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Artificial Intelligence Enabled Healthcare MRes + MPhil/PhD

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, 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.

UK students International students
Study mode
UK tuition fees (2024/25)
£6,035
£3,015
Overseas tuition fees (2024/25)
£31,100
£15,550
Duration
1 calendar year
2 calendar years
Programme starts
October 2024
February 2025
May 2025
Applications accepted
Round 1: 16 Oct 2023 – 24 Feb 2024

Applications closed

The Centre for Doctoral Training recruits in at least two rounds. Applicants are advised to apply early, priority will be given to those who have applied in round one.

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 programme 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

Every student who is accepted onto the AI-enabled Healthcare Systems Centre for Doctoral Training (CDT) must take the MRes Artificial Intelligence Enabled Healthcare in their first year.

This will be 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 is not currently available as a stand-alone programme.

The MRes programme 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, innovation and entrepreneurship.

During the MRes year, students 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, as well as a thorough treatment of topics in machine learning, advanced statistics and principles of data science.

As part of the MRes, alongside the core and elective modules, 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. The project you work on during your MRes normally leads to the chosen PhD research topic.

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.

As a cohort based PhD programme, students will also have the opportunity to participate in a range of seminars, training programmes, placements and other activities, including UCL's Doctoral Skills Development Programme.

Training Opportunities
The CDT programme consists of a range of activities and events including:

  • A Mini-MD programme where trainees undertake an immersive clinical experience within an NHS setting
  • Annual CDT Conference
  • Seminar series
  • PPI Training
  • UCL Training in:
    • Responsible Research & Innovation
    • Communication Skills
    • Entrepreneurship
    • Ethical Training
  • The opportunity to attend training programmes offered by the Alan Turing Institute
  • Opportunities for internships and placements with industry partners

More information can be found on the CDT Website.

Who this course is for

The Centre for Doctoral Training programme is for students with an interest in creating and developing AI solutions aimed to transform and solve healthcare challenges. The CDT programme is embedded within a NHS setting, and should appeal to students keen to develop clinical knowledge and algorithmic/ programming expertise.

Read about what it takes to become a UCL graduate research student

What this course will give you

  • 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 is rated No.1 for research power and impact in medicine, health and life sciences (REF 2021) and 9th in the world as a university (QS World Rankings 2024).
Find out how your research could have an impact

The foundation of your career

We do not yet have any graduates from the four-year programme, our first cohort of students will be graduating over the next few months. We expect them to stay within the field of AI and healthcare, and much like previous graduates from our experienced CDT supervisors, they will go onto successful careers in academia and industry. 

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

The Institute's 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 do 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 employed to facilitate effective learning and cater to different learning styles. Below are some common types of teaching methods that may be used across the programme:

Interdisciplinary Teaching:
Interdisciplinary teaching involves integrating knowledge and skills from multiple disciplines or subject areas to provide a comprehensive understanding of a topic, particularly AI and healthcare. This approach encourages students to make connections between different subjects and fosters critical thinking and problem-solving abilities.

Lecture-Based Teaching:
Lecture-based teaching is a traditional method where the instructor presents information to students through spoken words. It involves the teacher sharing knowledge, concepts, and theories, while students take notes and listen actively. This method is effective for conveying large amounts of information and providing foundational knowledge.

Practical Coding Sessions:
Practical coding sessions are hands-on learning experiences where students actively engage in coding exercises, programming tasks, and problem-solving activities. These sessions are essential for AI and programming-related subjects (machine learning, etc) as they allow students to apply theoretical knowledge to real-world scenarios.

Interactive Teaching:
Interactive teaching methods encourage active participation and engagement from students. These methods can include discussions, debates, group activities, and case studies, in particular in several modules such as Journal Club. Interactive teaching fosters collaboration, communication skills, and a deeper understanding of the subject matter.

Project-Based Learning:
Project-Based Learning involves assigning students long-term projects that require them to investigate and address real-world problems or challenges (such as AI & healthcare group project). It enhances critical thinking, research skills, and creativity while promoting independent learning and teamwork.

Collaborative Learning:
Collaborative learning involves students working together in small groups or pairs to solve problems, discuss ideas, and complete tasks. This method promotes teamwork, communication, and the exchange of diverse perspectives.

The use of these teaching/learning methods can vary depending on the subject matter, the goals of the programme, and the preferences of the instructors in the MRes year. Our educational programme incorporates a mix of these methods to cater to the diverse needs of learners and create a well-rounded learning experience.
 

Compulsory Modules:

CHME0033 Dissertation in Artificial Intelligence Enabled Healthcare

CHME0032 Healthcare Artificial Intelligence Journal Club

Optional Modules

CHME0012 Principles of Health Data Science

CHME0013 Data Methods for Health Research

CHME0015 Advanced Statistics for Records Research

CHME0016 Machine Learning in Healthcare and Biomedicine

CHME0031 Programming with Python for Health Research

CHME0034 Computational Genetics of Healthcare

CHME0035 Advanced Machine Learning for Healthcare

CHME0039 Artificial Intelligence in Healthcare Group Project

COMP0084 Information Retrieval and Data Mining

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.

Assessment methods are crucial components of an educational programme, as they evaluate students' understanding, knowledge, skills, and application of concepts. Here are various types of assessment methods that may be used across the programme:

Exams:
Traditional exams are a common assessment method that tests students' knowledge and understanding of the course material. These exams typically involve a time-bound written assessment, where students respond to questions related to the subject matter.

Open-Book Exam:
In an open-book exam, students are allowed to refer to their textbooks, notes, or other resources during the assessment. The questions in these exams are often designed to test higher-order thinking and problem-solving abilities, as students have access to reference materials.

Coursework:
Coursework assessments involve various assignments, essays, reports, or projects that students complete throughout the course. These assessments may cover specific topics or practical applications and help to assess students' comprehension and critical thinking skills.

Coding Exam:
A coding exam is specifically designed for courses related to computer science, software development, or programming. Students are given coding challenges or programming tasks that assess their coding proficiency and problem-solving abilities.

Collaborative Project:
In a collaborative project assessment, students work in groups to tackle a complex problem or complete a substantial task. This assessment measures teamwork, communication, time management, and the ability to achieve shared goals.

Presentation and Q&A:
Presentations require students to deliver a talk on a given topic or project. The presentation assesses their ability to communicate effectively, organize information, and present ideas coherently. Often, a question and answer (Q&A) session follows the presentation to delve deeper into the topic.

Research Proposal:
A research proposal is a preliminary plan for a research project that students submit to demonstrate their research capabilities. It outlines the research question, objectives, methodology, and potential outcomes of the study.

Dissertation Writing:
Dissertation writing is typically reserved for higher education levels, such as undergraduate and postgraduate studies. It involves an extended research project on a specific subject, allowing students to demonstrate research, analytical, and academic writing skills.

Online Quizzes and Tests:
Online quizzes and tests are digital assessments that may be used for formative or summative purposes. They are often employed in blended or online learning environments.

The use of assessment methods will vary based on the nature of the programme, the subject matter throughout the MRes year. 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 MRes 4 hours of a student's time is spent in tutorials per week and/or, 6-8 hours in lectures per week, and a further 20-24 hours in independent study per week.

Research areas and structure

  • 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

Research environment

Our research environment offers a unique degree programme that stands out among competitors. We provide students with the exceptional opportunity to explore the cutting-edge intersection of AI technology and healthcare applications. Our curriculum emphasizes research and innovation skills, empowering students to become independent researchers and adept problem solvers. A key difference is our close collaboration with clinicians and front-line practitioners. This interaction fosters a holistic understanding of healthcare challenges and real-world applications, ensuring that our graduates are equipped with practical knowledge and solutions. Our programme is inclusive, welcoming students from both computational and clinical backgrounds, creating a diverse and dynamic learning environment.

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

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

Accessibility

Details of the accessibility of UCL buildings can be obtained from AccessAble accessable.co.uk. Further information can also be obtained from the UCL Student Support and Wellbeing Services team.


Fees and funding

Fees for this course

UK students International students
Fee description Full-time Part-time
Tuition fees (2024/25) £6,035 £3,015
Tuition fees (2024/25) £31,100 £15,550

The tuition fees shown are for the year indicated above. Fees for subsequent years may increase or otherwise vary. Where the programme 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

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.

Funding your studies

Please visit the CDT website for funding information.

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

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 programmes (or one application for the Law LLM) in any application cycle.

Got questions? Get in touch

Institute of Health Informatics
Institute of Health Informatics

aihealthcdt@ucl.ac.uk

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