Summary

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)
Report Link: https://www.rootsanalysis.com/reports/digital-twins-in-healthcare-market.html
The Digital Twins in Healthcare Market is expected to reach USD 1.9 billion anticipated to grow at a CAGR of 30% during the forecast period 2024-2035.

Digital twins are digital representations of physical entities or systems, crafted using real-time data and simulation models. Within the pharmaceutical sector, these virtual replicas serve a multitude of purposes, including expediting clinical trials, conducting simulated studies for broader demographics, improving treatment efficacy, and cutting costs in drug design and testing. The advent of Industry 4.0 technologies has spurred growth in the healthcare sector's digital twins market, facilitating seamless integration of data and the creation of virtual replicas of physical assets, processes, and even human physiology.

According to recent research, healthcare executives anticipate a substantial increase in investment from pharmaceutical companies, with projections indicating a surge of over 65% in the next three years. Digital twins play a crucial role in hastening clinical trial timelines and facilitating simulated studies involving larger populations. Notably, researchers have developed a risk assessment tool employing virtual simulation, machine learning, and other methodologies to estimate the cardiotoxicity potential of various drugs. This innovation has the potential to alleviate some of the expenses associated with the estimated $2.5 billion annual expenditure on drug design and testing. Such technologies enable organizations to implement predictive maintenance strategies, simulate pharmaceutical processes, and enable real-time monitoring, sparking heightened interest from pharmaceutical companies. Nearly 90% of healthcare executives recognize digital twins as essential technologies for fostering collaboration among various units within their organizations. Furthermore, the increasing demand for virtual simulation, personalized medicine, and predictive maintenance is expected to drive further exploration of digital twin applications in the pharmaceutical industry.

Research Coverage:
This chapter provides a concise introduction to essential digital twin concepts, covering various types of digital twins and their predominant applications in healthcare. Additionally, it explores recent advancements within this sector of the market.
This chapter conducts an in-depth exploration of the current market landscape pertaining to entities engaged in digital twin development. It encompasses detailed information such as their founding year, company size, and headquarters location. Additionally, it delivers a thorough assessment of the wider digital twins market within healthcare, focusing on various critical parameters. The assessment covers the developmental status, categorized as either commercially available or under development, and delves into therapeutic areas such as cardiovascular disorders, metabolic disorders, orthopedic disorders, among others. Moreover, it scrutinizes diverse application areas including asset/process management, personalized treatment, surgical planning, diagnosis, health monitoring, clinical trials, and medical training. Furthermore, the analysis scrutinizes the technology employed, spanning artificial intelligence, virtual reality, augmented reality, blockchain, and others. It also differentiates various types of digital twins, such as body part twin, whole body twin, process twin, and system twin. Additionally, the evaluation extends to end users, encompassing healthcare providers, pharmaceutical companies, medical device manufacturers, patients, and other stakeholders within the industry.
In this section, an in-depth analysis of the healthcare digital twins market is presented, emphasizing contemporary trends. It employs five schematic representations to delineate crucial 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 visual representations provide valuable insights into the evolving landscape of digital twins within healthcare, shedding light on trends and pivotal factors influencing the market.
This section offers a thorough analysis of the competitiveness among stakeholders involved in the production and development of digital twins within the healthcare sector. It assesses various parameters, including years of experience, portfolio strength (comprising the number of products, development status, areas of application, technology utilized, targeted end users, and types of twins created), partnership efficacy (quantified by the number of partnerships, their duration, and the nature of collaborations), and funding capacity (evaluated based on the frequency, amount, timing, and type of funding received). Through this comprehensive analysis, it provides a detailed insight into the competitive landscape of the healthcare digital twins market, elucidating the strengths and capabilities of key players in the industry.
Detailed 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.
Comprehensive 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.
An Insightful 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.
In this section, a proprietary analysis is undertaken to evaluate start-ups operating within this market segment. This assessment involves assigning monetary values to distinguishing factors utilizing the Berkus start-up valuation parameters. These parameters encompass the strength of the business idea, the progress in prototype development, the expertise of the management team, and the strategic relationships forged by market players. Through this analytical approach, a quantitative assessment of start-up competitiveness is furnished, furnishing insights into their growth potential and likelihood of success within the market.
This section provides a thorough analysis of the factors impacting 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 require attention. Through this comprehensive examination, a holistic understanding of the dynamics shaping the digital twin market in healthcare is offered, facilitating informed decision-making and strategic planning for stakeholders within the industry.
Detailed 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.
Comprehensive 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.
Elaborate 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.
Detailed 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.
Elaborate 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.
Detailed 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 Systèmes
DEO
Mesh Bio
NavvTrack
OnScale
Phesi
PrediSurge
SingHealth
Twin Health
Unlearn
Verto
VictoryXR
Virtonomy

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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 Systèmes
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 Systèmes
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

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List of Tables/Graphs

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 Systèmes: 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)