Table 23.25 Funding and Investment Analysis: Distribution by Geography
  • Table 23.26 Most Active Players: Distribution by Number of Funding Instances, 2018-2023
  • Table 23.27 Most Active Players: Distribution by Amount Raised (USD Million), 2018-2023
  • Table 23.28 Global Digital Twins in Healthcare Market, Historical Trends, 2018-2023 (USD Billion)
  • Table 23.29 Global Digital Twins in Healthcare Market, Forecasted Estimates, till 2035, Conservative, Base and Optimistic Scenario (USD Billion)
  • Table 23.30 Global Digital Twins in Healthcare Market: Distribution by Therapeutic Area, 2018, 2024 and 2035
  • Table 23.31 Digital Twins in Healthcare Market for Cardiovascular Disorders, Historical Trends, 2018-2023 (USD Billion)
  • Table 23.32 Digital Twins in Healthcare Market for Cardiovascular Disorders, Conservative, Base and Optimistic Scenarios, till 2035 (USD Billion)
  • Table 23.33 Digital Twins in Healthcare Market for Metabolic Disorders, Historical Trends, 2018-2023 (USD Billion)
  • Table 23.34 Digital Twins in Healthcare Market for Metabolic Disorders, Conservative, Base and Optimistic Scenarios, till 2035 (USD Billion)
  • Table 23.35 Digital Twins in Healthcare Market for Orthopedic Disorders, Historical Trends, 2018-2023 (USD Billion)
  • Table 23.36 Digital Twins in Healthcare Market for Orthopedic Disorders, Conservative, Base and Optimistic Scenarios, till 2035 (USD Billion)
  • Table 23.37 Digital Twins in Healthcare Market for Other Disorders, Historical Trends, 2018-2023 (USD Billion)
  • Table 23.38 Digital Twins in Healthcare Market for Other Disorders, Conservative, Base and Optimistic Scenarios, till 2035 (USD Billion)
  • Table 23.39 Global Digital Twins in Healthcare Market: Distribution by Type of Digital Twin, 2018, 2024 and 2035
  • Table 23.40 Digital Twins in Healthcare Market for Process Twins, Historical Trends, 2018-2023 (USD Billion)
  • Table 23.41 Digital Twins in Healthcare Market for Process Twins, Conservative, Base and Optimistic Scenarios, till 2035 (USD Billion)
  • Table 23.42 Digital Twins in Healthcare Market for System Twins, Historical Trends, 2018-2023 (USD Billion)
  • Table 23.43 Digital Twins in Healthcare Market for System Twins, Conservative, Base and Optimistic Scenarios, till 2035 (USD Billion)
  • Table 23.44 Digital Twins in Healthcare Market for Whole Body Twins, Historical Trends, 2018-2023 (USD Billion)
  • Table 23.45 Digital Twins in Healthcare Market for Whole Body Twins, Conservative, Base and Optimistic Scenarios, till 2035 (USD Billion)
  • Table 23.46 Digital Twins in Healthcare Market for Body Part Twins, Historical Trends, 2018-2023 (USD Billion)
  • Table 23.47 Digital Twins in Healthcare Market for Body Part Twins, Conservative, Base and Optimistic Scenarios, till 2035 (USD Billion)
  • Table 23.48 Global Digital Twins in Healthcare Market: Distribution by Area of Application, 2018, 2024 and 2035
  • Table 23.49 Digital Twins in Healthcare Market for Asset / Process Management, Historical Trends, 2018-2023 (USD Billion)
  • Table 23.50 Digital Twins in Healthcare Market for Asset / Process Management, Conservative, Base and Optimistic Scenarios, till 2035 (USD Billion)
  • Table 23.51 Digital Twins in Healthcare Market for Personalized Treatment, Historical Trends, 2018-2023 (USD Billion)
  • Table 23.52 Digital Twins in Healthcare Market for Personalized Treatment, Conservative, Base and Optimistic Scenarios, till 2035 (USD Billion)
  • Table 23.53 Digital Twins in Healthcare Market for Surgical Planning, Historical Trends, 2018-2023 (USD Billion)
  • Table 23.54 Digital Twins in Healthcare Market for Surgical Planning, Conservative, Base and Optimistic Scenarios, till 2035 (USD Billion)
  • Table 23.55 Digital Twins in Healthcare Market for Diagnosis, Historical Trends, 2018-2023 (USD Billion)
  • Table 23.56 Digital Twins in Healthcare Market for Diagnosis, Conservative, Base and Optimistic Scenarios, till 2035 (USD Billion)
  • Table 23.57 Digital Twins in Healthcare Market for Other Application Areas, Historical Trends, 2018-2023 (USD Billion)
  • Table 23.58 Digital Twins in Healthcare Market for Other Application Areas, Conservative, Base and Optimistic Scenarios, till 2035 (USD Billion)
  • Table 23.59 Global Digital Twins in Healthcare Market: Distribution by End Users, 2018, 2024 and 2035
  • Table 23.60 Digital Twins in Healthcare Market for Pharmaceutical Companies, Historical Trends, 2018-2023 (USD Billion)
  • Table 23.61 Digital Twins in Healthcare Market for Pharmaceutical Companies, Conservative, Base and Optimistic Scenarios, till 2035 (USD Billion)
  • Table 23.62 Digital Twins in Healthcare Market for Medical Device Manufacturers, Historical Trends, 2018-2023 (USD Billion)
  • Table 23.63 Digital Twins in Healthcare Market for Medical Device Manufacturers, Conservative, Base and Optimistic Scenarios, till 2035 (USD Billion)
  • Table 23.64 Digital Twins in Healthcare Market for Healthcare Providers, Historical Trends, 2018-2023 (USD Billion)
  • Table 23.65 Digital Twins in Healthcare Market for Healthcare Providers, Conservative, Base and Optimistic Scenarios, till 2035 (USD Billion)
  • Table 23.66 Digital Twins in Healthcare Market for Patients, Historical Trends, 2018-2023 (USD Billion)
  • Table 23.67 Digital Twins in Healthcare Market for Patients, Conservative, Base and Optimistic Scenarios, till 2035 (USD Billion)
  • Table 23.68 Digital Twins in Healthcare Market for Other End Users, Historical Trends, 2018-2023 (USD Billion)
  • Table 23.69 Digital Twins in Healthcare Market for Other End Users, Conservative, Base and Optimistic Scenarios, till 2035 (USD Billion)
  • Table 23.70 Global Digital Twins in Healthcare Market: Distribution by Key Geographies, 2018, 2024 and 2035
  • Table 23.71 Digital Twins in Healthcare Market in North America, Historical Trends, 2018-2023 (USD Billion)
  • Table 23.72 Digital Twins in Healthcare Market in North America, Conservative, Base and Optimistic Scenarios, till 2035 (USD Billion)
  • Table 23.73 Digital Twins in Healthcare Market in the US, Historical Trends, 2018-2023 (USD Billion)
  • Table 23.74 Digital Twins in Healthcare Market in the US, Conservative, Base and Optimistic Scenarios, till 2035 (USD Billion)
  • Table 23.75 Digital Twins in Healthcare Market in Canada, Historical Trends, 2018-2023 (USD Billion)
  • Table 23.76 Digital Twins in Healthcare Market in Canada, Conservative, Base and Optimistic Scenarios, till 2035 (USD Billion)
  • Table 23.77 Digital Twins in Healthcare Market in Europe, Historical Trends, 2018-2023 (USD Billion)
  • Table 23.78 Digital Twins in Healthcare Market in Europe, Conservative, Base and Optimistic Scenarios, till 2035 (USD Billion)
  • Table 23.79 Digital Twins in Healthcare Market in France, Historical Trends, 2018-2023 (USD Billion)
  • Table 23.80 Digital Twins in Healthcare Market in France, Conservative, Base and Optimistic Scenarios, till 2035 (USD Billion)
  • Table 23.81 Digital Twins in Healthcare Market in Germany, Historical Trends, 2018-2023 (USD Billion)
  • Table 23.82 Digital Twins in Healthcare Market in Germany, Conservative, Base and Optimistic Scenarios, till 2035 (USD Billion)
  • Table 23.83 Digital Twins in Healthcare Market in Italy, Historical Trends, 2018-2023 (USD Billion)
  • Table 23.84 Digital Twins in Healthcare Market in Italy, Conservative, Base and Optimistic Scenarios, till 2035 (USD Billion)
  • Table 23.85 Digital Twins in Healthcare Market in Spain, Historical Trends, 2018-2023 (USD Billion)
  • Table 23.86 Digital Twins in Healthcare Market in Spain, Conservative, Base and Optimistic Scenarios, till 2035 (USD Billion)
  • Table 23.87 Digital Twins in Healthcare Market in the UK, Historical Trends, 2018-2023 (USD Billion)
  • Table 23.88 Digital Twins in Healthcare Market in the UK, Conservative, Base and Optimistic Scenarios, till 2035 (USD Billion)
  • Table 23.89 Digital Twins in Healthcare Market in Rest of the Europe, Historical Trends, 2018-2023 (USD Billion)
  • Table 23.90 Digital Twins in Healthcare Market in Rest of the Europe, Conservative, Base and Optimistic Scenarios, till 2035 (USD Billion)
  • Table 23.91 Digital Twins in Healthcare Market in Asia, Historical Trends, 2018-2023 (USD Billion)
  • Table 23.92 Digital Twins in Healthcare Market in Asia, Conservative, Base and Optimistic Scenarios, till 2035 (USD Billion)
  • Table 23.93 Digital Twins in Healthcare Market in China, Historical Trends, 2018-2023 (USD Billion)
  • Table 23.94 Digital Twins in Healthcare Market in China, Conservative, Base and Optimistic Scenarios, till 2035 (USD Billion)
  • Table 23.95 Digital Twins in Healthcare Market in India, Historical Trends, 2018-2023 (USD Billion)
  • Table 23.96 Digital Twins in Healthcare Market in India, Conservative, Base and Optimistic Scenarios, till 2035 (USD Billion)
  • Table 23.97 Digital Twins in Healthcare Market in Japan, Historical Trends, 2018-2023 (USD Billion)
  • Table 23.98 Digital Twins in Healthcare Market in Japan, Conservative, Base and Optimistic Scenarios, till 2035 (USD Billion)
  • Table 23.99 Digital Twins in Healthcare Market in Singapore, Historical Trends, 2018-2023 (USD Billion)
  • Table 23.100 Digital Twins in Healthcare Market in Singapore, Conservative, Base and Optimistic Scenarios, till 2035 (USD Billion)
  • Table 23.101 Digital Twins in Healthcare Market in South Korea, Historical Trends, 2018-2023 (USD Billion)
  • Table 23.102 Digital Twins in Healthcare Market in South Korea, Conservative, Base and Optimistic Scenarios, till 2035 (USD Billion)
  • Table 23.103 Digital Twins in Healthcare Market in Rest of the Asia, Historical Trends, 2018-2023 (USD Billion)
  • Table 23.104 Digital Twins in Healthcare Market in Rest of the Asia, Conservative, Base and Optimistic Scenarios, till 2035 (USD Billion)
  • Table 23.105 Digital Twins in Healthcare Market in Latin America, Historical Trends, 2018-2023 (USD Billion)
  • Table 23.106 Digital Twins in Healthcare Market in Latin America, Conservative, Base and Optimistic Scenarios, till 2035 (USD Billion)
  • Table 23.107 Digital Twins in Healthcare Market in Brazil, Historical Trends, 2018-2023 (USD Billion)
  • Table 23.108 Digital Twins in Healthcare Market in Brazil, Conservative, Base and Optimistic Scenarios, till 2035 (USD Billion)
  • Table 23.109 Digital Twins in Healthcare Market in Middle East and North Africa, Historical Trends, 2018-2023 (USD Billion)
  • Table 23.110 Digital Twins in Healthcare Market in Middle East and North Africa, Conservative, Base and Optimistic Scenarios, till 2035 (USD Billion)
  • Table 23.111 Digital Twins in Healthcare Market in UAE, Historical Trends, 2018-2023 (USD Billion)
  • Table 23.112 Digital Twins in Healthcare Market in UAE, Conservative, Base and Optimistic Scenarios, till 2035 (USD Billion)
  • Table 23.113 Digital Twins in Healthcare Market in Rest of the World, Historical Trends, 2018-2023 (USD Billion)
  • Table 23.114 Digital Twins in Healthcare Market in Rest of the World, Conservative, Base and Optimistic Scenarios, till 2035 (USD Billion)
  • Table 23.115 Digital Twins in Healthcare Market in Australia, Historical Trends, 2018-2023 (USD Billion)
  • Table 23.116 Digital Twins in Healthcare Market in Australia, Conservative, Base and Optimistic Scenarios, till 2035 (USD Billion)
  • Table 23.117 Digital Twins in Healthcare Market in New Zealand, Historical Trends, 2018-2023 (USD Billion)
  • Table 23.118 Digital Twins in Healthcare Market in New Zealand, Conservative, Base and Optimistic Scenarios, till 2035 (USD Billion)

List of Figures

  • Figure 2.1 Research Methodology: Research Assumptions
  • Figure 2.2 Research Methodology: Project Methodology
  • Figure 2.3 Research Methodology: Forecast Methodology
  • Figure 2.4 Research Methodology: Robust Quality Control
  • Figure 2.5 Research Methodology: Key Market Segmentations
  • Figure 4.1 Executive Summary: Market Landscape
  • Figure 4.2 Executive Summary: Partnerships and Collaborations
  • Figure 4.3 Executive Summary: Funding and Investment Analysis
  • Figure 4.4 Executive Summary: Market Forecast and Opportunity Analysis (I / II)
  • Figure 4.5 Executive Summary: Market Forecast and Opportunity Analysis (II / II)
  • Figure 5.1 Types of Digital Twins Used in Healthcare
  • Figure 5.2 Applications of Digital Twins in the Healthcare Domain
  • Figure 6.1 Digital Twins: Distribution by Development Status
  • Figure 6.2 Digital Twins: Distribution by Therapeutic Area
  • Figure 6.3 Digital Twins: Distribution by Areas of Application
  • Figure 6.4 Digital Twins: Distribution by Type of Technology Used
  • Figure 6.5 Digital Twins: Distribution by End Users
  • Figure 6.6 Digital Twins: Distribution by Type of Digital Twin
  • Figure 6.7 Digital Twin Developers: Distribution by Year of Establishment
  • Figure 6.8 Digital Twin Developers: Distribution by Company Size
  • Figure 6.9 Digital Twin Developers: Distribution by Location of Headquarters
  • Figure 7.1 Key Insights: Distribution by Area of Application and Development Status
  • Figure 7.2 Key Insights: Distribution by Type of Technology Used and Type of Digital Twin
  • Figure 7.3 Key Insights: Distribution by Type of End User and Type of Digital Twin
  • Figure 7.4 Key Insights: Distribution by Location of Headquarters and Area of Application
  • Figure 7.5 Key Insights: Distribution by Company Size and Location of Headquarters
  • Figure 8.1 Company Competitiveness Analysis: Benchmarking of Portfolio Strength
  • Figure 8.2 Company Competitiveness Analysis: Benchmarking of Partnership Activity
  • Figure 8.3 Company Competitiveness Analysis: Benchmarking of Funding Activity
  • Figure 8.4 Company Competitiveness Analysis: Dot-plot Analysis of Players Based in North America
  • Figure 8.5 Company Competitiveness Analysis: 3-D Bubble Chart Analysis of Players Based in North America
  • Figure 8.6 Company Competitiveness Analysis: Dot-plot Analysis of Players Based in Europe
  • Figure 8.7 Company Competitiveness Analysis: 3-D Bubble Chart Analysis of Players Based in Europe
  • Figure 8.8 Company Competitiveness Analysis: 3-D Bubble Chart Analysis of Players Based in Asia and Rest of the World
  • Figure 9.1 BigBear.ai: Annual Revenues, 2021-Q3 2023 (USD Million)
  • Figure 9.2 Certara: Annual Revenues, 2020-Q3 2023 (USD Million)
  • Figure 9.3 Dassault Systemes: Annual Revenues, 2019-Q3 2023 (EUR Billion)
  • Figure 11.1 Partnerships and Collaborations: Cumulative Year-wise Trend, 2018-2023
  • Figure 11.2 Partnerships and Collaborations: Distribution by Type of Partnership
  • Figure 11.3 Partnerships and Collaborations: Distribution by Year and Type of Partnership
  • Figure 11.4 Partnerships and Collaborations: Distribution by Type of Partnership and Company Size
  • Figure 11.5 Most Active Players: Distribution by Number of Partnerships
  • Figure 11.6 Partnerships and Collaborations: Local and International Agreements
  • Figure 11.7 Partnerships and Collaborations: Intercontinental and Intracontinental Agreements
  • Figure 12.1 Funding and Investment Analysis: Cumulative Year-wise Trend, 2018-2023
  • Figure 12.2 Funding and Investment Analysis: Cumulative Amount Invested (USD Million), 2018-2023
  • Figure 12.3 Funding and Investment Analysis: Distribution of Instances by Type of Funding, 2018-2023
  • Figure 12.4 Funding and Investment Analysis: Year-Wise Distribution by Type of Funding, 2018-2023
  • Figure 12.5 Funding and Investment Analysis: Distribution of Total Amount Invested (USD Million) by Type of Funding, 2018-2023
  • Figure 12.6 Funding and Investment Analysis: Distribution by Geography
  • Figure 12.7 Most Active Players: Distribution by Number of Funding Instances, 2018-2023
  • Figure 12.8 Most Active Players: Distribution by Amount Raised (USD Million), 2018-2023
  • Figure 12.9 Funding and Investment Summary, 2018-2023 (USD Million)
  • Figure 13.1 Berkus Start-Up Valuation: Total Valuation of Players (USD Million)
  • Figure 13.2 AI Body: Benchmarking of Berkus Start-Up Valuation Parameters
  • Figure 13.3 AnatoScope: Benchmarking of Berkus Start-Up Valuation Parameters
  • Figure 13.4 Antleron: Benchmarking of Berkus Start-Up Valuation Parameters
  • Figure 13.5 EmbodyBio: Benchmarking of Berkus Start-Up Valuation Parameters
  • Figure 13.6 Klinik Sankt Moritz: Benchmarking of Berkus Start-Up Valuation Parameters
  • Figure 13.7 MAI: Benchmarking of Berkus Start-Up Valuation Parameters
  • Figure 13.8 Mindbank AI: Benchmarking of Berkus Start-Up Valuation Parameters
  • Figure 13.9 Neo PLM: Benchmarking of Berkus Start-Up Valuation Parameters
  • Figure 13.10 TwInsight: Benchmarking of Berkus Start-Up Valuation Parameters
  • Figure 13.11 Sound Idea: Benchmarking of Players
  • Figure 13.12 Prototype: Benchmarking of Players
  • Figure 13.13 Management Experience: Benchmarking of Players
  • Figure 13.14 Strategic Relationships: Benchmarking of Players
  • Figure 13.15 Total Valuation: Benchmarking of Players
  • Figure 14.1 Digital Twins in Healthcare: Market Drivers
  • Figure 14.2 Digital Twins in Healthcare: Market Restraints
  • Figure 14.3 Digital Twins in Healthcare: Market Opportunities
  • Figure 14.4 Digital Twins in Healthcare: Market Challenges
  • Figure 15.1 Global Digital Twins in Healthcare Market, Historical Trends (2018-2023) and Forecasted Estimates (till 2035) (USD Billion)
  • Figure 15.2 Global Digital Twins in Healthcare Market, Forecasted Estimates (till 2035): Conservative Scenario (USD Billion)
  • Figure 15.3 Global Digital Twins in Healthcare Market, Forecasted Estimates (till 2035): Optimistic Scenario (USD Billion)
  • Figure 16.1 Digital Twins in Healthcare Market: Distribution by Therapeutic Area, 2018, 2024 And 2035
  • Figure 16.2 Digital Twins in Healthcare Market for Cardiovascular Disorders, Historical Trends (2018-2023) and Forecasted Estimates (till 2035) (USD Billion)
  • Figure 16.3 Digital Twins in Healthcare Market for Metabolic Disorders, Historical Trends (2018-2023) and Forecasted Estimates (till 2035) (USD Billion)
  • Figure 16.4 Digital Twins in Healthcare Market for Orthopedic Disorders, Historical Trends (2018-2023) and Forecasted Estimates (till 2035) (USD Billion)
  • Figure 16.5 Digital Twins in Healthcare Market for Other Disorders, Historical Trends (2018-2023) and Forecasted Estimates (till 2035) (USD Billion)
  • Figure 17.1 Digital Twins in Healthcare Market: Distribution by Type of Digital Twin, 2018, 2024 And 2035
  • Figure 17.2 Digital Twins in Healthcare Market for Process Twins, Historical Trends (2018-2023) and Forecasted Estimates (till 2035) (USD Billion)
  • Figure 17.3 Digital Twins in Healthcare Market for System Twins, Historical Trends (2018-2023) and Forecasted Estimates (till 2035) (USD Billion)
  • Figure 17.4 Digital Twins in Healthcare Market for Whole Body Twins, Historical Trends (2018-2023) and Forecasted Estimates (till 2035) (USD Billion)
  • Figure 17.5 Digital Twins in Healthcare Market for Body Part Twins, Historical Trends (2018-2023) and Forecasted Estimates (till 2035) (USD Billion)
  • Figure 18.1 Digital Twins in Healthcare Market: Distribution by Area of Application, 2018, 2024 And 2035
  • Figure 18.2 Digital Twins in Healthcare Market for Asset / Process Management, Historical Trends (2018-2023) and Forecasted Estimates (till 2035) (USD Billion)
  • Figure 18.3 Digital Twins in Healthcare Market for Personalized Treatment, Historical Trends (2018-2023) and Forecasted Estimates (till 2035) (USD Billion)
  • Figure 18.4 Digital Twins in Healthcare Market for Surgical Planning, Historical Trends (2018-2023) and Forecasted Estimates (till 2035) (USD Billion)
  • Figure 18.5 Digital Twins in Healthcare Market for Diagnosis, Historical Trends (2018-2023) and Forecasted Estimates (till 2035) (USD Billion)
  • Figure 18.6 Digital Twins in Healthcare Market for Other Application Areas, Historical Trends (2018-2023) and Forecasted Estimates (till 2035) (USD Billion)
  • Figure 19.1 Digital Twins in Healthcare Market: Distribution by End Users, 2018, 2024 And 2035
  • Figure 19.2 Digital Twins in Healthcare Market for Pharmaceutical Companies, Historical Trends (2018-2023) and Forecasted Estimates (till 2035) (USD Billion)
  • Figure 19.3 Digital Twins in Healthcare Market for Medical Device Manufacturers, Historical Trends (2018-2023) and Forecasted Estimates (till 2035) (USD Billion)
  • Figure 19.4 Digital Twins in Healthcare Market for Healthcare Providers, Historical Trends (2018-2023) and Forecasted Estimates (till 2035) (USD Billion)
  • Figure 19.5 Digital Twins in Healthcare Market for Patients, Historical Trends (2018-2023) and Forecasted Estimates (till 2035) (USD Billion)
  • Figure 19.6 Digital Twins in Healthcare Market for Other End Users, Historical Trends (2018-2023) and Forecasted Estimates (till 2035) (USD Billion)
  • Figure 20.1 Digital Twins in Healthcare Market: Distribution by Key Geographies, 2018, 2024 And 2035
  • Figure 20.2 Digital Twins in Healthcare Market in North America, Historical Trends (2018-2023) and Forecasted Estimates (till 2035) (USD Billion)
  • Figure 20.3 Digital Twins in Healthcare Market in the US, Historical Trends (2018-2023) and Forecasted Estimates (till 2035) (USD Billion)
  • Figure 20.4 Digital Twins in Healthcare Market in Canada, Historical Trends (2018-2023) and Forecasted Estimates (till 2035) (USD Billion)
  • Figure 20.5 Digital Twins in Healthcare Market in Europe, Historical Trends (2018-2023) and Forecasted Estimates (till 2035) (USD Billion)
  • Figure 20.6 Digital Twins in Healthcare Market in France, Historical Trends (2018-2023) and Forecasted Estimates (till 2035) (USD Billion)
  • Figure 20.7 Digital Twins in Healthcare Market in Germany, Historical Trends (2018-2023) and Forecasted Estimates (till 2035) (USD Billion)
  • Figure 20.8 Digital Twins in Healthcare Market in Italy, Historical Trends (2018-2023) and Forecasted Estimates (till 2035) (USD Billion)
  • Figure 20.9 Digital Twins in Healthcare Market in Spain, Historical Trends (2018-2023) and Forecasted Estimates (till 2035) (USD Billion)
  • Figure 20.10 Digital Twins in Healthcare Market in the UK, Historical Trends (2018-2023) and Forecasted Estimates (till 2035) (USD Billion)
  • Figure 20.11 Digital Twins in Healthcare Market in Rest of the Europe, Historical Trends (2018-2023) and Forecasted Estimates (till 2035) (USD Billion)
  • Figure 20.12 Digital Twins in Healthcare Market in Asia, Historical Trends (2018-2023) and Forecasted Estimates (till 2035) (USD Billion)
  • Figure 20.13 Digital Twins in Healthcare Market in China, Historical Trends (2018-2023) and Forecasted Estimates (till 2035) (USD Billion)
  • Figure 20.14 Digital Twins in Healthcare Market in India, Historical Trends (2018-2023) and Forecasted Estimates (till 2035) (USD Billion)
  • Figure 20.15 Digital Twins in Healthcare Market in Japan, Historical Trends (2018-2023) and Forecasted Estimates (till 2035) (USD Billion)
  • Figure 20.16 Digital Twins in Healthcare Market in Singapore, Historical Trends (2018-2023) and Forecasted Estimates (till 2035) (USD Billion)
  • Figure 20.17 Digital Twins in Healthcare Market in South Korea, Historical Trends (2018-2023) and Forecasted Estimates (till 2035) (USD Billion)
  • Figure 20.18 Digital Twins in Healthcare Market in Rest of the Asia, Historical Trends (2018-2023) and Forecasted Estimates (till 2035) (USD Billion)
  • Figure 20.19 Digital Twins in Healthcare Market in Latin America, Historical Trends (2018-2023) and Forecasted Estimates (till 2035) (USD Billion)
  • Figure 20.20 Digital Twins in Healthcare Market in Brazil, Historical Trends (2018-2023) and Forecasted Estimates (till 2035) (USD Billion)
  • Figure 20.21 Digital Twins in Healthcare Market in Middle East and North Africa, Historical Trends (2018-2023) and Forecasted Estimates (till 2035) (USD Billion)
  • Figure 20.22 Digital Twins in Healthcare Market in UAE, Historical Trends (2018-2023) and Forecasted Estimates (till 2035) (USD Billion)
  • Figure 20.23 Digital Twins in Healthcare Market in Rest of the World, Historical Trends (2018-2023) and Forecasted Estimates (till 2035) (USD Billion)
  • Figure 20.24 Digital Twins in Healthcare Market in Australia, Historical Trends (2018-2023) and Forecasted Estimates (till 2035) (USD Billion)
  • Figure 20.25 Digital Twins in Healthcare Market in New Zealand, Historical Trends (2018-2023) and Forecasted Estimates (till 2035) (USD Billion)
  • Figure 21.1 Conclusion: Market Landscape
  • Figure 21.2 Conclusion: Partnerships and Collaborations
  • Figure 21.3 Conclusion: Funding and Investments
  • Figure 21.4 Conclusion: Berkus Start-up Valuation Analysis
  • Figure 21.5 Conclusion: Market Forecast (I / II)
  • Figure 21.6 Conclusion: Market Forecast (II / II)