Daiser unveils collaboration with EPSRC's EdgeAI hub for the progress of digital healthcare innovation
In a groundbreaking move, Daiser, a pioneering digital health platform, has joined forces with The Engineering and Physical Sciences Research Council (EPSRC) EdgeAI Hub to advance Edge AI and Digital Twin technologies in healthcare. This partnership aims to leverage edge computing's capabilities in processing data locally on devices close to patients alongside AI-enhanced Digital Twins—virtual replicas of patients or systems—for improved diagnostics, treatment planning, and operational efficiency in healthcare environments.
The collaboration is focused on addressing unique privacy and security challenges in digital healthcare, ensuring data privacy and security, enhancing data quality, and improving the resilience of healthcare systems against cyber threats.
Since its launch in 2024, Daiser has been at the forefront of digital healthcare innovation. The EPSRC's EdgeAI Hub, dedicated to uniting the UK's expertise in artificial intelligence and edge computing, shares this vision. Professor Rajiv Ranjan, Director of the EdgeAI Hub, expressed that collaborating with Daiser allows them to apply their research in edge AI to real-world healthcare challenges.
One of the key applications of this collaboration is the development of Digital Twins in healthcare. These dynamic, continuously updated virtual models of patients integrate physiological, psychological, and behavioral data measured by sensors and biosensors. They support timely and accurate diagnoses and personalized treatments by simulating patient responses and risk factors in real time.
Edge AI enables real-time processing of vast health data streams from wearable sensors and Internet of Medical Things (IoMT) devices at or near the source. This enhances responsiveness and privacy by generating instant alerts and supporting clinical decision-making during surgeries or routine monitoring without relying solely on cloud computing.
The partnership also promises to optimize clinical trials by simulating patient outcomes and creating virtual control arms, making trials more efficient and patient-centric. This reduces required participant numbers and accelerates development timelines by providing predictive modeling of individual responses to standard care and treatments.
In the realm of surgical and procedural enhancements, AI-driven Digital Twins can generate patient-specific 3D anatomical maps for surgical planning and navigation, improving precision, safety, and outcomes during interventions. The partnership also seeks to optimize device sizing and placement in cardiac procedures, reducing complications and improving long-term outcomes.
Operational improvements are another area of focus, with Edge AI systems facilitating hospital administration tasks such as scheduling, billing, and patient communication through AI-powered assistants deployed at the edge, ensuring resilience even with unstable internet connections.
The joint efforts of Daiser and the EPSRC EdgeAI Hub mark a significant step toward developing secure, efficient, and patient-centric healthcare solutions. This approach aligns with current research trends highlighting Digital Twins powered by AI at the edge as pivotal for next-generation healthcare systems.
[1] Digital Twin in Healthcare: A Review. International Journal of Environmental Research and Public Health. 2021. [2] AI in Medical Imaging: Applications and Challenges. Springer Nature. 2020. [3] Edge Computing for Healthcare Applications: A Survey. IEEE Access. 2019. [4] Virtual Clinical Trials: A Review. Journal of Medical Internet Research. 2020.
- This partnership between Daiser and the EPSRC EdgeAI Hub is aimed at advancing patient care through the development of Digital Twin technologies, applying edge AI research to real-world healthcare challenges, and ensuring data privacy and security.
- One of the avenues for application of this collaboration is the development of AI-driven Digital Twins in healthcare, which can generate patient-specific 3D anatomical maps for surgical planning, optimize device sizing and placement in cardiac procedures, and support timely and accurate diagnostics.
- The collaboration also seeks to optimize operational efficiency in healthcare environments by leveraging edge computing's capabilities in processing data on medical devices, improving the resilience of healthcare systems against cyber threats, and enhancing efficiency in hospital administration tasks through AI-powered assistants.
