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Artificial Intelligence Enabled Healthcare MRes

London, Bloomsbury

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, the Artificial Intelligence Enabled Healthcare MRes will create a unique interdisciplinary environment to train the brightest and best healthcare artificial intelligence scientists and innovators of the future.

UK students International students
Study mode
UK tuition fees (2026/27)
£16,800
£8,400
Overseas tuition fees (2026/27)
£35,400
£17,700
Duration
1 calendar year
2 calendar years
Programme starts
September 2026
Applications accepted
Applicants who require a visa: 20 Oct 2025 – 26 Jun 2026
Applications close at 5pm UK time

Applications open

Applicants who do not require a visa: 20 Oct 2025 – 28 Aug 2026
Applications close at 5pm UK time

Applications open

Apply for this course

Entry requirements

A minimum of an upper second class honours undergraduate degree, or a Master's degree in a relevant discipline (or equivalent international qualifications or experience). Our preferred subject areas are Physical Sciences (Computer Science, Engineering, Mathematics and Physics) or Clinical / Biomedical Science. Applicants with a clinical background or degree in Biomedical Science must be able to demonstrate strong computational skills. You must be able to demonstrate an interest in creating, developing or evaluating AI-enabled Healthcare systems.

The English language level for this course is: Level 2

UCL Pre-Master's and Pre-sessional English courses are for international students who are aiming to study for a postgraduate degree at UCL. The courses will develop your academic English and academic skills required to succeed at postgraduate level.

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

If you are intending to apply for a time-limited visa to complete your UCL studies (e.g. Student visa, Skilled worker visa, PBS dependant visa etc.) you may be required to obtain ATAS clearance. This will be confirmed to you if you obtain an offer of a place. Please note that ATAS processing times can take up to six months, so we recommend you consider these timelines when submitting your application to UCL.

Equivalent qualifications

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. Please note that the equivalency will correspond to the broad UK degree classification stated on this page (e.g. upper second-class). Where a specific overall percentage is required in the UK qualification, the international equivalency will be higher than that stated below. Please contact Graduate Admissions should you require further advice.

About this degree

The Artificial Intelligence Enabled Healthcare MRes is a one-year stand-alone course (with a two-year part-time option) that provides training and support in advanced research and practical skills for future researchers or healthcare professionals to fit in the modern clinical environment that requires AI and health data science skills.

The course covers the core competencies of artificial intelligence and has a central emphasis on how healthcare organisations work. Ethical training for medical artificial intelligence will be explicitly emphasised alongside a broader approach to responsible research and innovation.

During the course, you will learn the statistical underpinnings of machine learning theory, get a practical grounding in research software engineering and the principles of healthcare and medical research, develop relationships between academia and the NHS, as well as a thorough treatment of topics in machine learning, statistics and principles of data science.

As part of the MRes, alongside the core and elective modules, you will complete a substantial Master's-level project of your choice, working with a supervisory team that consist of a group of clinicians and academics. As a cohort of the MRes programme, students will also participate in a range of seminars, training programmes, placements and other activities at UCL’s Institute of Health Informatics (UCL IHI).

Who this course is for

The MRes programme is for students with an interest in creating and developing AI solutions that transform and solve healthcare challenges. The programme is ideal for students keen to develop clinical knowledge and algorithmic and programming expertise.

Read about what it takes to study for a Master's at UCL

What this course will give you

  • Study at a university ranked 9th in the world (QS World University Rankings 2026), 3rd in the world for public health (ShanghaiRankings 2024) and rated number one for research power and impact in medicine, health and life sciences (REF 2021).
  • 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.
Read about what you'll get out of a graduate programme

The foundation of your career

We expect graduates to remain within the field of AI and healthcare. Following in the footsteps of previous graduates mentored by our experienced supervisors, they will embark on successful careers in academia and industry. This includes progressing to PhD programmes at top universities, securing positions at high-tech companies focused on healthcare, and working in healthcare departments such as the NHS.

Employability

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 hospitals.
  • Be comfortable working with patients and professionals, and responding to their input.
  • Appreciate the importance of addressing health needs rather than creating new demand.

Networking

UCL IHI research departments collaborate with third-sector and governmental organisations, as well as members of the media, both nationally and internationally to ensure the highest possible impact of their work beyond the academic community. Students are encouraged to undertake internships with relevant organisations where funding permits. Members of staff also collaborate closely with academics from leading institutions globally.

Teaching and learning

Various teaching and learning methods are used to facilitate effective learning and cater to different learning styles. Some of the common types of teaching methods that may be used across the programme include interdisciplinary teaching, lecture-based teaching, practical coding sessions, interactive teaching, project-based learning, and collaborative learning. The use of these teaching and learning methods can vary depending on the subject matter and the goals of the programme. Our educational programme incorporates a mix of these methods to cater to the diverse needs of learners and create a well-rounded learning experience.

Assessment methods are crucial components of an educational programme, as they evaluate students' understanding, knowledge, skills, and application of concepts. The types of assessment methods that may be used across the programme include exams, coursework, coding exam, collaborative project, presentation and Q&A, research proposal, dissertation writing, and online quizzes and tests. 

The use of assessment methods will vary based on the nature of the programme and the subject matter. A well-balanced combination of assessment types ensures that students' diverse abilities and learning styles are appropriately evaluated while providing a comprehensive understanding of their progress and achievements.

During the full-time MRes, two to four hours of your time is spent in tutorials per week and/or, six to eight hours in lectures per week, and a further 20-24 hours in independent study per week.

Modules

Considering the diverse backgrounds of MRes students, who come from both clinical and computational fields, we offer a comprehensive, project-based learning and training programme. Students can begin with a Python module in Term 1, and then deepen their knowledge in health data science during Terms 2 and 3. Alternatively, students with prior AI experience can enhance their skills through the Data Methods, Genetics and Group AI Project in Terms 1 and 2, followed by advanced courses in machine learning, and statistics in Term 3.

Students studying the programme full time will be expected to complete 180 credits during the academic year. 

Part-time students have the flexibility to choose when to complete the four optional modules and the compulsory Journal Club module. In Year 2, students will primarily focus on the compulsory Dissertation module, which is the most important outcome of the programme.

Students studying the programme part time will be expected to complete 180 credits across two academic years. 

Compulsory modules




Optional modules











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 are subject to change. Modules that are in use for the current academic year are linked for further information. Where no link is present, further information is not yet available.

Upon successful completion of 180 credits, you will be awarded an MRes in Artificial Intelligence Enabled Healthcare.

Accessibility

The department will endeavour to make reasonable adjustments for students with disabilities, including those with long-term health conditions, neurodivergence, learning differences and mental health conditions. This list is not exhaustive. If you're unsure of your eligibility for reasonable adjustments at UCL, please contact Student Support and Wellbeing Services.

Reasonable adjustments are implemented on a case-by-case basis. With the student's consent, reasonable adjustments are considered by UCL Student Support and Wellbeing Services, and where required, in collaboration with the respective department.

Details of the accessibility of UCL buildings can be obtained from AccessAble. Further information about support available can be obtained from UCL Student Support and Wellbeing Services.

For more information about the department and accessibility arrangements for your course, please contact the department.

Visit us

All open days and events

South Cloister, Wilkins Building, Gower Street, London, WC1E 6BT, United Kingdom - Open day

Life and Medical Sciences Postgraduate Open Evening

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Join us on campus to discover our Master’s degrees in Brain Sciences, Life Sciences, Medical Sciences, Medical Engineering, Population Health Sciences and Medical Anthropology. This event is ideal for prospective postgraduate students interested in Brain Sciences, Life Sciences, Medical Sciences, Medical Engineering, Population Health Sciences and Medical Anthropology.

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Graduate Open Events: Artificial Intelligence Enabled Healthcare MRes

Studying at the Institute of Health Informatics opens doors to impactful careers across healthcare, research, and the rapidly evolving field of digital health. Join our virtual open events to explore our Master’s degree in Artificial Intelligence Enabled Healthcare, meet Programme Directors, and learn how to make your application stand out.

Watch on demand event

Fees and funding

Fees for this course

UK students International students
Fee description Full-time Part-time
Tuition fees (2026/27) £16,800 £8,400
Tuition fees (2026/27) £35,400 £17,700

Postgraduate Taught students benefit from a cohort guarantee, meaning that their tuition fees will not increase during the course of the programme, but UCL reserves the right to increase tuition fees to reflect any sums (including levies, taxes, or similar financial charges) that UCL is required to pay any governmental authority in connection with tuition fees.

The tuition fees shown are for the year indicated above. Where the course is offered on a flexible/modular basis, fees are charged pro-rata to the appropriate full-time Master's fee taken in an academic session. Further information on fee status, fee increases and the fee schedule can be viewed on the UCL Students website: ucl.ac.uk/students/fees.

Additional costs

There are no additional costs for this programme.

For in-person teaching, UCL’s main teaching locations are in zones 1 (Bloomsbury) and zones 2/3 (UCL East). The cost of a monthly 18+ Oyster travel card for zones 1-2 is £119.90. This price was published by TfL in 2025. For more information on additional costs for prospective students and the cost of living in London, please view our estimated cost of essential expenditure at UCL's cost of living guide.

Funding your studies

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

See all the ways you can fund your studies

Institute of Health Informatics PGT Home Fees Studentship 2025

Deadline: 31 July 2026
Value: Full Home tuition fees (1 year)
Criteria Based on both academic merit and financial need
Eligibility: UK

Sparck AI Scholarship

Deadline: 1 May 2026
(Check course is eligible before applying)
Value: Full tuition fees plus £22,780 stipend (1 year)
Criteria Based on academic merit
Eligibility: UK, EU, Overseas

Next steps

Note for applicants: When applying on UCL Select, please select MRes Artificial Intelligence enabled healthcare to apply for programme.

Please note that you may submit applications for a maximum of two graduate courses (or one application for the Law LLM) in any application cycle.

Apply for this course

Got questions? Get in touch

Institute of Health Informatics
Institute of Health Informatics

ihi.education@ucl.ac.uk

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