Our working groups focus on novel areas of research in the fields of AI, robotics and human-machine systems.
Human-centric AI for medical imaging
The human-centric AI for medical imaging working group aims to build a critical mass to focus and drive research funding and impact cases in the field of machine learning, deep learning and AI in medical imaging. This cross-cutting group will foster novel RD&I collaborations with partners in industry, the public sector, and leading academic institutions and policymakers.
Artificial intelligence promises to transform areas of health and care. The problems that AI tries to solve are associated with big quantities of healthcare data which are too large for humans to analyse effectively but that an AI model can learn from. The use of AI for medical imaging is undergoing extensive evaluation with AI models showing impressive accuracy and sensitivity in diagnostic imaging in particular. The development, validation, commissioning and adoption of AI based solutions for healthcare application is a complex matter which is arguably still to be realised.
Secured digital built environment
The secured digital built environment working groups focuses on understanding the fundamental theory, requirements, and development of standards, systems and tools to apply security principles to ensure safe and sustainable digital built environments.
For many buildings and critical infrastructure, virtual models are becoming more practically utilised for monitoring, controlling and decision making, and in cases have real-time capacity for emergency response and management. Data and system security issues are critical for current and future digital society.
The emergence of BIM (Building Information Modelling) in AEC/O (Architecture, Engineering and Construction/Operation) has revolutionised the construction industry and triggered digital transition through physical asset lifecycles across different sectors. Digital twins have further expanded the area for digitalisation through leveraging IoTs, data-driven approaches, machine learning and AI.