The development and application of artificial intelligence (AI) to radiology requires an approach that encompasses a health system. The UK government and National Health Service (NHS) are creating an ecosystem to facilitate academic/industrial partnerships aimed at accelerating the creation of relevant and robust AI tools, which will improve the development and delivery of healthcare imaging. A series of recent initiatives are described, which will drive the development and adoption of AI in clinical imaging.
The VoxTox research programme has applied expertise from the physical sciences to the problem of radiotherapy toxicity, bringing together expertise from engineering, mathematics, high energy physics (including the Large Hadron Collider), medical physics and radiation oncology. In our initial cohort of 109 men treated with curative radiotherapy for prostate cancer, daily image guidance computed tomography (CT) scans have been used to calculate delivered dose to the rectum, as distinct from planned dose, using an automated approach. Clinical toxicity data have been collected, allowing us to address the hypothesis that delivered dose provides a better predictor of toxicity than planned dose. ; JES was supported by Cancer Research UK through the Cambridge Cancer Centre. NGB, ASP and MG are supported by the National Institute of Health Research Cambridge Biomedical Research Centre. KH, MR AMB, EW and SJB were supported by the VoxTox Research Programme, funded by Cancer Research UK. DJN is supported by Addenbrooke's Charitable Trust and Cancer Research UK through the Cambridge Cancer Centre. FMB was supported by the Science and Technology Facilities Council. MPDS was part supported by the VoxTox Research Programme, funded by Cancer Research UK. RJ was part supported by the VoxTox Research Programme, funded by Cancer Research UK. LS is supported by the Armstrong Trust. XC was supported by the Isaac Newton Trust. CBS acknowledges support from the EPSRC Centre for Mathematical and Statistical Analysis of Multimodal Clinical Imaging, the Leverhulme Trust, the EU-RISE project CHiPS and the Cantab Capital Institute for the Mathematics of Information. NT was supported by a Gates-Cambridge Scholarship, funded by the Bill and Melinda Gates Foundation, PLY and SYKS by the Singapore Government.
