Digital twins are virtual models designed to accurately reflect a physical object or system. The concept comes from engineering and has been applied to complex systems such as airplanes, manufacturing, and even cities. While the use of digital twins in healthcare is still very new, it is already showing promise for more personalised treatments and creating a better understanding of patient health.
This blog will provide an overview of digital twins in healthcare and discuss how they facilitate targeted, data-driven medicine and help optimise treatment plans.
What is a health digital twin?
Health digital twins refer to virtual counterparts of patients ("physical twins"), created with information from various sources, including genetic information, multimodal patient data, and real-time updates.
Digital twins play a pivotal role in propelling precision medicine forward by providing personalised, dynamic, and comprehensive representations of an individual’s health. These virtual models enable healthcare professionals to simulate and analyse the impact of different treatments, predict disease progression, and tailor interventions based on an individual's unique characteristics.
Digital twin technology is built on two key ideas: Cyber-Physical Systems (CPS) and Closed-Loop Optimization (CLO). CPS involves AI mimicking human reasoning using big data and pattern recognition, along with the Internet of Things (IoT) for quick data syncing between physical and digital twins. CLO uses real-time data to keep an eye on health, diagnose issues, predict diseases, and fine-tune treatments for better outcomes.
What are the benefits of using digital twins?
This technology holds immense potential in advancing medicine, enhancing clinical trial methodologies, and contributing to innovations in public health practices in the following ways:
Disease modelling and prediction: Digital twins leverage patient-specific data to create virtual models that simulate disease progression. By incorporating genetic information, lifestyle factors, and real-time health updates, these models enable accurate predictions of disease development and response to treatment.
Personalised treatment plans: Digital twins assist in tailoring treatment strategies by analysing how individuals respond to specific medications or interventions. This allows healthcare professionals to optimise dosage, select the most effective therapies, and minimise potential side effects, ultimately enhancing the efficacy of treatment plans.
Genomic analysis: Digital twins facilitate in-depth genomic analysis by integrating genetic data into their models. This information helps identify genetic markers, understand susceptibility to certain diseases, and guide the development of targeted therapies based on an individual's unique genetic profile.
Clinical trial simulation: In the realm of drug development, digital twins simulate virtual participants for clinical trials. This enables researchers to assess the potential efficacy and safety of new drugs in a controlled environment before moving to human trials, streamlining the drug development process.
Continuous monitoring and health maintenance: Digital twins offer real-time monitoring of patients' health parameters, allowing for continuous assessment of vital signs, biomarkers, and other relevant data. This continuous feedback loop supports proactive healthcare interventions and preventive measures, contributing to overall health maintenance. This is particularly important for individuals living with chronic disease.
Surgical planning and precision procedures: In surgical contexts, digital twins provide 3D representations of anatomical structures, aiding surgeons in precise planning and simulation of procedures. This enhances the accuracy of surgeries and reduces risks by allowing practitioners to anticipate potential challenges before entering the operating room.
Virtual health coaching: Digital twins can be employed in virtual health coaching, providing individuals with personalised guidance on lifestyle modifications, nutrition, and exercise routines based on their unique health data and goals.
Risk assessment and early intervention: By analysing a patient's digital twin over time, healthcare providers can identify potential health risks and intervene early to prevent the development or progression of diseases. This proactive approach contributes to more effective and personalised healthcare strategies.
Cases in point
A few case studies highlight the impact digital twins are having on reshaping medical practices and advancing patient-centric solutions.
Cardiovascular disease
Johns Hopkins University researchers have introduced Geno-DT, a genotype-specific digital-twin strategy aimed at revolutionising the diagnosis and treatment of arrhythmogenic right ventricular cardiomyopathy (ARVC). This approach involves creating 3D digital replicas of patients' hearts based on genetic data. The virtual heart tool aims to predict locations of abnormal heart rhythms, potentially improving the effectiveness of catheter ablation procedures.
Orthopaedics
By constructing a digital twin body, researchers successfully predicted the biomechanical properties of the real lumbar spine under various human postures. This real-time monitoring and prediction system, based on AI calculations and finite element methods, offered a new way of planning spine treatments and optimising surgical procedures.
Other applications
In neurocritical care, digital twins have been used to interpret EEG, monitor intracranial pressure, and simulate prognosis. Additionally, in the management of chronic diseases like type 1 diabetes, AI-based digital twin models are being used to continuously calculate insulin requirements, helping doctors accurately prescribe the right treatment to an individual patient.
Ethical and privacy concerns
Digital twins offer the ability to reduce costs, speed up diagnoses, better understand diseases and create more personalised treatment options for patients. However, their implementation does come with several challenges including some ethical and privacy concerns.
Privacy and data security
Digital twins require extensive data, encompassing personal health information, genetic data, and lifestyle details, raising concerns about privacy and data security. Robust measures, aligned with data protection regulations (e.g., GDPR, HIPAA), are imperative to prevent unauthorised access, breaches, and misuse of sensitive information.
Informed consent
Developing a digital health twin involves intensive monitoring, with informed consent required for comprehensive data sharing. Transparent consent processes aligned with data protection regulations are essential. Ensuring that patients fully understand the implications, uses, and risks associated with digital twin creation is also vital for maintaining trust.
Equity and accessibility
Ensuring equal access to digital twin technology and preventing the exacerbation of existing healthcare disparities. Evaluation of affordability, availability, and algorithmic biases is crucial, promoting fair access regardless of socioeconomic status, race, or location.
Psychological impact
The dynamic nature of digital twins can have psychological effects on individuals. Providing supportive resources such as counselling is essential to help individuals cope with the evolving representation of ageing and health changes over time.
Liability
Determining liability for errors in digital predictions or diagnoses poses challenges. Clear legal frameworks are necessary to define the responsibilities of healthcare providers, technology developers, and relevant stakeholders in the event of errors or inaccuracies.
Regulatory compliance
Digital twin development involves multiple regulatory frameworks – and because this is new technology, there aren’t always clear guidelines. Compliance with healthcare, data protection, medical device, and AI regulations is mandatory. Establishing clear regulatory frameworks also ensures accountability and adherence to existing guidelines.
Challenges and ongoing research
Navigating the integration of digital twins in healthcare requires a multifaceted approach that addresses both provider adoption and patient understanding. In the realm of provider adoption, transparency and educational initiatives emerge as critical factors for securing support from healthcare professionals. Overcoming challenges entails not only addressing concerns but also providing clarity and fostering a comprehensive understanding of digital twin technology.
Simultaneously, patient education and trust play pivotal roles in this process. Educating patients about the purpose and implications of digital twins, especially during the consent process, is vital for building trust between patients and healthcare providers.
Moreover, a key emphasis is placed on simplifying the approval process for digital health tools and AI models. The FDA's Medical Device Development Tools (MDDT) program is a significant initiative aimed at prequalifying these technologies, accelerating approvals through standardised documentation protocols. Essential to this process are collaborative efforts that involve advisory committees with expertise in data science. This collaboration is crucial to ensure regulatory compliance and to facilitate the smooth integration of digital twins into the healthcare landscape.
Conclusion
Digital twins are reshaping healthcare with applications ranging from disease modelling to clinical trial simulations to intervention optimisation. Despite their promise, ethical and privacy concerns in their implementation must be addressed, and challenges like provider adoption, patient education, and streamlined approval processes need to be addressed. But what’s most exciting? The integration of digital twins into healthcare signifies a shift toward personalised and data-driven medicine, with collaborative efforts paving the way for optimised patient care and drug development.
For more on the future of healthcare, download our report, "The future of clinical trials."
