Digital Twins in Medicine

@article{nokey,

title = { Design for a digital twin in clinical patient care},

author = {Anna-Katharina Nitschke; Carlos Brandl; Fabian Egersdörfer; Magdalena Görtz; Markus Hohenfellner; Matthias Weidemüller},

url = {http://dx.doi.org/10.1038/s44401-025-00060-1},

doi = {10.1038/s44401-025-00060-1},

isbn = {3005-1959},

year  = {2026},

date = {2026-02-02},

urldate = {2026-02-02},

journal = {NPJ Health Systems},

abstract = {Digital Twins hold great potential to personalize clinical patient care, provided the concept is translated to meet specific requirements emerging from established clinical workflows. We present a general and unspecialized Digital Twin design combining knowledge graphs and ensemble learning to reflect the entire patient’s clinical journey and assist clinicians in their decision-making. Such a design is predictive, modular, evolving, informed, interpretable and explainable, thus opening broad clinical applications.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Digital twins for personalized treatment in uro-oncology in the era of artificial intelligence},

author = {Magdalena Görtz; Carlos Brandl; Anna Nitschke; Anja Riediger; Daniel Stromer; Michael Byczkowski; Vincent Heuveline; Matthias Weidemüller},

url = {https://doi.org/10.1038/s41585-025-01096-6},

doi = {10.1038/s41585-025-01096-6},

year  = {2025},

date = {2025-10-10},

urldate = {2025-10-10},

journal = {Nature Reviews Urology},

abstract = {‘Digital twins’, also called ‘digital patient twins’ or ‘virtual human twins’ — digital patient-specific models derived from multimodal health data — are a strong focus in health care and are emerging as a promising tool for improving personalized care in uro-oncology. These models can integrate clinical, genomic, imaging and histopathological information to simulate organ behaviour and disease progress as well as predict responses to treatments. The concept of digital twins has shown potential in various fields, but its application in uro-oncology is still evolving, with few assessments of their feasibility and clinical utility. The advent of artificial intelligence adds a new dimension to their development, potentially enabling the synthesis of diverse, high-quality datasets to improve modelling accuracy and support real-time decision-making. However, substantial challenges exist, including data integration, patient privacy, computational demands and ethical frameworks. In addition, the interpretability of predictions remains essential for gaining clinical trust and guiding patient-centred decisions. The use of digital twins in uro-oncology has the potential to improve patient stratification and treatment planning; however, barriers must be overcome for their successful implementation in clinical routine. By integrating new technologies, fostering interdisciplinary collaboration and prioritizing transparency, digital twins could shape the future of precision uro-oncology.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = { A Personalized and Evidence-Based Clinical Decision Support System Using Ensemble Learning},

author = {Carlos Brandl, Anna-Katharina Nitschke, Fabian Egersdoerfer, Björn Ommer, Matthias Weidemüller},

url = {http://dx.doi.org/10.1109/embc58623.2025.11253016},

doi = {10.1109/embc58623.2025.11253016},

year  = {2025},

date = {2025-07-14},

urldate = {2025-07-14},

journal = {47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)},

abstract = {In clinical practice, medical guidelines and decision support tools help the clinician to ensure good and consistent patient care based on evidence-based knowledge and data science. As the next step in patient care, Digital Twins will provide a holistic patient representation and predict the best decision options available. Towards a patient-centered Digital Twin, we developed a modular, informed, interpretable, personalised, and evidence-adaptive Clinical Decision Support System (CDSS) based upon Ensemble Learning. We compare our approach for different datasets and show the ability to handle medically-provided feature separations, missing data, and the inclusion of evidence-based guidelines. Using the Cleve-land Heart Disease dataset, we reached AUCs of 0.94 (95%CI 0.91-0.97), which is similar to other publications, without extensive preprocessing. Comparing information-based subsets with medical procedure-based subsets, we only find marginal differences with AUCs of 0.92 (0.90-0.94) and 0.94 (0.91-0.96), respectively, emphasizing the variability of our approach. On the TCGA Glioma dataset, we exemplify the benefit of Informed Machine Learning by including guidelines. This results in an AUC of 0.93 (95%CI 0.92-0.95), which is an improvement over the guidelines and other published work. There is also improvement in accuracy over a purely data-driven ensemble, resulting in better accuracy and more balanced precision and recall. We simulated the ensembles behavior under missing feature subspaces and showed consistent improvement over its best performing Base Model. Hence, our CDSS architecture qualifies as a building block for a holistic, patient-journey-representative Digital Twin architecture.Clinical relevance— We provide a modular, informed, interpretable, personalized and evidence-adaptive Clinical Decision Support System, achieving AUCs over 90% on two independent tasks.},

type = {Journal Article},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Lippi2024,

title = {An Experimental Platform for Studying the Heteronuclear Efimov Effect with an Ultracold Mixture of Li-6 and Cs-133 Atoms},

author = {Eleonora Lippi and Manuel Gerken and Stephan Häfner and Marc Repp and Rico Pires and Michael Rautenberg and Tobias Krom and Eva D. Kuhnle and Binh Tran and Juris Ulmanis and Lauriane Chomaz and Matthias Weidemüller },

url = {https://link.springer.com/article/10.1007/s00601-024-01971-9},

doi = {10.1007/s00601-024-01971-9},

year  = {2024},

date = {2024-11-29},

urldate = {2024-11-29},

journal = {Few-Body Systems},

volume = {66},

number = {1},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Geier2024,

title = {Time-reversal in a dipolar quantum many-body spin system},

author = {Sebastian Geier and Adrian Braemer and Eduard Braun and Maximilian Müllenbach and Titus Franz and Martin Gärttner and Gerhard Zürn and Matthias Weidemüller},

url = {https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.6.033197},

doi = {https://doi.org/10.1103/PhysRevResearch.6.033197},

year  = {2024},

date = {2024-08-21},

urldate = {2024-08-21},

journal = {Phys. Rev. Research},

volume = {06},

number = {033197},

issue = {3},

pages = {1-8},

abstract = {Time reversal in a macroscopic system contradicts daily experience. It is practically impossible to restore a

shattered cup to its original state by just time reversing the microscopic dynamics that led to its breakage. Yet,

with the precise control capabilities provided by modern quantum technology, the unitary evolution of a quantum

system can be reversed in time. Here, we implement a time-reversal protocol in a dipolar interacting, isolated

many-body spin system represented by Rydberg states in an atomic gas. By changing the states encoding the

spin, we flip the sign of the interaction Hamiltonian, and demonstrate the reversal of the relaxation dynamics

of the magnetization by letting a demagnetized many-body state evolve back in time into a magnetized state.

We elucidate the role of atomic motion using the concept of a Loschmidt echo. Finally, by combining the

approach with Floquet engineering, we demonstrate time reversal for a large family of spin models with different

symmetries. Our method of state transfer is applicable across a wide range of quantum simulation platforms and

has applications far beyond quantum many-body physics, reaching from quantum-enhanced sensing to quantum

information scrambling.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Franz2024,

title = {Observation of anisotropy-independent magnetization dynamics in spatially disordered Heisenberg spin systems},

author = {Titus Franz and Sebastian Geier and Clément Hainaut and Adrian Braemer and Nithiwadee Thaicharoen and Moritz Hornung and Eduard Braun and Martin Gärttner and Gerhard Zürn and Matthias Weidemüller},

url = {https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.6.033131},

doi = {https://doi.org/10.1103/PhysRevResearch.6.033131},

year  = {2024},

date = {2024-08-05},

urldate = {2024-08-05},

journal = {Phys. Rev. Research},

volume = {06},

number = {033131},

issue = {3},

pages = {1-12},

abstract = {An important step towards a comprehensive understanding of far-from-equilibrium dynamics of quantum

many-body systems is the identification of unifying features that are independent of microscopic details of the

system. We experimentally observe such robust features in the magnetization relaxation dynamics of disordered

Heisenberg XX, XXZ, and Ising Hamiltonians. We realize these Heisenberg spin models with tunable anisotropy

parameter and power-law interactions in an ensemble of Rydberg atoms by encoding the spin in suitable Rydberg

state combinations. We consistently observe stretched-exponential relaxation of magnetization for all considered

spin models, collapsing onto a single curve after appropriate rescaling of time. This robust short-time relaxation

behavior is explained by a perturbative treatment that exploits the strong disorder in pairwise couplings, which

leads to a description in terms of approximately independent pairs of spins. In numerical simulations of small

systems, we show that these pairs of spins constitute approximate local integrals of motion, which remain at least

partially conserved on a timescale exceeding the duration of the relaxation dynamics of the magnetization.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}