CDHT Themes
The CDHT ecosystem carries out world class research and development within the following areas:
From Improved Healthcare Diagnostics to Medical Device Design
Healthcare digitisation enabling innovative patient centred care pathways – including technologies such as telehealth, in-silico medicine, Smart Apps, Living Labs, Ambient Assisted Living (AAL), Cloud, Small Signal Analysis (ECG/EEG) and Internet of Things (IoT).
Applying AI to optimise the design of medical devices and diagnostics e.g., prosthetics and implants; the development of ‘Smart Medical Devices’ for cardiac to ophthalmology systems; application of novel biomarkers for multiplexed – comorbidity, diagnostic POC devices.
Use of synthetic data, digital twinning and robotics in the future of next generation biomaterials formulation and biomanufacturing via bio-printing / 3-D printing / and synthesis.
Use of Biophotonics and nanotech in personalised diagnosis, therapies and surgical applications to improve the treatment of diseases e.g., miniature living lasers, integrated microfluidics, implantables; wireless remotely controlled implantable, modelling and cellular optoelectronics for novel imaging and wearables; hyperspectral imaging; ophthalmology applications or sensing with high spatial and temporal resolution particularly in areas such as cellular and extracellular vesicle (EV) signatures.
Applying AI and machine learning to optimise medical devices for diagnostics e.g., application of smart medical devices using novel extracellular vesicle (EV) biomarkers for multiplexed – comorbidity diagnostic POC devices
Utilising Generative Artificial Intelligence, LLM, Natural Language Processing, Data Analytics and Machine Learning.
Development of a TRE (Trusted Research Environment) service to provide approved researchers from trusted organisations with timely and secure access to health and care data.
Use of Artificial Intelligence ranging from Machine Learning to Deep Learning for Augmenting Clinical Workflows including Disease
Diagnosis, Patient Risk Stratification and Patient Monitoring, Effective Behaviour Change, Anomaly Detection.
Use of generative Artificial Intelligence to improve health service resource planning and allocation, anticipate public-health needs and interventions, and execute programs more effectively.
Natural Language Processing and Data Analytics in healthcare - using AI to extract value-added outcomes from medical literature and pathology reports.
Integration of digital healthcare applications into ECR systems.
AI approaches to Data Governance and Regulatory.
Utilising AI; UX design; Digital Upskilling and Reskilling
The deployment of AI for decision-support to mitigate against high-risk behaviours
The Digital Patient Experience – digital tools to provide patients with accurate information about their condition, treatment options, medications, and management strategies. Use of PPI methodologies to ensure the digital tools are based on the needs of patients.
Enhancing digital literacy and leveraging behavioural change technologies and platforms to address resistance to digital transformation at patient level.
UX design in Clinical Living Lab and digital healthcare to provide patients with better, more comfortable, and safer services and includes aspects of behaviour change theory to maximise the potential impact of the tools being developed.
Use of behavioural change technologies and platforms to measure resistance to digital change and transformation (system level).
Empowering healthcare professionals through digital upskilling (especially AI in healthcare & Innovation) which can ultimately improve patient care and increase reach in healthcare accessibility, including the use of VR / AR in skills development.
Development of smart AI controlled robotic laboratories to develop new formulation and processes for next generation sensing.