UCL Graduate degrees


Artificial Intelligence Enabled Healthcare MRes + MPhil/PhD

Artificial Intelligence (AI) has the potential to transform health and healthcare systems globally, yet few individuals have the required skills and training. To address this challenge, our Centre For Doctoral Training (CDT) in AI-Enabled Healthcare Systems will create a unique interdisciplinary environment to train the brightest and best healthcare artificial intelligence scientists and innovators of the future.


Key information

Programme starts

September 2020

Modes and duration

Full time: 4 years
Part time: 8 years

Full time: MRes - Year 1 PhD - Years 2-4 Part time: MRes Modules - Year 1 (75 Unuts); MRes Project - Year 2 (105 Units); PhD - Years 3-6.

Application dates

Industry and self-funded students
Open: 1 November 2019
Close: 7 August 2020
Final round deadline (UKRI funding)
Open: 1 November 2019
Close: 7 August 2020
Second round deadline (UKRI funding)
Open: 1 November 2019
Close: 16 February 2020
First round deadline (UKRI funding)

Open: 1 November 2019
Close: 5 January 2020

Tuition fees (2020/21)

£5,365 (FT)
£2,685 (PT)
£24,980 (FT)
£12,470 (PT)

Note on fees: The tuition fees shown are for the year indicated above. Fees for subsequent years may increase or otherwise vary. Further information on fee status, fee increases and the fee schedule can be viewed on the UCL Students website.

Location: London, Bloomsbury

English language requirements

If your education has not been conducted in the English language, you will be expected to demonstrate evidence of an adequate level of English proficiency.

The English language level for this programme is: Standard

Further information can be found on our English language requirements page.

International students

Country-specific information, including details of when UCL representatives are visiting your part of the world, can be obtained from the International Students website.

International applicants can find out the equivalent qualification for their country by selecting from the list below.

Select your country:


The CDT programme consists of a 1 year MRes followed by a 3 year PhD. Throughout this period the CDT will continue to closely monitor the need for continuing training and support, tailored to each student, and provide ongoing training in Research Skills.

The MRes programme covers the core competencies of artificial intelligence and has a central emphasis on how healthcare organisations work. As part of the MRes, you will complete a substantial Masters-level project of your choice, working with a supervisory team that will normally include a clinician and an academic. Projects will be embedded within the NHS setting, with trainees undertaking an immersive clinical experience through our mini-MD programme.

The remaining years will be more like a traditional PhD, which leads to the presentation of a PhD thesis at the end of the fourth year. During your PhD you will remain involved in CDT activities and will continue to work closely with relevant health professionals and clinical teams through our NHS partners and leading academics at UCL.

Research areas

  • AI-enabled diagnostics or prognostics
  • AI-enabled operations
  • AI-enabled therapeutics
  • Public Health Data Science
  • Machine Learning in Health Care
  • Public Health informatics
  • Learning health systems
  • Electronic health records and clinical knowledge management
  • Big Data
  • e-health and m-health
  • Clinical Decision Support Systems

About this degree

MRes Year

Core Modules: 

  • Healthcare Artificial Intelligence Journal Club 
  • Scientific Software Development with Python for Health Research 
  • Dissertation in Artificial Intelligence Enabled Healthcare 

Optional Modules: choose three Masters level modules offered at UCL (all level 7, 15 credits)

  • Principles of Health Data Science 
  • Data Methods for Health Research 
  • Machine Learning in Healthcare and Biomedicine 
  • Advanced Statistical Analysis 
  • Computational Genetics of Healthcare
  • Advanced Machine Learning for Healthcare
  • Information Retrieval and Data Mining 
  • Graphical Models 

Please note: Optional modules may be subject to change.

Please note that the list of modules given here is indicative. This information is published a long time in advance of enrolment and module content and availability is subject to change.

Additional costs

All studentships include a research training support grant, which covers additional research costs throughout students’ time on the programme.

For more information on additional costs for prospective students please go to our estimated cost of essential expenditure at Accommodation and living costs.


Details of the accessibility of UCL buildings can be obtained from AccessAble. Further information can also be obtained from the UCL Student Support & Wellbeing team.


Please visit the UKRI UCL CDT in AI-enabled Healthcare Systems website for current funding information. Most students are fully funded either by UKRI, industry or private funding (which generally includes a stipend, tuition fees and research costs).

For a comprehensive list of the funding opportunities available at UCL, including funding relevant to your nationality, please visit the Scholarships and Funding website.


The distinctive characteristics of our programme allow us to produce graduates who are prepared to:

  • engineer adaptive and responsive solutions that use AI to deal with complexity;
  • innovate across all levels of care, from community services to specialist hospital;
  • be comfortable working with patients and professionals, and responding to their input;
  • appreciate the importance of addressing health needs rather than creating new demand.


The CDT is a new programme. Previous students from our experienced CDT supervisors have tackled projects in AI and healthcare and gone on to successful careers in academia and industry.


Students will be supervised by leading researchers and innovators from both computer science and healthcare, with a key focus on the application of AI to healthcare. 

The CDT is supported by numerous commercial and NHS partners across a range of sectors. It is through the generosity and support of partners that the CDT is able to train the next generation of leaders in AI-enabled healthcare. By working closely with partners, the CDT and its students are able to understand their requirements and formulate projects which match those needs. 

Students are encouraged to undertake internships with relevant organisations.

Why study this degree at UCL?

  • Benefit from UCL’s excellence both in computational science and biomedical research innovating in AI;
  • Be supervised by world leading clinicians and AI researchers in areas related to your research; 
  • Work within a real-world setting, embedded within hospitals, allowing you to gain a practical understanding of the value and limitations of the datasets and the translational skills required to put systems into practice;
  • Have the opportunity to not only apply AI to healthcare, but to apply healthcare to AI, generating novel large-scale open datasets driving methodological innovation in AI;
  • Become a future leader in solving the most pressing healthcare challenges with the most innovative AI solutions;
  • Study at UCL, which was ranked the top university in the UK for research strength in the most recent Research Excellence Framework (2014) and ranked 10th best university in the 2019 QS World University Rankings.

Department: Institute of Health Informatics

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Application and next steps


Students are advised to apply as early as possible due to competition for places. Those applying for scholarship funding (particularly overseas applicants) should take note of application deadlines.

Application deadlines

Industry and self-funded students
7 August 2020
Final round deadline (UKRI funding)
7 August 2020
Second round deadline (UKRI funding)
16 February 2020

First round deadline (UKRI funding)

Open: 1 November 2019
Close: 5 January 2020

For more information see our Applications page.

Apply now

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Page last modified on 13 August 2020