Training

Both the UKRI AI Centre for Doctoral Training in Digital Healthcare and the UKRI Centre in AI for Healthcare at Imperial College London focus on healthcare applications of core AI to train AI PhDs and Clinical PhD Fellows. We will deliver training that integrates the development of technical skills with an appreciation for approaches to human-in-the-Loop AI design that are socially and ethically acceptable. The term “AI” means for us the development of intelligent systems that embody a practical solution. Amongst contemporary AI approaches, Machine Learning methods have shown to yield powerful solutions which work in purely data-driven manners and link via data science to emerging biomedical research methodologies. However, practical solutions involving AI will require a broader approach and we will drive technical innovation by providing broad training for exploitation of multiple technological strategies within the broader realm of AI, including, Machine Learning, Logic-based, Computer Vision or Natural Language Processing methods.

Expert Mentors

The exposure of our students to our expert mentors in related areas such as computer vision (e.g. for digital pathology and diagnostics) as well as sensing and wearable technologies (e.g. mobile mental health and digital biomarkers) will enhance the depth of training we can offer. Ultimately, no technological progress can help patients unless it is transformed into a (regulated) product or service. The UKRI CDT in AI for Healthcare plays to the established strength of the investigators and the whole supervisory team in leading cross-disciplinary research spanning AI and healthcare. Students will have at least two supervisors one with AI and one with biomedical background, and the two supervisors will be normally from two different Faculties/Departments – thus following our tried-and-tested crossdisciplinary training tradition at Imperial. The Centre will leverage the research excellence of its 100+ participating potential PhD supervisors across Imperial and our partners.

Partnerships

We have partnered closely with patient-focused charities, NHS trusts and a range of industries to ensure that students are trained in how to entrepreneurially realise impact from their work and understand how to navigate the regulatory pathways. One approach to delivering this will be for our PhD students to form co-creation teams including world-leading AI researchers, clinicians across NHS trusts through our Academic Health Science Centre (Imperial Healthcare NHS Trust,  Royal Marsden NHS Foundation Trust), industry stakeholders and patient organisation partners. Moreover, we will embed clinicians as Clinical PhD Fellows into the Centre’s PhD cohorts to foster a shared understanding between doctoral researchers developing and applying AI approaches and those using the technology offering a unique level of cognitive diversity and complementary in skills that will stimulate students to learn from each other in our co-training groups create a unique environment for research and training.

Integrated Training

Our large and diverse PhD cohorts regular PhD students and clinical PhD Fellows will benefit from an integrated training program and the ability to offer our students a unique sandbox in which to build their PhD success from AI and Healthcare applications. This is enabled through a PhD co-creation process by pairing students with AI and healthcare supervisors as well as industry and healthcare partners. The 4-year long PhD training programme (the programme duration for clinical PhD fellows is 3 years) is split into three phases that provide underpinning skill training (Foundation phase), research training (Research Phase) and finally drive PhD impact training (Impact phase).

The Foundation Phase starts with a Welcome Week with plenty of cohort-building and social activities. The students and their project teams will start their regular meetings and the student-led co-training teams will define their outline topics in the first month. The underpinning AI training is delivered by taking AI-relevant taught modules and the Research Tutorial. Lastly, the Research Planning Report needs to be submitted during the Foundation Phase.

AI Foundation Courses: AI foundation courses (taught modules) are typically selected from established degree programmes in the Department of Computing. For example, in 2023-24 students took the modules Python Programming, Deep Learning and Ethics, Fairness and Explanation in AI. The Research Tutorial in AI for Health is a bespoke module which is core for all AI4Health CDT students. The aim of this course is to evaluate research and to develop critical research thinking. 

Research Planning Report: PhD students will be required to submit their Research Planning Report, normally within 3-3.5 months of starting. The research plan is an important document to support defining the PhD project. 

Professional skills development training: All Imperial College PhD students are required to complete the Professional skills programme of the College’s Graduate School. Details are here.

During the CDT’s Research Phase students will focus on their research project with their co-creation team. Technical training will shift to project-based training, emphasis reflected in the technical masterclasses as well as the activities of their students AI-centred co-training teams (e.g. journal tutorials). The Research phase sees three major milestones which need to be completed and passed, according to Imperial College’s academic milestones’ policy in order to progress the PhD registration. 

Early Stage Assessment (ESA): All PhD students at Imperial College are required to complete the Early Stage Assessment (ESA) which includes ethical and regulatory implications of their research, as well as preliminary results of their work. The ESA milestone must normally be completed by the end of the first year of the PhD. It consists of a comprehensive report and a seminar.  

Late Stage Review (LSR): The LSR is completed by the end of the second year of the initial registration. Normally, students would give a presentation detailing achievements and a plan to finish the PhD.

By the middle of their 3rd year (30 months), AI4Health CDT students are required to confirm progress by confirming publications.

36-Months Progress Review: At 36 months of full-time registration, all PhD students at Imperial College must complete this formal progress check. The aim of this review is to help students on their way to the successful and on-time completion of their PhD and to ensure that there is a realistic plan for submitting the thesis within maximum 48 months of the start date, i.e. This milestone requires to show that there is a plan of work for fully completing the PhD thesis.

Also at 36 months, CDT students will be asked to submit three complete PhD chapters, and focus on the impact and/or real-world applications their research could have by providing an outline Plan
to Impact and what potential impact this plan could have.

Writing-up and PhD Exam Entry procedures: Imperial College has a Writing-up and PhD examination entry process which students would be encouraged to familiarise themselves. Some students may decide to formally move into the Writing-up status, and start writing up their thesis. Details can be found here.

In this phase, monitoring the write-up progress and activity proposed in the impact plan takes centre stage.

PhD students often start thinking about preparing a completion talk or presenting their work to a relevant audience. In line with Imperial College Research degree regulations, the maximum thesis submission date is within 48 months of registration. Details of the Imperial College Academic and Examination Regulations for Research Degrees are here.

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Student-led training will organise the cohort in co-training teams. Cohort cohesion after year 1 is maintained by regular cohort-wide seminars, events and activities. There may also be special sessions organised by the Imperial Clinical Academic Training Office (CATO), which will be open to our CDT students.