The AIDoCS-EMS research project develops a speech-based, multimodal assistance system to automate data collection and processing during prehospital emergency care using AI. Emergency services in Germany and Switzerland face growing challenges due to an aging population and staff shortages.

Current documentation practices are manual, error-prone, unhygienic, and time-consuming.

AIDoCS-EMS aims to create a locally operable platform that integrates spoken communication with automatically captured vital signs and mission data. It generates real-time documentation, including reports and billing data, and securely transmits them to hospitals and control centers.

The system adaptively supports emergency personnel by suggesting questions, reducing cognitive load, and ensuring complete records. Designed for harsh field conditions, it meets the highest standards in data protection, medical terminology, and usability.

In many places, emergency medical services in Germany are already reaching their limits. Due to steadily rising case numbers, a severe shortage of skilled personnel, and a high number of false alarms or non-emergency calls, the mandated response and deployment times can barely be met anymore. Especially in rural areas with long distances between emergency stations and incident sites, the current problems pose enormous challenges for first responders.

The main goal of the COMERGENCY project is to relieve the burden on emergency services through a mobile medical service infrastructure, provide targeted medical care, and improve mobile transport systems. The innovative core of the project is the collection, transmission, and evaluation of the accident victim’s vital signs before professional emergency responders arrive at the scene. (cars, etc.) This will make it possible for the first time to integrate smart devices (e.g., smartwatches, car sensors) into the emergency response chain. In the event of an emergency call, the dispatcher at the emergency operations center can better assess the accident situation (e.g., number of injured persons and severity of injuries), select the appropriate emergency resources, and provide targeted assistance to accident victims through assistance functions. In addition, new network technologies such as smartphone ad-hoc networks and Vehicle-to-X communication are to be utilized to overcome the limitations of network availability in rural areas. These innovative technological concepts and methods will fundamentally transform emergency services and medical transport systems in the near future.

The healthcare system faces the challenge of making ever-increasing volumes of data rapidly, securely, and reliably available for use. This is particularly critical in emergency situations, where vital signs, sensor data, and medical imaging information must be transmitted without delay between ambulances, dispatch centers, and hospitals.

Only under these conditions can well-informed decisions be made and patient care be improved. The project aims to develop and evaluate 6G-based communication and data infrastructures for emergency and acute care medicine that ensure high availability, low latency, and interoperability across system boundaries. Leipzig University Medical Center focuses on the analysis of dispatch center communication and the integration of data flows between emergency medical services and hospitals. Central to this work are concepts for information prioritization and intuitive visualization that support emergency personnel in time-critical situations. In addition, UML provides dedicated platforms for testing the developed solutions in realistic, practice-oriented scenarios.

The PRIDAR project (“Patient-Oriented Resource Analysis for Interdisciplinary Acute Emergency Operations”) addresses the increasing challenges in emergency care, including rising hospital occupancy, insufficient transparency regarding actual capacities, and partial lack of digital integration between hospitals and emergency medical services.

The aim of the project is the development of an intelligent, modularly scalable, and adaptive data and analytics architecture capable of automatically capturing, multi-dimensionally aggregating, and providing structured representations of both current and predicted occupancy levels in emergency departments and clinical specialties. In particular, the system is intended to provide emergency medical services with real-time and prospective decision-support information, enabling allocation processes to be optimized in a resource-sensitive, situation-adaptive, and patient-centered manner.

Through the smart integration of real-time data from hospital information systems (HIS), emergency department scoring systems (CEDOCS), DIVI intensive care registry data, and emergency medical service datasets, a comprehensive digital system is created that enables informed destination hospital selection. This facilitates faster and more appropriate patient allocation. In addition to improving patient care, the system also optimizes the utilization of emergency medical service resources, thereby increasing the overall efficiency of the emergency and healthcare system.

Within the project, ICCAS will extend the existing capacity management software IVENA of the project partner mainis IT-Service GmbH with an automated, data-driven forecasting component and integrate AI methods and expert systems for occupancy prediction. This represents an important step toward a patient-centered, digitally supported emergency care system. The project outcomes will be validated through a pilot deployment involving selected stakeholders.

Mobile communication networks of the current generation (5G) are, in both practical and regulatory terms, part of the Critical Infrastructure. With the advent of 6G, new application domains will emerge in areas such as energy, transport, and healthcare, further increasing the criticality of mobile communication systems.

Within the project, the medical domain is considered a particularly relevant application area. The objective is to enhance the resilience of mobile-network-based medical applications and thereby improve the security of healthcare delivery in crisis situations. To this end, concepts for 6G campus networks are being developed that enable local island operation in the event of a failure of public networks, or conversely allow critical applications to be offloaded to public networks and cloud infrastructures.

In this way, both the resilience of the mobile communication system itself and the end-to-end resilience from an application perspective are strengthened. In the medical domain, at least two use cases will be implemented and evaluated in order to derive transferable blueprints for other Critical Infrastructures. The focus of ICCAS lies in addressing the specific requirements of medical technology. In this context, the project systematically investigates how future-proof resilience concepts can be integrated into networked healthcare systems in order to reduce their vulnerability and enhance security of supply. To this end, medical, organizational, and technical use cases with high relevance for clinical workflows are identified and analyzed.

The concepts are evaluated in a clinical-like environment within the Health Communication Lab (HeCoLa). For this purpose, the laboratory infrastructure is extended by the public 5G network as well as by resilience demonstrators developed by the project partners. Particular emphasis is placed on interoperable system integration: hospital information systems and mobile end devices are required to communicate seamlessly, relying on established standards such as HL7, FHIR, and DICOM. In addition, ICCAS develops a validation framework that incorporates both technical criteria and functional test scenarios within a clinical usage context.

The project is accompanied by the Federal Office for Information Security (BSI) as an associated partner.

KIMed – the network for artificial intelligence in medicine brings together data-leading institutions, methodological and technical partners, and users from the medical field with the common goal of establishing a powerful and secure research environment and specifically promoting AI innovations. A central project of the network is the design of a protected infrastructure for processing medical data in Saxony. This infrastructure will enable the use of large, networked data sets under strict data protection guidelines. In this way, KIMed creates the conditions for modern AI applications to be developed in a secure structure and, in the future, used in medical research and practice.

The central objectives of the project are:

• Network development and governance: Development of a sustainable network for networking and coordination of research partners, establishment of effective network governance, and initiation of new third-party funded projects by members
• Networking to establish a protected research environment: Development of a Secure Processing Environment (SPE) that enables the secure processing and analysis of sensitive medical data without the need for physical transfer
• KIMed portal for data indexing and networking: Establishment of a central portal for accessing medical data sources, generating synthetic data, and networking existing data sets
• Testing of cooperative demonstrators in the SPE: Development and provision of practical application examples that demonstrate the added value of the protected research environment and offer researchers and clinical partners innovative uses
• Training, further education, and consulting: Design and provision of training courses for all network participants and consulting for partners on the optimal use of network resources

All measures strengthen and combine the future fields of health and digital technology to create an important research and innovation location for the healthcare industry and healthcare provision. The initiative is not only of central importance for the Free State of Saxony, but also has national and international relevance.

KIMed is a joint project of the Technical University of Dresden, the University of Leipzig, and Mittweida University of Applied Sciences. The network aims to actively shape medical progress – for better care, research, and teaching.

KIMed will be funded from April 2025 to December 2027 with €3.6 million from the European Regional Development Fund (ERDF) and tax revenues based on the budget approved by the Saxon state parliament.

Despite major scientific advances, neonatal sepsis and premature infant apnoea remain key challenges in neonatal intensive care medicine. Late-onset sepsis is blood poisoning that occurs at the earliest 72 hours after birth and is usually caused by hospital germs. Apneas (breathing stops) occur in almost all premature babies and often lead to critical drops in oxygen saturation (SpO2}. Although current monitoring makes it possible to detect sepsis and apnea, it requires numerous contact electrodes, which can cause skin damage in immature premature babies. In addition, apnea is often only detected through its effects (drop in SpO2). The aim of the project is to develop a contactless measurement method for recording respiratory movement and body temperature for sepsis and apnea detection, based on the time-of-flight measurement of electromagnetic waves and optical temperature measurement, in order to reduce the number of wired sensors.

The project will investigate whether the expression of a specific gene in gastric tumors can be determined solely from tissue sections after routine staining and using advanced deep learning techniques. This could optimize and potentially replace current methods, which are costly and time-consuming. For this purpose, an extensive data set from an already completed multicenter study and complex neural networks will be used. The results could lead to faster and more cost-effective diagnostics and personalization of treatment for patients with gastric cancer. The models and results will be made publicly available to support further research and possible integration into clinical cancer treatment.

The DaDriv-StAC project aims to develop a digital platform that enables data-driven decision support in the aftercare of stroke patients. The focus is on researching and applying the innovative concept of virtual medical twins. These will integrate and analyse complex health data from different sectors – such as acute hospitals, rehabilitation facilities and the outpatient sector – and compare it with standardized and individual treatment paths. Artificial intelligence (AI) will be used to derive treatment recommendations that support personalized and guideline-based treatment throughout the entire course of care.

As stroke aftercare is particularly challenging due to individual risk factors, secondary diseases and long-term restrictions such as paralysis or speech disorders, DaDriv-StAC aims to improve cross-sector networking and continuous therapy monitoring. To date, there has been a lack of corresponding technological platforms that dynamically update, network and evaluate health data in a meaningful way for those treating patients. The project is the first to implement the concept of a dynamic virtual twin in stroke aftercare, which continuously adapts to the course of treatment and thus enables data-based feedback to practitioners and patients.

Leipzig University Medicine (UML) is playing a central role in this. Together with Actimi GmbH, it is developing the platform and conducting a clinical feasibility study to evaluate acceptance, effectiveness and practicability. In addition, the data collected will be used for medical quality assurance and process optimization.

The technological core of the project is the creation of a new data model that integrates medical, clinical and subjective patient data (e.g. PROMs/PREMs). Based on this, new analysis and decision-making methods will be developed that enable a more precise assessment of risk factors and the comparison of individual progressions with optimal treatment paths.

The project thus addresses key challenges facing the healthcare system: it aims to close information gaps, improve treatment quality and contribute to more efficient, personalized care in the long term. The results will be both scientifically published and prepared for later commercial scaling.

DaDriv-StAC meets the objectives of the funding line for the development of data-driven decision-making and support systems in the healthcare sector and makes an important contribution to digitalization and innovation in medical care.

The SUSTAINET-guarDian project is developing secure, resilient and sustainable mobile communications infrastructures based on a further development of today’s 5G technologies towards a service-based architecture. It deliberately avoids a completely new 6G generation. The focus is on a robust security architecture, automated network control and the targeted use of artificial intelligence (AI) to make networks efficient, reliable and scalable.

Important goals are the standardization of authentication and encryption, protection across untrusted network segments and the protection of AI over its entire life cycle. Cyber resilience is strengthened by modern measures such as homomorphic encryption, quantum key exchange and trustworthy cloud usage.

Another focus is on sustainability and energy efficiency. This includes distributed learning, efficient algorithms and methods for reducing energy consumption. The solutions developed will be tested and optimized using practical demonstrators in the areas of smart grid and medical applications.