Funded by the European Union (DTRIP4H, No. 101188432). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the granting authority. Neither the European Union nor the granting authority can be held responsible for them.
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Use Cases
Innovative Health-Related
Proof-of-Concept Use Cases (UCs)
Addressing Challenges in Healthcare
5 Core Project Use Cases:
DTRIP4H addresses critical challenges in healthcare, including data harmonisation, equitable access, and strict privacy protection, through the use of Digital Twin technologies. The project includes seven innovative health-related proof-of-concept Use Cases (UCs). These will focus on cancer treatment, drug development, environmental health, precision treatment for schizophrenia, and personalized medicine, using AI and Augmented/Virtual Reality-powered digital twins within a decentralized health digital twin ecosystem. Five use cases are already being implemented, while two additional use cases will be developed at later stages of the project through an open call procedure.
Use Case 1: Medical Imaging Environment-Based Digital Twins
Creating a digital twin with the purpose of training and educating professionals, and informing patients about the potential risks of exposure to electromagnetic fields during magnetic resonance imaging (MRI).
MRI is one of the most important tools in healthcare. It is generally safe and widely used for diagnosing and monitoring many conditions. However, MRI scanners use very strong magnetic fields and radio waves, meaning that both patients and healthcare staff are exposed to electromagnetic fields (EMF). The level of exposure can vary significantly. Staff members who operate the scanners, support patients, or even briefly enter the MRI room—such as anesthesiologists, caregivers, or cleaners – may experience different levels of EMF exposure. Patients are also exposed differently depending on the type of scan; for example, a brain scan involves different conditions than a knee scan. Because of this, proper MRI safety training is crucial to protect patients and healthcare workers from potential risks. The main challenge is that MRI scanners are almost always in use for patient care, leaving very little opportunity for training or research.
The Use Case 1 team introduces digital-twin MRI environments. These virtual replicas allow medical staff to train, explore safety measures, and test therapy evaluation methods without interrupting daily healthcare operations. With digital twins, safety can be improved, training enhanced, and MRI procedures made safer for everyone involved.
Physical
Digital Twin
Empowered with Augmented / Virtual Reality
Use Case 2: Virtual Reality Drug Combiner
Developing a virtual reality drug combiner to virtually assess combinations of drugs and identify those with high potential efficacy and acceptable toxicity.
Repurposing existing drugs has enormous potential. With more than 20,000 FDA-approved drugs already well studied, they could transform treatments for rare diseases, where new drugs are rarely developed. However, the main challenge is scale: the number of possible drug combinations, sequences, and dosages is enormous. Laboratory tests allow only limited, static “cocktails,” while animal studies would require thousands of experiments and often show poor translation to human outcomes.
The Use Case 2 team leverages the digital twin concept to develop AI models and advanced in vitro evaluation systems. These tools predict drug combinations’ toxicity, efficacy, and kinetics before they reach patients. By using digital twins, researchers can explore a vast range of treatment possibilities virtually, reduce animal testing, accelerate discovery, and open new opportunities for rare disease therapies.
Physical
Digital Twin
Empowered with Augmented / Virtual Reality
Use Case 3: Assessment of Inhalation Exposure in an Urban Area
Using available spatiotemporal data to construct AI/ML models of air quality, drawing on both the existing network of reference monitoring stations and supplementary measurements from newly tested sensors. At the same time, we will attempt to develop an agent-based model that can simulate the movement of synthetic citizens throughout the city. This will lead to a significant improvement in exposure estimates for use in cohort studies and urban planning.
Urban air pollution has significant negative impacts on health, but traditional exposure models rely mainly on static monitoring stations and residence-based data. This approach often fails to capture real human movement and behavior. For example, children walking to school or workers commuting daily may experience much higher exposure than residence-based models suggest. As a result, exposure estimates can be biased, leading to less effective interventions.
Project partners in Brno, Czech Republic, are developing a digital twin of urban air quality by combining personal samplers, IoT sensors, mobile monitoring, and advanced city imagery analysis. Agent-based models are used to simulate the impact of policies on different population groups, while generative AI enhances the data with synthetic inputs to strengthen predictions. This approach provides city planners and policymakers with better decision-making tools and offers a replicable blueprint for addressing air quality challenges worldwide.
Physical
Digital Twin
Empowered with Augmented / Virtual Reality
Use Case 4: Digital Twin Based on Artificial Tumour Niches with Tunable Biophysical Properties
Applying the digital twin concept in medical and clinical oncology to develop a digital twin-based mathematical model of cancer niche geometry and biomechanical properties, accurately describing the 2D/3D cellular constituents of the cancer system.
Cancer remains one of the leading health burdens worldwide, with incidence and mortality trends continuing to grow as populations age. Although recent reports show stabilising or slightly decreasing death rates in some countries, cancer continues to be a major cause of illness and death, and the overall burden remains high across Europe. Traditional animal models are time-consuming, raise ethical concerns, and often fail to capture how cancer develops in humans, which limits progress toward personalised treatments.
Project partners are developing human tumour models using 3D bioprinting. These lab-grown tumour niches closely mimic early-stage cancer development in a physiologically relevant artificial microenvironment, enabling accurate animal-free studies of tumour growth and high-throughput drug screening. The resulting data feeds into an AI-driven digital twin of cancer – a virtual model that evolves as new insights are generated. This approach could lay the groundwork for a predictive model that enables clinicians to anticipate the site of relapse and simulate how a specific patient’s tumour may respond to different therapies before treatment begins. This would reduce trial-and-error, accelerate research, reduce animal testing, and bring medicine closer to tailored patient therapies.
Physical
Digital Twin
Empowered with Augmented / Virtual Reality
Use Case 5: Precision Treatment Development for Schizophrenia
Employing precision modelling for drug design and personalized treatment planning for patients with schizophrenia, using a digital twin environment to enable novel biomarker discovery and treatment outcome prediction.
Schizophrenia affects millions of people worldwide, yet current treatments are effective for only a portion of patients and often cause significant side effects. There is currently no reliable way to predict how an individual patient will respond to a specific treatment, resulting in a costly and burdensome trial-and-error approach to care.
Project partners are using digital twins to model drug effects and support the design of personalized treatment strategies. By simulating patient-specific responses before prescribing, clinicians could reduce ineffective treatments and avoid unnecessary side effects. This approach increases the likelihood of effective care, improves patients’ quality of life, and supports a shift from one-size-fits-all therapies to data-driven precision psychiatry, accelerating drug development and enabling truly personalized mental healthcare.
Physical
Digital Twin
Empowered with Augmented / Virtual Reality