헬스케어 분야 디지털 트윈 시장 : 치료 분야별, 디지털 트윈 유형별, 응용 분야별, 최종사용자별, 주요 지역별 - 업계 동향과 세계 예측(- 2035년)

Digital Twins in Healthcare Market by Therapeutic Area, Type of Digital Twin, Areas of Application, End Users and Key Geographical Regions : Industry Trends and Global Forecasts, Till 2035

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한글목차

세계 헬스케어 분야 디지털 트윈 시장 규모는 2035년까지 예측 기간 동안 30%의 연평균 복합 성장률(CAGR)로 확대되어 현재 19억 달러에서 2035년까지 334억 달러로 성장할 것으로 예상됩니다.

최근 인공지능, 딥러닝, 머신러닝, 빅데이터와 같은 기술 관련 발전이 눈에 띄게 이루어지고 있습니다. 이러한 프로그램들은 현재 헬스케어 분야에 종사하는 신규 시장 진출기업들의 큰 관심을 받고 있습니다. 그 결과, 업계 이해관계자들은 앞서 언급한 기술을 사용하여 혁신을 촉진하고 기존 프로세스의 성능을 개선하기 위해 노력하고 있습니다. 이러한 기술 중 디지털 트윈은 헬스케어 분야에서 유망한 기술로 떠오르고 있습니다. 이러한 맥락에서 디지털 트윈은 본질적으로 시스템이나 프로세스의 성능을 예측할 수 있는 시뮬레이션을 생성하기 위해 실제 데이터를 사용하는 가상 모델이라는 점을 언급할 필요가 있습니다. 구체적으로 헬스케어 영역에서 디지털 트윈은 위험 예측, 인건비 절감, 환자 치료 개선, 의사결정 프로세스 자동화 등에 적용될 수 있습니다. 또한, 헬스케어 분야의 기업들은 연구개발비용을 절감하기 위해 디지털 트윈 개념을 도입할 가능성이 높아지고 있습니다. 개인화된 의료, 가상 시뮬레이션, 자동화 기술에 대한 수요 증가에 힘입어 헬스케어 분야 디지털 트윈은 크게 성장할 것으로 예상됩니다.

현재 90개 이상의 디지털 트윈이 진단, 건강 모니터링, 수술 계획 등 다양한 헬스케어 관련 용도로 시판 중이거나 개발 중입니다.

업계에서 제공하는 디지털 트윈의 42% 이상이 프로세스 트윈이며, 대부분 자산/프로세스 관리, 개인화 치료, 수술 계획 등을 위한 것입니다. 이 업계의 파트너십 활동은 지난 3년 동안 20% 이상 성장했으며, 그 중 45% 이상이 지난 2년 동안 체결된 파트너십이라는 점은 주목할 만합니다. 현재 진행 중인 혁신을 지원하기 위해 여러 민간 및 공공 투자자들이 막대한 자본을 투자하고 있습니다. 디지털 트윈 시장의 스타트업들은 경쟁사와의 차별화를 위해 인공지능, 블록체인과 같은 진보적이고 혁신적인 기술을 점차적으로 도입하고 있습니다. 원격 환자 모니터링, 예측 분석, 개인 맞춤형 치료, IoT 통합에 대한 관심이 높아지면서 헬스케어 분야 디지털 트윈 시장은 당분간 견조한 성장세를 보일 것으로 예상됩니다. 헬스케어 및 제약 산업에서 디지털 트윈 기술의 채택이 증가함에 따라 헬스케어 분야의 세계 디지털 트윈 시장은 2035년까지 연평균 30%의 성장률을 나타낼 것으로 예측됩니다.

세계의 헬스케어 분야 디지털 트윈(Digital Twin) 시장에 대해 조사했으며, 시장 개요와 함께 치료 분야별/디지털 트윈 유형별/응용 분야별/최종사용자별/주요 지역별 동향, 시장 진출기업 프로파일 등의 정보를 전해드립니다.

목차

제1장 서문

제2장 조사 방법

제3장 경제 및 기타 프로젝트 특유의 고려사항

제4장 주요 요약

제5장 서론

제6장 시장 구도

제7장 주요 인사이트

제8장 기업 경쟁력 분석

제9장 상세한 기업 개요

• 본 장의 개요
• BigBear.ai
• Certara
• Dassault Systemes
• NavvTrack
• Unlearn.ai

제10장 기업 개요 리스트

제11장 파트너십 및 협업

제12장 자금조달과 투자 분석

제13장 BERKUS 스타트업 평가 분석

제14장 시장에 대한 영향 분석 : 성장 촉진요인 및 억제요인, 기회, 과제

제15장 세계의 헬스케어 분야 디지털 트윈 시장

제16장 헬스케어 분야 디지털 트윈 시장, 치료 분야별

제17장 헬스케어 분야 디지털 트윈 시장, 디지털 트윈 유형별

제18장 헬스케어 분야 디지털 트윈 시장, 응용 분야별

제19장 헬스케어 분야 디지털 트윈 시장, 최종사용자별

제20장 헬스케어 분야 디지털 트윈 시장, 주요 지역별

제21장 결론

제22장 주요 인사이트

제23장 부록 I : 표 형식 데이터

제24장 부록 II : 기업 및 단체 리스트

LSH

영문 목차

영문목차

DIGITAL TWINS IN HEALTHCARE MARKET: OVERVIEW

As per Roots Analysis, the global digital twins in healthcare market is estimated to grow from USD 1.9 billion in the current year to USD 33.4 billion by 2035, at a CAGR of 30% during the forecast period, till 2035.

The market opportunity for cell and gene therapy supply chain software has been distributed across the following 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

DIGITAL TWINS IN HEALTHCARE MARKET: GROWTH AND TRENDS

In recent years, there have been significant advancements related to technologies, such as artificial intelligence, deep learning and machine learning and big data. These programs have now garnered significant interest of players engaged in the healthcare domain. Consequently, the industry stakeholders are undertaking efforts to drive innovation and improve the performance of the existing processes, using the aforementioned technologies. Amidst these technologies, digital twins have emerged as a promising technique for use in the healthcare sector. In this context, it is worth mentioning that a digital twin, in its essence, is a virtual model that employs real-world data to create simulations, which are capable of predicting the performance of a system or process. Specifically, in the healthcare domain, digital twins can be applied for risk prediction, lowering labor costs, providing improved patient care and automated decision-making process. Further, players engaged in the healthcare domain may increasingly adopt the digital twin concept to cut down their research and development costs. Driven by the growing demand for personalized medicine, virtual simulation and automated technologies, the digital twin in healthcare is poised to witness substantial growth.

DIGITAL TWINS IN HEALTHCARE MARKET: KEY INSIGHTS

The report delves into the current state of the digital twins in healthcare market and identifies potential growth opportunities within the industry. Some key findings from the report include:

• Currently, over 90 digital twins are either commercially available in the market or are under development for various healthcare related applications including diagnosis, health monitoring and surgical planning.

• Over 42% of the digital twins offered by industry players are process twins; majority of the twins are primarily intended for asset / process management, personalized treatment and surgical planning.
• The partnership activity in this industry has grown at a rate of over 20% in the past three years; it is worth noting that over 45% of the deals have been signed in the last two years.
• To support the ongoing innovations, several private and public investors have made substantial capital investments; notably, most of the funding rounds took place in the past few years.
• Start-ups in the digital twin market are gradually adopting advanced and innovative technologies, such as artificial intelligence and blockchain, in order to differentiate themselves from their competitors.
• Owing to the growing interest towards remote patient monitoring, predictive analytics, personalized treatment, and IoT integration, the market for digital twins in healthcare will increase steadily in the foreseeable future.
• Driven by increasing adoption of digital twin technologies in healthcare and pharmaceutical industries, it is anticipated that the global digital twins market in healthcare domain is likely to grow at an annualized rate of 30%, till 2035.

DIGITAL TWINS IN HEALTHCARE MARKET: KEY SEGMENTS

Cardiovascular Disorders are Likely to Dominate the Digital Twins in Healthcare Market

Based on the therapeutic areas, the market is segmented into cardiovascular disorders, metabolic disorders, orthopedic disorders and other disorders. At present, cardiovascular disorders hold the maximum share of the digital twins in healthcare market. This trend is unlikely to change in the near future.

Asset / Process Management Segment Occupies the Largest Share of the Digital Twins in Healthcare Market

Based on the areas of application, the market is segmented into asset / process management, personalized treatment, surgical planning, diagnosis and other applications. Currently, asset / process management captures the highest portion of the digital twins in healthcare market. However, this trend is expected to gradually shift towards personalized treatment in the future. This can be attributed to the fact that personalized biochemical modeling has experienced an emerging trend in this domain and has demonstrated several promising results, including administrating soft tissue behavior in orthopedic surgical interventions.

Pharmaceutical Companies are Likely to Dominate the Digital Twins in Healthcare Market

Based on the end users, the market is segmented into healthcare providers, medical device manufacturers, patients, pharmaceutical companies and other end users. At present, the pharmaceutical companies hold the maximum share of the digital twins in healthcare market. This trend is likely to remain the same in the forthcoming years.

North America Accounts for the Largest Share of the Market

Based on key geographical regions, the market is segmented into North America, Europe, Asia, Latin America, Middle East and North Africa, and Rest of the World. The majority share is expected to be captured by players based in North America. It is worth highlighting that, over the years, the market in Middle East and North Africa is expected to grow at a higher CAGR.

Example Players in the Digital Twins in Healthcare Market

• BigBear.ai
• Certara
• Dassault Systemes
• DEO
• Mesh Bio
• NavvTrack
• OnScale
• Phesi
• PrediSurge
• SingHealth
• Twin Health
• Unlearn
• Verto
• VictoryXR
• Virtonomy

Primary Research Overview

The opinions and insights presented in this study were influenced by discussions conducted with multiple stakeholders. The research report features detailed transcripts of interviews held with the following industry stakeholders:

• Co-Founder and Chief Scientific Officer, Australia
• Managing Director and Chief Executive Officer, Germany
• Chief Commercial Officer, Germany
• Chief Solutions Officer, Canada
• Co-founder and Chief Technology Officer, US
• Business Consultant, France
• Business Development Executive, US

DIGITAL TWINS IN HEALTHCARE MARKET: RESEARCH COVERAGE

• Market Sizing and Opportunity Analysis: The report features an in-depth analysis of the digital twins in healthcare market, focusing on key market segments, including [A] therapeutic area, [B] type of digital twin, [C] area of application, [D] end users and [E] key geographical regions.
• Market Landscape: A comprehensive evaluation of companies involved in the development of digital twins, considering various parameters, such as [A] year of establishment, [B] company size, [C] location of headquarters, [D] status of development, [E] therapeutic area, [F] areas of application, [G] type of technology used, [H] type of digital twin and [I] end users.
• Key Insights: An in-depth digital twins in healthcare market analysis, highlighting the contemporary market trends, using five schematic representations, based on [A] areas of application and status of development, [B] type of technology used and type of digital twin, [C] type of end user and type of digital twin, [D] area of application and location of headquarters, and [E] company size and location of headquarters.
• Company Competitiveness Analysis: A comprehensive competitive analysis of digital twins developers, examining factors, such as [A] years of experience, [B] portfolio strength, [C] partnership strength and [D] funding strength.
• Company Profiles: In-depth profiles of key industry players offering digital twins, focusing on [A] company overviews, [B] financial information (if available), [C] recent developments and [D] an informed future outlook.
• Partnerships and Collaborations: An analysis of partnerships established in this sector, since 2018, covering 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 others.
• Funding and Investment Analysis: A detailed evaluation of the investments made in the digital twins market, encompassing grants, seed funding, venture capital investments, initial public offering, secondary offerings, private placements, debt financing and other equity.
• Berkus Start-Up Valuation Analysis: A proprietary analysis designed to assess start-ups in this market, by assigning monetary values to various competition differentiators possessed by a player. This evaluation is based on the Berkus start-up valuation criteria, which include factors such as sound idea, prototype, management experience and strategic relationships undertaken by market players.
• Market Impact Analysis: The report analyzes various factors such as drivers, restraints, opportunities, and challenges affecting the market growth.

KEY QUESTIONS ANSWERED IN THIS REPORT

• How many companies are currently engaged in this market?
• Which are the leading companies in this market?
• What factors are likely to influence the evolution of this market?
• What is the current and future market size?
• What is the CAGR of this market?
• How is the current and future market opportunity likely to be distributed across key market segments?

REASONS TO BUY THIS REPORT

• The report provides a comprehensive market analysis, offering detailed revenue projections of the overall market and its specific sub-segments. This information is valuable to both established market leaders and emerging entrants.
• Stakeholders can leverage the report to gain a deeper understanding of the competitive dynamics within the market. By analyzing the competitive landscape, businesses can make informed decisions to optimize their market positioning and develop effective go-to-market strategies.
• The report offers stakeholders a comprehensive overview of the market, including key drivers, barriers, opportunities, and challenges. This information empowers stakeholders to stay abreast of market trends and make data-driven decisions to capitalize on growth prospects.

ADDITIONAL BENEFITS

• Complimentary PPT Insights Packs
• Complimentary Excel Data Packs for all Analytical Modules in the Report
• 10% Free Content Customization
• Detailed Report Walkthrough Session with Research Team
• Free Updated report if the report is 6-12 months old or older

TABLE OF CONTENTS

1. PREFACE

• 1.1. Introduction
• 1.2. Market Share Insights
• 1.3. Key Market Insights
• 1.4. Report Coverage
• 1.5. Frequently Asked Questions
• 1.6. 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
  • 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 and Foreign Exchange Rate
    • 3.2.2.1. Major Currencies Affecting the Market
    • 3.2.2.2. Factors Affecting Currency Fluctuations and Foreign Exchange Rates
    • 3.2.2.3. Impact of Foreign Exchange Rate Volatility on the Market
    • 3.2.2.4. Strategies for Mitigating Foreign Exchange Risk
  • 3.2.3. Trade Policies
    • 3.2.3.1. Impact of Trade Barriers on the Market
    • 3.2.3.2. Strategies for Mitigating the Risks Associated with Trade Barriers
  • 3.2.4. Recession
    • 3.2.4.1. Historical 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. Future Perspectives

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
• 12.4. Concluding Remarks

13. BERKUS START-UP VALUATION ANALYSIS

• 13.1. Chapter Overview
• 13.2. Assumptions and Key Parameters
• 13.3. Methodology
• 13.4. Berkus Start-Up Valuation: Total Valuation of Players
• 13.5. Digital Twins in Healthcare: Benchmarking of Berkus Start-Up Valuation Parameters
  • 13.5.1. AI Body: Benchmarking of Berkus Start-Up Valuation Parameters
  • 13.5.2. AnatoScope: Benchmarking of Berkus Start-Up Valuation Parameters
  • 13.5.3. Antleron: Benchmarking of Berkus Start-Up Valuation Parameters
  • 13.5.4. EmbodyBio: Benchmarking of Berkus Start-Up Valuation Parameters
  • 13.5.5. Klinik Sankt Moritz: Benchmarking of Berkus Start-Up Valuation Parameters
  • 13.5.6. MAI: Benchmarking of Berkus Start-Up Valuation Parameters
  • 13.5.7. Mindbank AI: Benchmarking of Berkus Start-Up Valuation Parameters
  • 13.5.8. Neo PLM: Benchmarking of Berkus Start-Up Valuation Parameters
  • 13.5.9. Twinsight: Benchmarking of Berkus Start-Up Valuation Parameters
• 13.6. Digital Twins in Healthcare: Benchmarking of Players
  • 13.6.1. Sound Idea: Benchmarking of Players
  • 13.6.2. Prototype: Benchmarking of Players
  • 13.6.3. Management Experience: Benchmarking of Players
  • 13.6.4. Strategic Relationships: Benchmarking of Players
  • 13.6.5. 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 (since 2018) and Forecasted Estimates (till 2035)
  • 15.3.1. Scenario Analysis
    • 15.3.1.1. Conservative Scenario
    • 15.3.1.2. Optimistic Scenario
  • 15.3.2. Key Market Segmentations

16. DIGITAL TWIN IN HEALTHCARE MARKET, BY THERAPEUTIC AREA

• 16.1. Chapter Overview
• 16.2. Assumptions and Methodology
• 16.3. Digital Twin in Healthcare Market: Distribution by Therapeutic Area, 2018, 2024 and 2035
  • 16.3.1. Cardiovascular Disorders: Historical Trends (since 2018) and Forecasted Estimates (till 2035)
  • 16.3.2. Metabolic Disorders: Historical Trends (since 2018) and Forecasted Estimates (till 2035)
  • 16.3.3. Orthopedic Disorders: Historical Trends (since 2018) and Forecasted Estimates (till 2035)
  • 16.3.4. Other Disorders: Historical Trends (since 2018) and Forecasted Estimates (till 2035)
  • 16.3.5. Data Triangulation and Validation

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

• 17.1. Chapter Overview
• 17.2. Assumptions and Methodology
• 17.3. Digital Twin in Healthcare Market: Distribution by Type of Digital Twins, 2018, 2024 and 2035
  • 17.3.1. Process Twins: Historical Trends (since 2018) and Forecasted Estimates (till 2035)
  • 17.3.2. System Twins: Historical Trends (since 2018) and Forecasted Estimates (till 2035)
  • 17.3.3. Whole Body Twins: Historical Trends (since 2018) and Forecasted Estimates (till 2035)
  • 17.3.4. Body Part Twins: Historical Trends (since 2018) and Forecasted Estimates (till 2035)
  • 17.3.5. Data Triangulation and Validation

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

• 18.1. Chapter Overview
• 18.2. Assumptions and Methodology
• 18.3. Digital Twin in Healthcare Market: Distribution by Area of Application, 2018, 2024 And 2035
  • 18.3.1. Asset / Process Management: Historical Trends (since 2018) and Forecasted Estimates (till 2035)
  • 18.3.2. Personalized Treatment: Historical Trends (since 2018) and Forecasted Estimates (till 2035)
  • 18.3.3. Surgical Planning: Historical Trends (since 2018) and Forecasted Estimates (till 2035)
  • 18.3.4. Diagnosis: Historical Trends (since 2018) and Forecasted Estimates (till 2035)
  • 18.3.5. Other Applications: Historical Trends (since 2018) and Forecasted Estimates (till 2035)
  • 18.3.6. Data Triangulation and Validation

19. DIGITAL TWIN IN HEALTHCARE MARKET, BY END USERS

• 19.1. Chapter Overview
• 19.2. Assumptions and Methodology
• 19.3. Digital Twin in Healthcare Market: Distribution by End Users, 2018, 2024 and 2035
  • 19.3.1. Pharmaceutical Companies: Historical Trends (since 2018) and Forecasted Estimates (till 2035)
  • 19.3.2. Medical Device Manufacturers: Historical Trends (since 2018) and Forecasted Estimates (till 2035)
  • 19.3.3. Healthcare Providers: Historical Trends (since 2018) and Forecasted Estimates (till 2035)
  • 19.3.4. Patients: Historical Trends (since 2018) and Forecasted Estimates (till 2035)
  • 19.3.5. Other End Users: Historical Trends (since 2018) and Forecasted Estimates (till 2035)
  • 19.3.6. Data Triangulation and Validation

20. DIGITAL TWIN IN HEALTHCARE MARKET, BY GEOGRAPHY

• 20.1. Chapter Overview
• 20.2. Assumptions and Methodology
• 20.3. Digital Twins in Healthcare Market: Distribution by Key Geographies, 2018, 2024 And 2035
  • 20.3.1. North America: Historical Trends (since 2018) and Forecasted Estimates (till 2035)
    • 20.3.1.1. US: Historical Trends (since 2018) and Forecasted Estimates (till 2035)
    • 20.3.1.2. Canada: Historical Trends (since 2018) and Forecasted Estimates (till 2035)
  • 20.3.2. Europe: Historical Trends (since 2018) and Forecasted Estimates (till 2035)
    • 20.3.2.1. France: Historical Trends (since 2018) and Forecasted Estimates (till 2035)
    • 20.3.2.2. Germany: Historical Trends (since 2018) and Forecasted Estimates (till 2035)
    • 20.3.2.3. Italy: Historical Trends (since 2018) and Forecasted Estimates (till 2035)
    • 20.3.2.4. Spain: Historical Trends (since 2018) and Forecasted Estimates (till 2035)
    • 20.3.2.5. UK: Historical Trends (since 2018) and Forecasted Estimates (till 2035)
    • 20.3.2.6. Rest of Europe: Historical Trends (since 2018) and Forecasted Estimates (till 2035)
  • 20.3.3. Asia: Historical Trends (since 2018) and Forecasted Estimates (till 2035)
    • 20.3.3.1. China: Historical Trends (since 2018) and Forecasted Estimates (till 2035)
    • 20.3.3.2. India: Historical Trends (since 2018) and Forecasted Estimates (till 2035)
    • 20.3.3.3. Japan: Historical Trends (since 2018) and Forecasted Estimates (till 2035)
    • 20.3.3.4. Singapore: Historical Trends (since 2018) and Forecasted Estimates (till 2035)
    • 20.3.3.5. South Korea: Historical Trends (since 2018) and Forecasted Estimates (till 2035)
    • 20.3.3.6. Rest of Asia: Historical Trends (since 2018) and Forecasted Estimates (till 2035)
  • 20.3.4. Latin America: Historical Trends (since 2018) and Forecasted Estimates (till 2035)
  • 20.3.5. Middle East and North Africa: Historical Trends (since 2018) and Forecasted Estimates (till 2035)
  • 20.3.6. Rest of the World: Historical Trends (since 2018) and Forecasted Estimates (till 2035)
    • 20.3.6.1. Australia: Historical Trends (since 2018) and Forecasted Estimates (till 2035)
    • 20.3.6.2. New Zealand: Historical Trends (since 2018) and Forecasted Estimates (till 2035)
  • 20.3.7. Data Triangulation and Validation

21. CONCLUSION

22. EXECUTIVE INSIGHTS

• 22.1. Chapter Overview
• 22.2. Company A
  • 22.2.1. Company Snapshot
  • 22.2.2. Interview Transcript: Chief Commercial Officer
• 22.3. Company B
  • 22.3.1. Company Snapshot
  • 22.3.2. Interview Transcript: Chief Solutions Officer
• 22.4. Company C
  • 22.4.1. Company Snapshot
  • 22.4.2. Interview Transcript: Co-founder and Chief Technology Officer
• 22.5. Company D
  • 22.5.1. Company Snapshot
  • 22.5.2. Interview Transcript: Data Scientist
• 22.6. Company E
  • 22.6.1. Company Snapshot
  • 22.6.2. Interview Transcript: Business Consultant
• 22.7. Company F
  • 22.7.1. Company Snapshot
  • 22.7.2. Interview Transcript: Co-Founder and Chief Scientific Officer
• 22.8. Company G
  • 22.8.1. Company Snapshot
  • 22.8.2. Interview Transcript: Head of Business Development
• 22.9. Company H
  • 22.9.1. Company Snapshot
  • 22.9.2. Interview Transcript: Managing Director and Chief Executive Officer

23. APPENDIX I: TABULATED DATA

24. APPENDIX II: LIST OF COMPANIES AND ORGANIZATIONS

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