What if your future doctor wasn’t human, but virtual? Imagine a world where every heartbeat, every breath, every anomaly in your body is monitored in real time by a digital twin. A virtual copy that knows you better than you know yourself, able to prevent illnesses before they even appear. Welcome to the age of digital health twins, a technology that could change our approach to medicine forever. But how do they really work, and how far can they go to transform our health?

Exploring the Multiple Aspects and Benefits of Digital Twins in Healthcare

In the healthcare industry, digital twins are available in several types, each adapted to specific uses ranging from research to clinical practice. These virtual replicas of physical systems can model and simulate complex processes, offer innovative solutions to optimize patient care, improve medical research and reduce costs. Here’s an overview of the main types of digital twins used in healthcare and their benefits.

Organ digital twin

These virtual models are created from medical data such as MRI scans and ultrasounds to represent a specific organ of the human body, such as the heart, liver or lungs. Their use enables a better understanding of an organ’s physiology and pathology, facilitating personalized treatment planning. Dassault Systèmes has created a digital twin called the Living Heart Project, which is an excellent example of the use of digital twins for organ modeling. The project is designed to create an accurate, dynamic digital model of the human heart. It uses medical and physiological data to simulate various cardiac conditions, enabling doctors and researchers to test surgical interventions, medical devices and pharmacological treatments in a virtual environment before applying them to real patients. One of the goals of the Living Heart Project is to improve the conception and evaluation of medical devices, such as heart valves, and to predict how they will behave once implanted.

See Table available from the article : Digital twins for health: a scoping review

Patient Digital Twin

Digital patient twins represent a person’s entire health status, integrating biometric, clinical and behavioral data to create a dynamic model of their overall health. They enable doctors to predict disease progression, monitor patients remotely and personalize treatments more precisely. In an article in the Journal of Clinical Medicine, the authors explain that managing type 2 diabetes in the elderly is challenging because it is associated with other geriatric pathologies and lifestyle factors, such as reduced activity levels, multiple medications and variability in nutrient absorption. This makes blood glucose levels highly unpredictable. It is therefore essential to personalize medicine and treatment.

The authors created digital twins for 15 Type 2 diabetes patients to deliver precision insulin and obtain more in-depth information. They found that personalizing diabetes treatment using digital twins improved the time during which blood glucose is within the target range, reduced the number of hyper and hypo events (when blood glucose is too high or too low, respectively), and decreased the amount of insulin required for infusion by 14-29%.

Hospital Process Digital Twins

Digital twins are used to optimize hospital resource management, such as operations planning and bed management. For example, the Cleveland Clinic has received a $3.14 million grant from the National Institutes of Health (NIH) for research into the use of digital twins to understand health disparities by region. The project uses data models built from electronic health records (EHRs) to analyze health trends in a combined registry of over 250,000 patients, enabling a better understanding of how environmental, economic and social factors influence health outcomes.

Medical Procedure Digital Twins

These digital twins are used to model specific medical interventions, such as surgery or catheterization procedures. Atlas Meditech uses digital twins and AI to help surgeons prepare pre-operative procedures. In collaboration with NVIDIA, they use the Omniverse 3D development platform and MONAI medical imaging framework to create digital twins of patients’ brains. This technology enables radiologists to automatically segment tumors and normal structures on CT and MRI scans, improving patient safety and surgical outcomes.

Challenges of Digital Twins in Healthcare

However, this promising technology is not without challenges. The issue of data protection is paramount. How can we guarantee the confidentiality and security of personal information collected in real-time?

Data Security Concerns

The development and management of digital twins requires the gathering and analysis of huge amounts of sensitive health data of patients, raising significant concerns about data security and confidentiality. Protecting this data from unauthorized access, breach or misuse is a major challenge. Compliance with regulations, such as HIPAA (Health Insurance Portability and Accountability Act) and GDPR (General Data Protection Regulation), adds to the complexity. Strict measures must be put in place to ensure patient confidentiality, data encryption, secure storage and regulatory compliance.

Systems interoperability issues

Integrating digital twins into existing healthcare systems is a major challenge due to the diversity of data formats and systems used by healthcare facilities. According to a 2022 report by the Office of the National Coordinator for Health Information Technology (ONC), around 70% of US hospitals reported being able to send, receive, find, and integrate critical clinical data from external sources, but only 10% have achieved an “ideal” level of interoperability where data can be shared and used fluidly and efficiently across various systems without significant human intervention.

Ethical and Regulatory Issues

Digital twins also raise ethical and regulatory issues concerning obtaining people’s informed consent for data collection and use in the development of digital twins, data ownership and control, patient autonomy and identifying legal constraints. Recommendations concerning responsible and secure data sharing, protection of anonymity and informed consent should be implemented in the development of connected digital twins to encourage trust and ethical practices.

High implementation and maintenance costs

Implementing digital twins in hospitals and other healthcare facilities requires significant initial investment in digital infrastructure, software and staff training. According to a report by Global Market Insights, the global market for digital twins in healthcare was indeed valued at around $2.5 billion in 2021. The report also forecasts a compound annual growth rate (CAGR) of over 25% between 2022 and 2030.

Digital twins are more than just an innovation; they represent a revolution in the way we think about medicine. But for this potential to be fully realized, we need to overcome the technical, ethical and regulatory challenges that stand in the way. Would you be willing to entrust your health to your digital double?