University of Birmingham - College of Medicine and Health
MSc in Artificial Intelligence Implementation (Healthcare)
Birmingham, United Kingdom
MSc
DURATION
2 years
LANGUAGES
English
PACE
Full time, Part time
APPLICATION DEADLINE
EARLIEST START DATE
TUITION FEES
GBP 30,370 / per year **
STUDY FORMAT
On-Campus
* international students: may 12 | UK students: august 29
** for international students I £10,900: UK students
Key Summary
Course overview
There is an urgent need for leaders of AI implementation across the healthcare workforce of the UK and worldwide. The MSc AI Implementation (Healthcare) programme is designed to address this critical need, equipping you with the expertise to advance responsible and impactful AI innovations at scale across healthcare systems globally.
Whether you come from a clinical, technical, or operational background, this programme will prepare you to lead multidisciplinary teams to deliver AI-enabled benefits to patients and services. The modular design of the programme accommodates varied career stages and learning goals through diploma and part-time options.
Course highlights
What makes this course different?
The University of Birmingham proudly offers the UK’s first taught MSc in AI Implementation. The programme draws on experts in engineering, medicine, governance, regulation and policy. Enriched by a cast of guest lecturers, this multidisciplinary faculty delivers the breadth of insights required for safe, effective and equitable innovation with AI health technologies. We're positioned at the intersection of key regional and national networks, including the UK’s Incubator for AI and Digital Healthcare. This enables exposure to professionals leading AI practice and policy, ensuring programme content remains state of the art and highlighting onward career opportunities for graduates.
- Career accelerator: Whether you are a manager, clinician, innovator or recent graduate, this programme develops the capabilities required to lead teams implementing AI in healthcare.
- Future-proof training: Horizon scanning from national leaders of AI regulation, policy and industry within our faculty prepares graduates to deliver state of the art innovation in this dynamic field.
- Practical Capstone Project: The programme culminates in a capstone project focused on real-world AI implementation challenges, enabling graduates to seamlessly transition into roles in a range of healthcare settings.
To help you afford your studies, we’ve put more than £33 million into student support and scholarships. We also offer a range of advice on searching for funding and managing your finances.
Birmingham Masters Scholarships
We want to welcome the brightest talent to our postgraduate community. That’s why our Birmingham Masters Scholarships award £3,000 to more than 300 students each year.
Course Structure
This course will run over 12 months if taken full-time. There's also scope to undertake the programme on a part-time basis.
While the exact content may change, here’s what you can expect to study each semester. This is based on a full time Masters course.
Term One
The first semester covers methods to identify and characterise clinical needs that present impactful opportunities for innovation. Pragmatic insights into the risks and benefits of a contemporary range of AI technologies for different innovation targets are gained, without any need for coding skills. A chosen module will allow an additional complementary focus on global health or the planning, procuring and monitoring of healthcare services.
Term Two
The second semester introduces the regulatory framework for healthcare technologies, focusing on the implications for AI products. The curriculum covers methods for local governance and evaluation within healthcare organisations, along with legal and ethical responsibilities. You'll complete the semester choosing between modules in healthcare leadership and management or sociology and social policy.
Term Three
A practice-oriented capstone project prepares graduates for roles in AI implementation within healthcare. Drawing on insights from the entire programme, participants will create a dissertation centred on an AI healthcare application of their choice, offering the opportunity to apply knowledge and skills to a real-world problem that aligns with their personal interests.
Module information
Two 30-credit modules and two 20-credit modules are compulsory, you'll then choose two further 10-credit modules for the MSc or PGDip award.
The modules listed below are an indication only and may be subject to change. Occasionally, it may be necessary to make changes to modules, for example, to ensure they remain current and relevant.
Modules will be available to view soon.
This programme empowers graduates with the knowledge and skills needed to implement safe, effective and fair AI-enabled healthcare.
Graduates will be well placed to find opportunities within healthcare providers, management and innovation consultancies, MedTech companies, or public policy organisations. The comprehensive competencies developed in this programme will enhance graduates’ existent domain expertise, facilitating a step-change in their career towards leadership roles in responsible AI innovation.
Careers Network
Get ready for tomorrow, with advice, guidance and opportunities at every step of your studies. From developing new skills to preparing for a PhD, our Careers Network can help you gain an advantage in the job market or advance in your field.
Whatever you plan to do after your degree, the Careers Network offers a range of events and support services including networking opportunities, career coaching, one-to-one guidance, careers fairs and links with leading graduate recruiters. We also offer subject-specific careers consultants and a dedicated careers website for international students.
Course delivery
Study with world-class AI academics and practitioners using diverse learning methods to build confidence and prepare for emerging career opportunities across the healthcare sector. The course is delivered through a mix of tutorials, seminars, projects and practical assignments.
- Face-to-face classroom teaching – exploring key concepts and techniques across a diverse range of topics.
- Small group discussions and collaboration – working together with multidisciplinary peers to make sense of new ideas and test and apply them in different case studies.
- Practice-oriented projects – independent completion of tasks demanded by real AI implementation projects with tailored feedback and support from faculty.