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MSc Artificial Intelligence for Medicine & Medical Research Full-time

MSc (NFQ Level 9)
Internships Available

This course is available through the following application route(s)

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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 (transcriptomics, genomics, proteomics) and imaging methods.
  • Understand the design of biological and medical research projects.
  • Demonstrate a knowledge and understanding of the ethical and privacy issues associated with the use of medical and biological data.
  • 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.

About This Course

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.

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.



 


Below is a list of all modules offered for this degree in the current academic year. Click on the module to discover what you will learn in the module, how you will learn and assessment feedback profile amongst other information.

Incoming Stage 1 undergraduates can usually select an Elective in the Spring Trimester. Most continuing undergraduate students can select up to two Elective modules (10 Credits) per stage. There is also the possibility to take up to 10 extra Elective credits.

Module Type Module   Trimester Credits
Stage 1 Core Modules
 ANAT40040 Biological Principles and Cellular Organisation Autumn  5
Stage 1 Core Modules
 CLIP40010 High Throughput Technologies Autumn  5
Stage 1 Core Modules
 PATH40060 Precision Oncology Spring  5
Stage 1 Core Modules
 MEIN40330 AI for Personalised Medicine Summer  10
Stage 1 Core Modules
 PATH40050 AI & Digital Pathology: Theory & Practice Summer  10
Stage 1 Core Modules
 RDGY41710 AI For Medical Image Analysis Summer  10
Stage 1 Options - A) Min 0 of:
Option module
 MEIN40400 Research Internship 2 Trimester duration (Spr-Sum)  10
Stage 1 Options - A) Min 0 of:
Option module
 COMP47460 Machine Learning (Blended Delivery) Autumn  5
Stage 1 Options - A) Min 0 of:
Option module
 MDCS42240 Medical Research Design, Regulations & Ethics Autumn  5
Stage 1 Options - A) Min 0 of:
Option module
 MDSA40280 Professional Skills and Career Development Autumn  5
Stage 1 Options - A) Min 0 of:
Option module
 PHPS41040 Clin Info and Decision Support Autumn  10
Stage 1 Options - A) Min 0 of:
Option module
 PHPS41150 Introduction to Biostatistics Autumn  5
Stage 1 Options - A) Min 0 of:
Option module
 STAT30340 Data Programming with R Autumn  5
Stage 1 Options - A) Min 0 of:
Option module
 STAT40730 Data Programming with R (Online) Autumn  5
Stage 1 Options - A) Min 0 of:
Option module
 STAT40800 Data Prog with Python (online) Autumn  5
Stage 1 Options - A) Min 0 of:
Option module
 COMP40400 Bioinformatics Spring  5
Stage 1 Options - A) Min 0 of:
Option module
 COMP41680 Data Science in Python Spring  5
Stage 1 Options - A) Min 0 of:
Option module
 COMP47590 Advanced Machine Learning Spring  5
Stage 1 Options - A) Min 0 of:
Option module
 COMP47650 Deep Learning Spring  5
Stage 1 Options - A) Min 0 of:
Option module
 COMP47970 Information Visualisation (Blended Delivery) Spring  5
Stage 1 Options - A) Min 0 of:
Option module
 IS41020 Information Ethics Spring  5
Stage 1 Options - A) Min 0 of:
Option module
 MDSA40310 Entrepreneurship in Prec. Med Spring  5
Stage 1 Options - A) Min 0 of:
Option module
 PHAR40050 Drug Discovery and Development I Spring  5
Stage 1 Options - A) Min 0 of:
Option module
 PHPS40880 Intro to Genetic Epidemiology Spring  5
Stage 1 Options - A) Min 0 of:
Option module
 STAT41010 Stat Network Analysis Spring  5

MSc Artificial Intelligence for Medicine & Medical Research Full-time (X903) Full Time
EU          fee per year - € 11690
nonEU    fee per year - € 27520

***Fees are subject to change

This course is eligible for UCD Global Excellence Scholarships. For more information on these scholarships and application details visit this link.

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. Any exceptions are also listed on this webpage.

Full Time option suitable for:

Domestic(EEA) applicants: Yes
International (Non EU) applicants: Yes


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.


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General application route(s) for Irish/UK/EU applicants* for International (non-EU) applicants* to MSc Artificial Intelligence for Medicine & Medical Research Full-time:

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

Full-Time
Commencing September 2025
Graduate Taught
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