Digital Twins in Healthcare Market by Type (Process & System Digital Twin, Product Digital Twin), Application (Drug Discovery & Development, Healthcare Workflow Optimization & Asset Management, Medical Device Design and Testing), End-use - Global Forecast
Digital Twins in Healthcare Market by Type (Process & System Digital Twin, Product Digital Twin), Application (Drug Discovery & Development, Healthcare Workflow Optimization & Asset Management, Medical Device Design and Testing), End-use - Global Forecast 2024-2030
The Digital Twins in Healthcare Market size was estimated at USD 1.40 billion in 2023 and expected to reach USD 1.85 billion in 2024, at a CAGR 33.92% to reach USD 10.85 billion by 2030.
Digital twins in healthcare encompasses the integration of digital replicas of physical assets, processes, people, and devices with real-world entities for the purpose of enhancing patient outcomes, reducing medical errors, optimizing operational efficiencies, and fostering innovation within healthcare services. These digital models are created using advanced technologies such as artificial intelligence (AI), the Internet of Things (IoT), big data analytics, and machine learning (ML) to simulate complex interactions and enable data-driven decision-making. The increased implementation of IoT-enabled devices in healthcare for better collection of health-related data to provide accurate diagnoses through predictive analysis is driving market growth. The increase in demand for personalized medicine is expanding the usage of digital twins for the creation of personalized treatment plans based on a patient's unique genetic makeup and health history. Data privacy and security concerns associated with digital twins in healthcare are hampering the market growth. Growing investment in developing more efficient algorithms that accurately interpret vast amounts of data generated by digital twins is expected to create opportunities for market growth.
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1. Preface
1.1. Objectives of the Study
1.2. Market Segmentation & Coverage
1.3. Years Considered for the Study
1.4. Currency & Pricing
1.5. Language
1.6. Stakeholders
2. Research Methodology
2.1. Define: Research Objective
2.2. Determine: Research Design
2.3. Prepare: Research Instrument
2.4. Collect: Data Source
2.5. Analyze: Data Interpretation
2.6. Formulate: Data Verification
2.7. Publish: Research Report
2.8. Repeat: Report Update
3. Executive Summary
4. Market Overview
5. Market Insights
5.1. Market Dynamics
5.1.1. Drivers
5.1.1.1. Increasing focus on manufacturing personalized medicines
5.1.1.2. Rising demand for the development of medical devices
5.1.1.3. Growing inclination for predictive analytics to predict disease progression
5.1.2. Restraints
5.1.2.1. Data quality issues and accuracy of digital twins in healthcare
5.1.3. Opportunities
5.1.3.1. Continuous advancements in the development of digital twins in healthcare
5.1.3.2. Growing integration of digital twins into telemedicine platforms
5.1.4. Challenges
5.1.4.1. Data privacy and security issues with digital twins in healthcare
5.2. Market Segmentation Analysis
5.2.1. Type: Rising role of process & system digital twin in healthcare that focuses on streamlining and optimizing workflows
5.2.2. Application: Expanding usage of digital twins in healthcare workflow optimization & asset management to enhance overall operational efficiency
5.2.3. End-Use: Expanding deployment of digital twins across hospitals & clinics to enhance patient care
5.3. Market Disruption Analysis
5.4. Porter’s Five Forces Analysis
5.4.1. Threat of New Entrants
5.4.2. Threat of Substitutes
5.4.3. Bargaining Power of Customers
5.4.4. Bargaining Power of Suppliers
5.4.5. Industry Rivalry
5.5. Value Chain & Critical Path Analysis
5.6. Pricing Analysis
5.7. Technology Analysis
5.8. Patent Analysis
5.9. Trade Analysis
5.10. Regulatory Framework Analysis
6. Digital Twins in Healthcare Market, by Type
6.1. Introduction
6.2. Process & System Digital Twin
6.3. Product Digital Twin
7. Digital Twins in Healthcare Market, by Application