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LDTH: 2nd Special Session on Leveraging Digital Twins in Healthcare
The session will be held in Osaka, Japan
10th - 12th September 2025
in conjunction with the 29th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES 2025)
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MISSION:
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Digital Twins (DTs) are emerging as a revolutionary factor in several industries due to their ability to provide a digital and dynamic representation of a physical system or process. With their ability to provide detailed and dynamic information, DTs enable more effective, efficient and predictive management of physical systems and industrial processes. This contributes significantly to improving performance, reducing costs and promoting innovation in the various sectors in which they are implemented. Moreover, their impact is not only limited to monitoring and predicting system status but involves even taking autonomous maintenance decisions, providing recovery action and ensuring the continuity of critical services. These characteristics have earned DTs a position of relevance in both the industrial and academic spheres, with increasing success in the field of Critical Infrastructures. In particular, in healthcare systems, DTs are transforming the delivery of patient care. Through the utilization of real-time data integration, advanced analytics, and virtual simulations, digital twins provide improved patient care, predictive analytics, optimization of clinical operations, and opportunities for training and simulation.

DTs can have multiple functions in healthcare. On the one hand, it is possible to create digital models of patients that contain anatomical, physiological and historical data. These models can be used to tailor treatment plans, simulate complex surgeries and predict individual responses to specific therapies. On the other hand, DTs can be used to monitor the condition and performance of medical devices in real time, predict preventive maintenance and improve the safety and reliability of instruments used in clinical settings. In addition, the flexibility of DTs layered architecture allows the embedding and the combination of several sophisticated analysis techniques – Machine Learning, Process Mining, Deep Learning, Model-driven approaches – that increase the dependability of the results obtained.


TOPICS:
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The topics of interest include but are not limited to, the areas listed below
- Architectural patterns for digital twins
- Modelling concepts and languages, methods, and tools for developing digital twins
- Digital twin for predictive maintenance and performance prediction
- Digital twin for process control
- Digital twin modelling of patients
- Combining models and data in digital twins
- AI for precision medicine
- Security, privacy, and ethical challenges in DT applications
- Use cases and applications of HDTs in clinical practice and research


PAPER SUBMISSION:
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Submitted papers (up to 10 pages including references) will be reviewed by three members of Program Committee (or their sub-reviewers) and selection of accepted papers will be based on relevance, quality and originality. Ensuring the reproducibility of the results is a highly appreciated feature of the contributions.
Accepted papers will be published in a Elsevier's Procedia Computer Science open access journal (https://www.sciencedirect.com/journal/procedia-computer-science/about/aims-and-scope). At least one of the (co)author(s) of each accepted paper is required to register and attend the conference to present the work.

Authors must use the Procedia templates obtainable at: http://kes2025.kesinternational.org/submission.php.
All papers for the LDTH Session should be submitted via dedicated EasyChair (https://easychair.org/account2/signin_timeout?l=5137246614220884910).

If you have any questions regarding the submission of manuscripts, please contact one of the Session Chairs.


VENUE:
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The conference will be held in Osaka, Japan.
More info will be available soon on the 29th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES 2025) (http://kes2025.kesinternational.org/)


SESSION CHAIRS:
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Laura Verde, University of Campania "L. Vanvitelli" (Italy)”, laura.verde@unicampania.it
Jan Vrba, University of Chemistry and Technology (Czech Republic)”, jan.vrba@vscht.cz
Roberta De Fazio, University of Campania "L. Vanvitelli" (Italy)”, roberta.defazio@unicampania.it