Major Growth Driving Factors:
Some of the major factors that will present opportunities to the market during the forecast period are the rise in investments and funding in the digital twin market, which is supporting the increasing number of startups, and the growing emphasis on cutting-edge real-time analytics. But some of the barriers to the adoption of new technologies and the antiquated digital infrastructure are impeding the market’s growth.
Driver: Increasing investments in digital twin technology by public and private entities
Digital twin technology, which entails building a virtual duplicate of a real object, system, or process, has gained popularity in recent years. Numerous industries, including manufacturing, healthcare, construction, and infrastructure management, have found extensive uses for technology. Because digital twin technology has the ability to spur innovation and boost operational effectiveness, both public and private organizations have been investing more in it. Launched in 2018, “Digi Twins” is a flagship project for Europe that involves over 200 partners from 32 countries. Universities, research centers, medical facilities, and tech firms are some of these partners. Siemens, Philips, Fraunhofer Institute, Technical University of Munich, Imperial College London, and Karolinska Institute are some of the project’s major partners. For a period of ten years, it has been funded with USD 1,180 million.
Opportunity: Modern real-time data analytics are receiving more attention.
Massive volumes of data are produced by digital twins, and these data can be instantly analyzed to reveal information about patient health, resource usage, and operational effectiveness. For instance, real-time data from multiple sources, such as IoT sensors, medical devices, and electronic health records, can be gathered and analyzed by digital twins in hospitals to identify operational inefficiencies, anticipate patient outcomes, and identify possible equipment failures. Healthcare providers can also benefit from real-time data analytics by using it to inform patient care decisions, treatment plans, and resource allocation.
Data analytics is the cornerstone of the entire digital twins technology. Once established, analytics and digital twins will offer optimal and predictive operations along with more precise diagnostics. The use of digital twins in healthcare has been made easier by these developments, and in the years to come, potential will be realized.
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Restraint: Managing privacy concerns, data quality, and expensive implementation
Data privacy and quality are issues with technology that gathers and analyzes sensitive data. Numerous sources of data are gathered, such as wearable technology, electronic health records, and other medical devices. Subsequently, it is securely, dependable, and consistently incorporated into the digital twins. The digital twin’s accuracy and dependability are jeopardized in the absence of high-quality data, which may have an effect on patient care and treatment results. Data privacy is yet another important issue. Organizations should implement strong data governance frameworks and security protocols to guarantee the security, dependability, and accuracy of patient data in order to prevent privacy concerns.
The cost of implementing digital twin technology can be attributed to the acquisition of hardware, software, and data storage systems that are required. Over time, it is anticipated that costs will likely decline as technology advances and is embraced by more people. It is anticipated that the aforementioned factors will have some effect on market growth.
Challenge: Issues with data management and a lack of technical expertise
Digital twin technology implementation and upkeep in the healthcare industry call for specific expertise in fields like data science, software engineering, and machine learning. It is difficult to adjust to this emerging technology, particularly in the healthcare industry with its vast repositories of unstructured data.
Healthcare organizations may experience delays, increased costs, and restricted access to the advantages of digital twins technology if there are insufficient qualified personnel to develop, implement, and maintain the technology. Healthcare organizations can work with academic institutions to develop specialized training programs to meet the growing demand for skilled professionals in this field, as well as invest in education and training programs for their current workforce.
End User Scenario:
According to end users, the research and academia sector of the digital twins in the healthcare market is anticipated to grow significantly.
End users anticipate a significant increase in the use of digital twins in the healthcare market in the research and academia sector. This growth is explained by their capacity to propel technological advancements in the healthcare industry, their cooperative research initiatives that promote knowledge exchange and improve patient care outcomes, and their access to a wealth of healthcare data as well as their proficiency in its analysis and modeling, which give research institutions a competitive edge when it comes to effectively utilizing digital twins. Finally, academic institutions’ propensity to investigate new technologies puts them in a position to be early adopters and growth accelerators for the digital twins in the healthcare industry.
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Projected Revenue Growth, Globally:
The global market for digital twins in healthcare is expected to generate $1.6 billion in revenue in 2023 and $21.1 billion by 2028, growing at a compound annual growth rate (CAGR) of 67.0%.
Geographical Growth Scenario:
Throughout the forecast period, APAC is expected to grow at a significant rate.
Over the course of the projection period, the Asia Pacific market is anticipated to grow at a significant CAGR. Digital twins for healthcare applications are expected to be developed in the Asia Pacific region due to factors like rising medical tourism, growing investments that are expected to create new market participants, growing prevalence of various technologies like IoT, machine learning, etc., and the aging population.
Leading Companies Operating in This Industry:
Prominent companies in this market include Siemens Healthineers AG (Germany), Dassault Systèmes (France), Microsoft (US), Koninklijke Philips N.V. (Netherlands), Faststream Technologies (US), Twin LTD (US), IBM (US), NVIDIA Corporation (US), GE Healthcare (US), NUREA (France), ANSYS, Inc. (US), Rescale, Inc. (US), Predictiv (US), Verto Health (Canada), PrediSurge (France), Qbio (US), Virtonomy GmbH (Germany), Unlearn AI (US), Atos SE (France), ThoughtWire (Canada), Amazon Web Services, Inc.(US), Oracle(US), PTC (US), SAP (Germany), Sim and Cure (France).
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