Artificial Intelligence for Medicine & Medical Research

This Masters programme consolidates core disciplines to address a rapidly increasing skill gap in the healthcare and biomedical research sector. AI is already revolutionising medical imaging, digital pathology, drug development and pharmaceutical research. In the era of genomic medicine, AI will transform the way we diagnose and treat diseases, reducing the impact of the healthcare crisis in industrialised countries caused by cancer, obesity and diabetes.



This course combines teaching in AI and machine learning (ML), precision medicine, systems biology, bioinformatics and computational biology. The programme consists of (i) introductory modules that aim to familiarise students with the basic concepts of biology and medicine through examples and the analysis of relevant data sets and (ii) advanced modules focusing on AI/ML applications in omics/image analysis and include project work.



What Will I Learn?

On successful completion of the programme students will be able to:

• Demonstrate a comprehensive knowledge and understanding of the current state-of-the-art methods in AI/ML and their possible applications to biological and medical data.

• Understand the research questions and possible applications in these fields that can be solved using AI/ML.

• Understand the nature and structure of biological and medical data including those produced by omics and imaging methods.

• Understand the design of biological and medical research projects.

• Understand how to use medical and health information systems.

• Demonstrate a knowledge and understanding of the ethical and privacy issues associated with the use of medical and biological data and analysis results.

• Apply AI/ML applications that can drive the discovery and development of new and highly innovative biomedical and biotech methods and products.

• Demonstrate skills in problem-solving and incorporating critical thinking and decision-making into a variety of clinical, biopharmaceutical, and biological research applications and environments.

• Demonstrate the analytical and technical skills required for the analysis and interpretation of different data types in the exploitation of scientific discovery and development in industrial, academic and clinical settings.

• Work with data from biological and biomedical databases and e-health information systems.

• Incorporate ethical and data governance considerations into the analysis of patient and research data that satisfy concurrent data protection frameworks in the era of GDPR.



Student Internships

Students are offered the opportunity to undertake an internship module* as part of the programme. The aim of this module is to provide hands-on experience in data analysis using real-life problem-solving projects in collaboration with our academic, clinical and industry partners.



* The placement has limited capacity and entry is competitive.



The programme is aimed at computer scientists, data scientists, mathematicians, and statisticians. We also offer the course for biologists with good programming skills (R or Python). Entry requirements are a Bachelor’s degree (minimum 2H1), good programming skills and a solid foundation in statistics/mathematics or biology.

Module/Trimester/Credits



Stage 1 Core Modules

ANAT40040 Biological Principles and Cellular Organisation Autumn 5

CLIP40010 High Throughput Technologies Autumn 5

PATH40060 Precision Oncology Spring 5

MEIN40330 AI for Personalised Medicine Summer 10

RDGY41710 AI For Medical Image Analysis Summer 10



Stage 1 Options - A) Min 0 of:

Students are advised to seek guidance on module selection.

MEIN40420 Internship Research Project 10 2 Trimester duration (Spr-Sum) 10

MEIN40430 Internship research project 20 2 Trimester duration (Spr-Sum) 20

RDGY41850 Data Challenges in Medical Imaging AI: From Models to Clinical Insights 2 Trimester duration (Spr-Sum) 5

COMP47460 Machine Learning (Blended Delivery) Autumn 5

MDCS42240 Medical Research Design, Regulations & Ethics Autumn 5

MDSA40280 Professional Skills and Career Development Autumn 5

PHPS41040 Clin Info and Decision Support Autumn 10

PHPS41150 Introduction to Biostatistics Autumn 5

STAT30340 Data Programming with R (Blended) Autumn 5

STAT40730 Data Programming with R (Online) Autumn 5

STAT40800 Data Prog with Python (online) Autumn 5

COMP40400 Bioinformatics Spring 5

COMP41680 Data Science in Python Spring 5

COMP41840 AI for Health Spring 5

COMP47590 Advanced Machine Learning Spring 5

COMP47650 Deep Learning Spring 5

COMP47970 Information Visualisation (Blended Delivery) Spring 5

MDSA40310 Entrepreneurship in Prec. Med Spring 5

PHAR40050 Drug Discovery and Development I Spring 5

PHPS40880 Intro to Genetic Epidemiology Spring 5

STAT41010 Stat Network Analysis Spring 5

PATH40260 AI in Pathology Summer 5

The programme is aimed at computer scientists, data scientists, mathematicians and statisticians.



Entry requirements are a Bachelor’s degree (min 2H2), good computing skills, basic programming skills, and a solid foundation in statistics and mathematics.



If English is not the applicant’s native language, unless the primary degree was read through English medium in an English-speaking country, an English language qualification is required. English language qualifications include a minimum score of 6.5, overall, in the International English Language Testing System (IELTS). Other evidence of proficiency in English may be accepted such as the Cambridge Certificate, TOEFL or Pearson’s Test of English, as per the standard UCD requirements.



You may be eligible for Recognition of Prior Learning (RPL), as UCD recognises formal, informal, and/or experiential learning. RPL may be awarded to gain Admission and/or credit exemptions on a programme. Please visit the UCD Registry RPL web page for further information at: https://www.ucd.ie/registry/prospectivestudents/admissions/rpl/ Any exceptions are also listed on this webpage.

Online Application.



Who Should Apply?

Full Time option suitable for:

Domestic(EEA) applicants: Yes

International (Non EU) applicants: Yes

1 year full-time. Delivery: Blended.

X903: Master of Science in Artificial Intelligence for Medicine & Medical Research Full-time

Commencing September 2026

Graduate Taught

Career & Graduate Study Opportunities

This programme equips students with the fundamental skills to work in a variety of roles in the biopharmaceutical industry, healthcare sector or biomedical research including AI/ML engineer, data scientist, bioinformatics specialist. In addition, this course will enhance the academic profile and practical skills of students, making them highly competitive for PhD programmes in universities and research departments in biopharmaceutical companies. Recent career destinations of our graduates include Novartis, Optum and other employers. Multiple graduates continued their education in PhD programmes and are now part of UCD, other Irish or international universities, including the Icahn School of Medicine at Mount Sinai and Harvard Medical School in the USA.