University College London (UCL)
Artificial Intelligence and Medical Imaging MSc
London, United Kingdom
MSc
DURATION
1 year
LANGUAGES
English
PACE
Full time
APPLICATION DEADLINE
EARLIEST START DATE
Sep 2026
TUITION FEES
GBP 36,500 *
STUDY FORMAT
On-Campus
* international students: full time £36,500 | UK students: full time £18,400. Additional fees may apply
This MSc program combines artificial intelligence with medical imaging to prepare students for advancements in healthcare technology. The course covers a range of topics including machine learning, computer vision, and data analysis, with a focus on how these tools can be applied to improve medical diagnostics and treatment. Students gain practical skills through hands-on projects and learn how to work with medical data, algorithms, and imaging techniques. The program aims to bridge the gap between technology and healthcare, equipping students with the knowledge needed to innovate in medical imaging.
The curriculum emphasizes understanding current challenges in medical imaging and developing solutions using AI. It encourages students to critically analyze data and design algorithms to support clinical decision-making. Alongside technical skills, the program also explores ethical considerations and the future role of AI in medicine. This comprehensive approach prepares students for careers in research, industry, or healthcare settings, where they can contribute to smarter, more precise medical imaging methods. The course is designed to support those with backgrounds in engineering, computer science, or health sciences who want to specialize in AI’s role in medicine.
UCL Scholarships
There are a number of scholarships available to postgraduate students, including our UCL Masters Bursary for UK students and our UCL Global Masters Scholarship for international students. You can click the link below to search via the scholarships finder for awards that you might be eligible for. Your academic department will also be able to provide you with more information about funding.
External Scholarships
Online aggregators like Postgraduate Studentships, Scholarship Search, Postgraduate Funding and International Financial Aid and College Scholarship Search contain information on a variety of external schemes.
If you have specific circumstances or ethnic or religious background it is worth searching for scholarships/bursaries/grants that relate to those things. Some schemes are very specific.
Funding for disabled students
Master's students who have a disability may be able to get extra funding for additional costs they incur to study.
Teaching and learning
Your time will be split between lectures, seminars and tutorials, and independent study.
You’ll be assessed through exams, coursework, group work, lab sessions and a research project.
Each module typically consists of around 36-40 hours of lectures and problem classes over a 10-week term. This equates to around 20 contact hours a week.
On top of your timetabled hours, you’ll spend time outside of class reviewing the material and completing coursework. In total, you’ll need to spend approximately 35-40 hours a week on your studies as a full-time student.
Finally, you’ll need to spend a significant amount of your study time on your research project (on average, up to 8 hours a week for full-time students). Exactly how much time you spend on your research project will change from term to term – you’ll spend less time on it in Terms 1 and 2 and then work exclusively on it in Term 3 (the summer term after the exam period).
Modules
Full-time
The taught part of the programme is comprised of mix of compulsory and optional modules.
The first two academic terms consist of the taught modules, with your research project comprising a large part of the programme running from March to September. This is carried out under the supervision of an academic member of staff.
Compulsory modules
- Biomedical Ultrasound
- Information Processing in Medical Imaging
- Programming Foundations for Medical Image Analysis
- MSc Research Project
- Machine Learning in Medical Imaging
- Applied AI in Medical Imaging
- Applied Deep Learning
- Medical Imaging with Ionising Radiation
- MRI and Biomedical Optics
Optional modules
- Computer-Assisted Surgery and Therapy
- Medical Device Enterprise Scenario
- Artificial Intelligence for Surgery and Intervention
- Inverse Problems in Imaging
- Computational Modelling for Biomedical Imaging
- Computational MRI
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.
Students undertake modules to the value of 180 credits. Upon successful completion of 180 credits, you will be awarded an MSc in Artificial Intelligence and Medical Imaging.
What this course will give you
This degree offers you the following benefits and opportunities:
- Develop your skills alongside renowned academics across UCL's Department of Medical Physics and Biomedical Engineering. UCL is ranked 6th in the world for Medicine and 18th in the world for Data Science and Artificial Intelligence (2024 QS World University Rankings by Subject).
- Be part of a world-leading hub for interdisciplinary research and collaborations between computer scientists, physicists, mechanical engineers, biomedical scientists and medical practitioners across UCL and its affiliated teaching hospitals.
- Learn directly from research staff in a close-knit community, with regular opportunities for networking and professional development.
- Get first-hand insight into the latest research taking place globally in this field, in areas like foundation models for medical imaging, image registration and segmentation.
- Build analytical, research and communication skills you can take with you into your career.
- Work on a 9-month-long dissertation structured around the application of AI and medical imaging in healthcare.
- Study in the world's best city for university students (QS Best Student Cities 2024). UCL’s Bloomsbury campus is in the heart of a London district famous for its cultural and educational institutions.
The foundation of your career
Globally, the use of AI in healthcare is rising rapidly. Diagnostic workflows in clinics and other healthcare settings rely on an ever-increasing amount of imaging data (such as x-rays, MRIs and ultrasounds) and their analysis puts time pressure on consultations.
AI technologies can help ease the burden, and provide faster and more accurate diagnoses.
High-tech start-ups and established healthcare providers need to recruit engineers with these skills to drive research and innovation in this rapidly emerging field.
Employability
By the end of this Master’s, you’ll be well placed to pursue diverse careers and opportunities – from academic research to roles in industry and positions that contribute to emerging technologies such as the use of AI in healthcare. Your expertise will be relevant in both industry and public healthcare. You could go on to develop or evolve new technologies as part of a med-tech startup or global company.
This MSc is also an excellent starting point for doctoral studies and a career in research, as you’ll be learning from world-leading UCL researchers at the forefront of healthcare innovations.
The department’s MSc graduates have obtained employment with a wide range of employers in hospitals around the world, and major industry companies such as Elekta, Siemens, Nikon and started research careers at prestigious universities.
Networking
You’ll have regular opportunities to connect, collaborate and build professional contacts as part of your Master’s.
- Benefit from our national and international collaborations across the clinical, industrial and academic sectors. We have close links with many London hospitals, including University College London Hospital, Great Ormond St Hospital, Moorfields Eye Hospital, Royal National Orthopaedic Hospital, Royal Free Hospital, National Hospital for Neurology and Neurosurgery, Royal National ENT and Eastman Dental Hospital, and Whittington Hospital. We also work with organisations like the National Physical Laboratory, Institute of Nuclear Medicine, and Institute of Neurology. A wide range of MedTech companies have spun out of departmental research.
- Work within research groups and across UCL departments to develop your knowledge and skills.
- Network with external partners and showcase outputs to potential employers at private industry events and clinical centres.
- Build your networks further, and socialise, through clubs and societies at UCL, such as the UCL MedTech Society and UCL AI Society.
- Tap into our partnerships with charities, research councils and international organisations.