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MSc Artificial Intelligence for Medicine & Medical Research Part-Time

MSc (NFQ Level 9)

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, pharmaceutical research, and remote sensing and connected health. 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. It combines teaching in data analytics, machine learning/AI, systems biology, precision medicine, health informatics and connected health.

The programme is divided into (i) introductory modules (eight mandatory modules and seven optional modules) leading to a graduate diploma (60 ECTS); and (ii) three advanced modules leading to a Master of Science, Medicine degree (90 ECTS). 

The introductory modules aim to familiarise students with the basic concepts of biology and medicine through examples and the analysis of relevant data sets. The advanced modules will focus on AI applications and include project work.

Modules on offer cover the following major themes of data analysis in biomedicine including:

  • State-of-the-art methods in AI/Machine Learning and their applications to biological and medical data

  • Programming and tools for AI

  • Tools and methods for large scale data analytics

  • Data visualisation

  • Nature and structure of biological and medical data including those produced by omics and imaging methods

  • Design of biological and medical research projects

  • Ethical and privacy issues associated with the use of medical and biological data and analysis results

About This Course

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 GDP

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 Options - A)4 of:
Year 1 CORE Modules
 ANAT40040 Biological Principles and Cellular Organisation Autumn  5
Stage 1 Options - A)4 of:
Year 1 CORE Modules
 PATH40080 High Throughput Technologies Autumn  5
Stage 1 Options - A)4 of:
Year 1 CORE Modules
 PATH40060 Precision Oncology Spring  5
Stage 1 Options - A)4 of:
Year 1 CORE Modules
 MEIN40330 AI for Personalised Medicine Summer  10
Stage 1 Options - B)0 of:
Year 1 Option Modules
 COMP47460 Machine Learning (Blended Delivery) Autumn  5
Stage 1 Options - B)0 of:
Year 1 Option Modules
 MDCS42240 Medical Research Design, Regulations & Ethics Autumn  5
Stage 1 Options - B)0 of:
Year 1 Option Modules
 PHPS41040 Clin Info and Decision Support Autumn  10
Stage 1 Options - B)0 of:
Year 1 Option Modules
 PHPS41150 Introduction to Biostatistics Autumn  5
Stage 1 Options - B)0 of:
Year 1 Option Modules
 STAT30340 Data Programming with R Autumn  5
Stage 1 Options - B)0 of:
Year 1 Option Modules
 STAT40730 Data Programming with R (Online) Autumn  5
Stage 1 Options - B)0 of:
Year 1 Option Modules
 STAT40800 Data Prog with Python (online) Autumn  5
Stage 1 Options - B)0 of:
Year 1 Option Modules
 COMP40400 Bioinformatics Spring  5
Stage 1 Options - B)0 of:
Year 1 Option Modules
 COMP41680 Data Science in Python Spring  5
Stage 1 Options - B)0 of:
Year 1 Option Modules
 COMP47590 Advanced Machine Learning Spring  5
Stage 1 Options - B)0 of:
Year 1 Option Modules
 COMP47650 Deep Learning Spring  5
Stage 1 Options - B)0 of:
Year 1 Option Modules
 COMP47970 Information Visualisation (Blended Delivery) Spring  5
Stage 1 Options - B)0 of:
Year 1 Option Modules
 IS41020 Information Ethics Spring  5
Stage 1 Options - B)0 of:
Year 1 Option Modules
 MDSA40310 Entrepreneurship in Prec. Med Spring  5
Stage 1 Options - B)0 of:
Year 1 Option Modules
 PHAR40050 Drug Discovery and Development I Spring  5
Stage 1 Options - B)0 of:
Year 1 Option Modules
 PHPS40880 Intro to Genetic Epidemiology Spring  5
Stage 1 Options - B)0 of:
Year 1 Option Modules
 STAT41010 Stat Network Analysis Spring  5
Stage 1 Options - B)0 of:
Year 1 Option Modules
 PATH40050 AI & Digital Pathology: Theory & Practice Summer  10
Stage 1 Options - B)0 of:
Year 1 Option Modules
 RDGY41710 AI For Medical Image Analysis Summer  10
Stage 1 Options - D)0 of:
Year 2 Modules
 MEIN40400 Research Internship 2 Trimester duration (Spr-Sum)  10
Stage 1 Options - D)0 of:
Year 2 Modules
 COMP47460 Machine Learning (Blended Delivery) Autumn  5
Stage 1 Options - D)0 of:
Year 2 Modules
 MDCS42240 Medical Research Design, Regulations & Ethics Autumn  5
Stage 1 Options - D)0 of:
Year 2 Modules
 MDSA40280 Professional Skills and Career Development Autumn  5
Stage 1 Options - D)0 of:
Year 2 Modules
 PHPS41040 Clin Info and Decision Support Autumn  10
Stage 1 Options - D)0 of:
Year 2 Modules
 PHPS41150 Introduction to Biostatistics Autumn  5
Stage 1 Options - D)0 of:
Year 2 Modules
 STAT30340 Data Programming with R Autumn  5
Stage 1 Options - D)0 of:
Year 2 Modules
 STAT40730 Data Programming with R (Online) Autumn  5
Stage 1 Options - D)0 of:
Year 2 Modules
 STAT40800 Data Prog with Python (online) Autumn  5
Stage 1 Options - D)0 of:
Year 2 Modules
 COMP40400 Bioinformatics Spring  5
Stage 1 Options - D)0 of:
Year 2 Modules
 COMP41680 Data Science in Python Spring  5
Stage 1 Options - D)0 of:
Year 2 Modules
 COMP47590 Advanced Machine Learning Spring  5
Stage 1 Options - D)0 of:
Year 2 Modules
 COMP47650 Deep Learning Spring  5
Stage 1 Options - D)0 of:
Year 2 Modules
 COMP47970 Information Visualisation (Blended Delivery) Spring  5
Stage 1 Options - D)0 of:
Year 2 Modules
 IS41020 Information Ethics Spring  5
Stage 1 Options - D)0 of:
Year 2 Modules
 MDSA40310 Entrepreneurship in Prec. Med Spring  5
Stage 1 Options - D)0 of:
Year 2 Modules
 PHAR40050 Drug Discovery and Development I Spring  5
Stage 1 Options - D)0 of:
Year 2 Modules
 PHPS40880 Intro to Genetic Epidemiology Spring  5
Stage 1 Options - D)0 of:
Year 2 Modules
 STAT41010 Stat Network Analysis Spring  5
Stage 1 Options - D)0 of:
Year 2 Modules
 PATH40050 AI & Digital Pathology: Theory & Practice Summer  10
Stage 1 Options - D)0 of:
Year 2 Modules
 RDGY41710 AI For Medical Image Analysis Summer  10

MSc Artificial Intelligence for Medicine & Medical Research Part-Time (X984) Part Time
EU          fee per year - € 11500 ti
nonEU    fee per year - € 27520 ti

tiYear Fee is Full Programme Fee charged to First Year 2024/25 Entry students only.
***Fees are subject to change

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.

Part Time option suitable for:

Domestic(EEA) applicants: Yes
International (Non EEA) applicants currently residing outside of the EEA Region. No


This programme is aimed at computer scientists, data scientists, mathematicians and statisticians.  Entry requirements are a bachelor’s degree (minimum2H2), good computing skills, basic programming skills, and a solid foundation in statistics and mathematics.



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.


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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 Part-Time:

ROWCLASS Apply to   Application Type  
showAudience-audienceEU showAudience-audienceInt X984
MSc Artificial Intelligence for Medicine & Medical Research Part-Time
Master of Science		 Part-Time
Commencing 2024/2025 September		 Graduate Taught Apply
* you can change options at the top of the page
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