| EPSRC Reference: |
EP/Y030869/1 |
| Title: |
UKRI AI Centre for Doctoral Training in Biomedical Innovation |
| Principal Investigator: |
Simpson, Professor I |
| Other Investigators: |
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| Researcher Co-Investigators: |
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| Project Partners: |
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| Department: |
Sch of Informatics |
| Organisation: |
University of Edinburgh |
| Scheme: |
Centre for Doctoral Training |
| Starts: |
01 April 2024 |
Ends: |
30 September 2032 |
Value (£): |
7,932,184
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| EPSRC Research Topic Classifications: |
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| EPSRC Industrial Sector Classifications: |
| Healthcare |
Pharmaceuticals and Biotechnology |
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Summary on Grant Application Form |
The greatest challenge to realising the potential of artificial intelligence in biomedicine is its translation to real-world use. Our vision is to create an AI Centre for Doctoral Training in Biomedical Innovation (AI4BI) to produce inter-disciplinary researchers with both technical skills and domain knowledge, and with experience delivering innovation into the public and private sectors. To achieve this, we need to train researchers who can successfully design, develop, and implement AI approaches in partnership with external stakeholders such as the UK National Health Services, pharmaceutical, and biotechnology companies.
Our research will focus on the largest application areas for AI in biomedicine; 1.) biomedical imaging - AI can be used to detect regions of interest in images such as X-rays, MRI scans, Ultrasounds, or retinal scans that can help clinicians decide on approaches for surgery or treatment with specific drugs. 2.) biomedical engineering - AI can be used to aid the design of medicines and vaccines and to improve structural and molecular simulations of biological processes helping researchers to understand mechanisms of disease and drug action. 3.) biomedical & health informatics - AI methods are well suited to integrating diverse data such as genomic, medical record, imaging, and sensor data at scale into predictive models that are more informative than those created from a single data source. 4.) genomic medicine - AI has the potential to capture causal relationships between people's clinical features and their genomic sequences which is central to efforts to predict and diagnose disease and its causes. The successful development and adoption of novel AI technologies in all of these application areas has the potential to revolutionise our understanding of disease, accelerate the development of new treatments, and improve the diagnosis and treatment of patients.
The UK has a world- leading environment for biomedical research and innovation and has some of the most advanced and highly regarded health services and systems in the world. For us to realise the potential of AI in this domain as envisioned in the governmental UK National AI Strategy (2021) and UK Life Sciences Vision (2021) we need to address critical skills shortages that have been identified by the BBSRC, MRC, and the Association of British Pharmaceutical Industries in computational and digital skills. This CDT will play a significant role in increasing the number of early career researchers that have those skills and who are quipped to ensure that the AI tools and methods they develop are widely adopted.
Our CDT will adopt a 4-year PhD programme with integrated study where core skills are taught using cohort-based approaches, supplemented by a flexible elective programme of advanced courses in biomedical, clinical, computational, statistical, and mathematical topics. Most assessed learning will take place over the first two years with students able to pick-up additional courses tailored to any emerging needs in the third year. We will run a complimentary training and activities programme in transferable skills, responsible research innovation, ethics, and public outreach. All PhD projects will involve an external partner ensuring that research is well aligned with societal and commercial needs. Students will undertake a group research project to develop inter-disciplinary working skills, facilitate peer-peer learning, and strengthen the cohort effect and a 3-month research placement project with an external partner. They will be able to take part in the "Venture Builder Incubator" programme where they will develop entrepreneurial skills whilst embedded in their research projects. We have designed our programme to maximise the ability of students from different academic backgrounds to transition effectively into applied AI researchers and for them to benefit from the inter-disciplinary experience, skills, and ideas of other cohort members.
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
| Summary |
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| Date Materialised |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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| Project URL: |
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| Further Information: |
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| Organisation Website: |
http://www.ed.ac.uk |