Our vision is to establish a world leading centre for PhD training of next-generation innovators in Artificial Intelligence (AI) applied to Healthcare. We take the view that AI is ultimately a question about how to realise artificial systems that solve problems presently requiring human intelligence to solve (e.g. problems solved by clinicians, nurses and therapists). The contemporary answer to this question machine learning, i.e. learning how to solve the problem from experience instead of programming it. Learning in turn requires data, which extends our view of AI to encompass also data science and data engineering.
AI in our centre implies an entire system that solves a practical (healthcare) problem, this may require combining different algorithmic and representational approaches that encompass not only machine learning but also causal inference & symbolic reasoning, natural language processing, computer vision etc. Tackling healthcare challenges requires learning how to bridge our understanding of the clinical language and methodologies, the regulatory, legal and ethical frameworks of healthcare with core AI technical skills. Our training outcomes are PhDs that have learned to move fluidly across the disciplinary AI/healthcare boundaries and can develop and implement deployable solutions - therefore we have paired with two BRCs, three NHS Trusts and the NHS Digital Academy, among other partners to deliver unique healthcare training.
Our bespoke training program will enable our CDT students to emerge from their PhD as future leaders in AI industry and healthcare able to bring about transformative change by equipping them with the technical AI skills, an understanding of regulation and embedding them in an ecosystem of industry, accelerator, regulator and healthcare partners.
Our PhD training has 4 broad healthcare themes that leverage AI in healthcare: (1) Making healthcare provision more efficient and effective by increasing the productivity of doctors and nurses or patients through assistive technology; (2) Developing AI based diagnostics & monitoring that can e.g. detect disease earlier and monitor health with higher precision; (3) AI-based decision support systems that will e.g. free up doctors’ and patient’s time to focus on key challenges or optimising the delivery or development of treatments (4) AI that accelerates patient-centric drug and treatment development. These CDT’s research themes arise from our engagement with our healthcare partners (Imperial's 3 affiliated NHS trusts Imperial Healthcare NHS Trust, Royal Brompton & Harefield NHS Foundation Trust, Royal Marsden NHS Foundation Trust ), technology companies and patient organisations. We thus look forward to see the full breadth of our partners medical capabilities reflected in our potential topics.
Each PhD student will have (at least) two supervisors - one AI supervisor and one Healthcare supervisor that will be in two different Faculties or Departments. Our view is to prefer supervisor pairs that are from two different faculties but we also allow supervisors from the same faculty in two different Departments (this rule also accommodates the increasing number of colleagues with joint appointments across two faculties).
The supervisor pool currently comprises:
Additional healthcare supervisors can be from clinicians in:
with or without Imperial Honorary Contracts (we can sponsor an Imperial College Honorary Contract as needed through the kind support of the Academic Health Sciences Centre)
We run an open CDT model, so other Departments at Imperial or individual supervisors can join, subject to them subscribing to our CDT’s financial model (please contact our CDT Management team (email@example.com).
PhD students will apply to the CDT which reviews and ranks them via our CDT (composed of academics, such as Directors of Post-graduate Research or deputies of some of the participating departments). The CDT will then rank the best X students and offer them to choose from a set of Y projects, where Y>>X and X approx 15-20 per annum.
The CDT we will not rank projects per se but instead the CDT research board will check for remit and feasibility. Admitted students will rank their preferred projects. Once we know which students have ranked their projects we will contact the Departments of the selected projects’ supervisors. The Department/Trust will then check that the supervisor/student is supportable and feeds the information back to the CDT, so we can inform students of the highest ranked project that is available to them. Only when this pairing is accepted by the student and the supervisor we proceed with a full offer.
Exceptionally, in this first year, 2019, intake we have compressed timelines as the UKRI CDTs decision arrived months later than for the EPSRC CDT (e.g. the award letter arrived late in April). Therefore, we would like to also consider students that have been in your Departments regular PhD pipeline for an October 2019 start and see if their project can be sensibly transformed into a CDT project (if you want them to be considered, please ask them to apply as instructed on the CDT website). All students (direct applicants via our website and those in Departmental pipelines) will be ranked by our CDT admissions panel together and the top students considered for funding by the CDT. We will close the first round of student intake by July 14th 23:59 BST. The AI4Health admissions team is reachable under firstname.lastname@example.org .
The PhD milestones (eg ESA, LSR) are structured as cohort-based activities, and thus the CDT will run and organise these.
The CDT has a student progression/milestones/monitoring process in line with College regulations and thus are very similar to the 11 participating Departments (ESA around month 9, exit before month 12 deadline; LSR by month 24) these have been reviewed by the Dept of Computing as host of the CDT and are now with Registry for approval (after their review). Similarly, as required by UKRI we have an exit degree (an MRes) that students who want to/have to leave in the first year can obtain.