How Digital Twin Technology Can Revolutionize Healthcare

The recent Artificial Intelligence webinar series by the Society for Imaging Informatics in Medicine (SIIM) discussed how digital twin technology is set to revolutionize healthcare.

The webinar, moderated by Ms. Sara Aghamiri, an engineer and human digital twin researcher at the University of Nebraska and a member of the SIIM Machine Learning Education Subcommittee, featured expert speakers Dr. Jun Deng, Professor of Therapeutic Radiology and Director of Physics Research at Yale School of Medicine, and Dr. Reinhard Laubenbacher, Dean’s Professor, Laboratory Director, and Systems Medicine expert at the University of Florida. This session focused on how digital twin technology is transforming healthcare, with speakers providing valuable insights into its definition, evolution, applications, collaborations, and challenges in healthcare.

Ms. Aghamiri started the discussion by introducing digital twin technology, a concept originally developed for engineering applications, that has emerged as a promising frontier in healthcare and precision medicine. These models replicate physical systems, providing valuable insights into future conditions or responses to treatments. Despite the growing interest in developing human digital twins, this field is considered one of the grand challenges in precision medicine. As Ms. Aghamiri pointed out, recent publications in this field demonstrate that the term “digital twin” is currently applied in various ways. Some differences vary from sector to sector, but definitions within a sector can also vary significantly. Thus, the speakers first shared their perspectives on defining digital twins and distinguishing them from traditional models.

Understanding Digital Twin Technology

Dr. Laubenbacher described a digital twin as a computational model representing an aspect of a patient’s health. Unlike traditional models used for studying diseases, digital twins are continuously updated with patient-specific data. This dynamic, bidirectional connection allows clinicians to optimize treatment decisions in real-time. Dr. Laubenbacher also highlighted the ongoing debate around defining digital twins, noting that the 2023 National Academies report has helped establish a more standardized understanding. He sees digital twins as a transformative technology, bridging the gap from basic scientific research to direct clinical applications, bringing precision and personalization to healthcare in unprecedented ways. Dr. Deng expanded on this definition, emphasizing that digital twins serve as virtual representations of individuals, organs, or systems, which integrate multimodal data for predictive health outcomes. According to his recent scoping review, a digital twin integrates multi-scale modeling and multimodal data to analyze health status, simulate treatments, predict outcomes, and ultimately enhance quality of life. Dr. Deng outlined the essential components of a digital twin: a physical entity, a virtual replica, and a real-time bidirectional connection between the two. He also introduced the “Five I’s” that characterize an effective digital twin: it must be individualized, interconnected, interactive, informative, and impactful. He distinguished digital twins from traditional computational models, noting that while modeling plays a significant role, digital twins go beyond by enabling continuous, real-time interactions across multiple scales, from microscopic biological processes to lifelong health monitoring. This continuous bidirectional exchange, where real-time patient data is used to update and refine the virtual model, makes digital twins a transformative tool in modern healthcare.

Evolution Of Digital Twins In Healthcare

Speakers explained that digital twins have only become prominent in medicine and healthcare within the last decade, emerging as a concept that wraps up various approaches such as precision and personalized medicine. Dr. Laubenbacher pointed out that the key factors enabling this evolution include advances in data collection and computational technologies such as Machine Learning (ML) and Artificial Intelligence (AI). He acknowledged that scaling up this technology will require substantial research, infrastructure building, and unprecedented collaboration among clinicians, biologists, modelers, data analysts, and industry partners. Dr. Deng added that a major obstacle to building comprehensive human digital twins is our limited biological understanding of phenomena occurring at different scales, suggesting we might be at least ten years away from achieving the grand vision of complete human digital twins. Dr. Laubenbacher discussed the integration of AI and ML with mechanistic modeling, acknowledging their effectiveness despite initial reluctance. He highlighted their success in improving large, multi-scale stochastic hybrid models. Dr. Laubenbacher also emphasized that while AI techniques applied to patient data for stratification and clustering may not always qualify as digital twins, they are highly valuable and have the potential to evolve into more sophisticated digital twin models. He advocated for continued development in AI and ML to build a critical mass of tools for future applications.

Digital Twins Applications In Medicine

Moving on, Dr. Deng mentioned three key sectors where digital twin technology is making notable advancements in healthcare: precision medicine, medical device design, and digital wellness. However, he pointed out that sectors such as drug discovery and virtual clinical trials remain underdeveloped, possibly due to IP concerns and market competition, which may be delaying progress and limiting published results. Dr. Deng explained that while digital twin applications in fields such as radiation oncology are still in the early stages of research, they have yet to be deployed clinically. He also shared his team’s work on developing a digital twin-enabled clinical decision support tool for individualized cancer radiotherapy that integrates multimodal data, including treatment outcomes, imaging, diagnostic scans, and electronic health records, to provide evidence-based treatment options. These diverse approaches all aim to personalize radiation therapy using digital twins, with promising results beginning to emerge across the field. Dr. Deng also discussed the close collaboration between radiology and oncology, where radiologists work with oncologists to diagnose patients and create treatment plans. He highlighted the potential of digital twins in radiology, noting that AI and ML models are already being used to assist in image analysis and diagnostic reporting, improving the accuracy of radiologist assessments, especially for junior professionals. However, he acknowledged that fully developing digital twins in radiology is still a work in progress. Combining modeling with other patient data to create individualized systems for outcome analysis, risk-benefit assessments, and prognosis could soon lead to true digital twins in radiology, which would further aid in treatment decisions.

Expanding on digital twin applications in healthcare, Dr. Laubenbacher also discussed his team’s work on developing digital twin technology for patients with respiratory diseases in intensive care units. He explained that the ICU environment is ideal for digital twin applications because patients are connected to numerous monitoring devices that generate substantial data, while physicians must process vast amounts of information under significant time pressure, creating a need for effective decision support tools. He also discussed a new project focused on premature infants, where digital twins could help personalize nutrition based on gestational age and maternal nutritional history, addressing critical gaps in current understanding. Dr. Laubenbacher discussed the potential for using imaging, such as CT scans and X-rays, to build digital twins for lung diseases, specifically in the context of COVID-19. He highlighted the challenge of correlating imaging data with critical biological factors, such as the level of damage to the epithelial lining of the lungs, which is more important in determining the disease outcome than the viral load itself. By successfully making this correlation, routine imaging data could be used to assess the extent of lung damage, providing a significant advance in digital twin applications. Dr. Laubenbacher emphasized that this approach can be extended to other diseases, with the key being the integration of imaging data with relevant biological variables to build accurate digital twins.

International Collaboration Frameworks

Dr. Laubenbacher highlighted that while Europe has made massive investments in digital twins technology both in broad initiatives such as the Virtual Physiological Human and in disease-specific projects such as cancer-focused digital twins, the U.S. requires more equivalent large-scale programs. Currently, researchers in the U.S. can seek funding through National Institutes of Health (NIH) R01 grants or mathematically focused programs from the National Science Foundation (NSF). However, these mechanisms are not well-suited for the comprehensive bench-to-bedside scope required to develop and commercialize digital twins. Dr. Laubenbacher emphasized the need for coordinated, large-scale funding efforts across government agencies, private foundations, and international collaborations. 

Uncertainty Quantifications In Digital Twin Predictions

Speakers emphasized the critical importance of validation, verification, and uncertainty quantification in digital twin development. Dr. Deng emphasized that every major digital twin project must address uncertainty management to maintain robustness and avoid brittle AI models, which perform well in lab settings but fail in real-world clinical applications. Key factors include data quality, accuracy, bias mitigation, and clinical context awareness. To improve reliability, researchers must define the boundaries of both models and data, ensuring outputs align with biological benchmarks. Also, reinforcement learning from human feedback enables continuous refinement, incorporating clinician expertise to iteratively validate and enhance digital twin predictions. This dynamic interplay between real-world data and virtual models promotes the development of a more adaptive and clinically relevant digital twin ecosystem.

Dr. Laubenbacher highlighted the critical role of uncertainty quantification in digital twin applications, noting its significance across various domains, from wind turbines to medicine. In healthcare, the complexity of heterogeneous patient data and flawed datasets contributes to high levels of uncertainty, making predictive modeling particularly challenging. He underscored that digital twins offer a unique opportunity to quantify uncertainty, which can revolutionize personalized treatment strategies. By providing physicians and patients with quantitative assessments of treatment efficacy, digital twins enable more informed medical decisions, offering clarity on the advantages, limitations, and confidence levels associated with different therapeutic options.

In summary, digital twin technology has the potential to revolutionize healthcare by enabling personalized patient care. The SIIM webinar highlighted its impact on precision medicine and the need for ongoing advancements in AI and multi-scale modeling to address challenges and facilitate clinical integration. While progress has been made, further research, collaboration, and innovation remain essential to fully unlock its benefits, particularly in radiology and clinical decision support. With a coordinated effort, digital twins could enhance treatment personalization and improve patient outcomes across healthcare systems.