Bournemouth University

What you'll study

This programme integrates core topics from artificial intelligence, data analytics and digital health practice to prepare you to develop and evaluate AI-enabled healthcare technologies. Teaching mixes lectures, hands-on practical labs, group design projects and a substantial independent dissertation or industry project.

• Core machine learning and AI methods – supervised and unsupervised learning, neural networks and deep learning, model evaluation, explainability and uncertainty estimation.
• Health data and informatics – electronic health records, health data standards and interoperability, clinical coding, pre-processing of medical datasets and data governance.
• Medical imaging and signal processing – fundamentals of imaging modalities, image analysis with AI, time-series and biosignal processing (where relevant to student pathway).
• Clinical decision support and evaluation – design of decision support systems, clinical trials and evaluation methods, usability and human–computer interaction in clinical contexts.
• Ethics, law and regulation in digital health – patient privacy, data protection, medical device regulation and the societal impacts of deploying AI in healthcare.
• Software engineering for healthcare systems – secure software development, deployment, cloud services, APIs and integration with clinical workflows.
• Research methods and statistics – study design, biostatistics, reproducible research practices and critical appraisal of health technology evidence.
• Dissertation or industry project – a major, supervised project that applies AI techniques to a real digital health problem, often undertaken with external partners or within BU research groups.

Optional modules and project topics reflect staff expertise and industry demand; typical projects include predictive models for patient outcomes, AI-based triage tools, automated image analysis pipelines and evaluations of telehealth services.

Entry requirements

Applicants are normally expected to hold a UK 2:2 honours degree, or equivalent, in computing, computer science, data science, engineering, health-related disciplines or a quantitative subject. Applicants without a directly relevant degree but with substantial professional experience in healthcare, IT or data analysis may be considered on a case-by-case basis.

Applicants should have demonstrable programming ability (for example in Python or R) and an understanding of basic statistics. Where necessary, the programme may recommend or require preparatory study to address gaps in programming or mathematics prior to or during the early part of the course.

International applicants whose first language is not English will be required to meet Bournemouth University’s English language requirements; usual evidence includes an approved English language test or equivalent qualification.

Career prospects

Graduates are prepared for roles at the intersection of health and data science. Typical job titles include:

• Clinical data scientist or healthcare data analyst
• Machine learning engineer specialising in health applications
• Health informatics specialist or digital health implementation lead
• Medical imaging analyst or biomedical AI researcher
• Product manager or consultant for digital health technology vendors

Alumni work in NHS trusts, health-tech startups, large technology companies with healthcare divisions, research institutes and consultancy firms. The programme’s emphasis on applied projects and industry engagement helps graduates move into roles that require both technical AI skills and an understanding of clinical workflows, regulation and patient safety.

Why study at Bournemouth University

Bournemouth University offers an applied, multidisciplinary approach combining computing and health expertise. The campus supports practical learning with computing labs, access to high-performance computing resources and clinical simulation facilities within the Faculty of Health & Social Sciences, helping students bridge theory and practice.

Students benefit from staff active in digital health research and established links with local NHS partners, industry and the university’s research centres. The programme emphasises employability through project work with external organisations, guest lectures from practitioners, and training in professional skills such as project management and regulatory awareness.

Small-group teaching, practical lab classes and supervisory support for the dissertation/industry project enable students to develop a portfolio of work and practical experience that is attractive to employers in the rapidly evolving digital health sector.