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Artificial Intelligence for Medicine & Medical Research (MSc) Full-time

MSc (NFQ Level 9)
Internships Opportunity
Scholarships Available

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

Duration:
1 Year
Attendance:
Full Time
Delivery:
Blended
NFQ Level:
9 (90 credits)
Level:
Graduate Taught
Award:
Master of Science
Next Intake:
September
Country Specific Entry Requirements:
Visit the UCD Global Undergraduate Entry Requirements webpage.
Other School Leaving Requirements:
See www.ucd.ie/admissions
Curricular information is subject to change.

Apply Now

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

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

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
 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
Stage 1 Options - A) Min 0 of:
Students are advised to seek guidance on module selection.
 MEIN40430 Internship research project 20 2 Trimester duration (Spr-Sum)  20
Stage 1 Options - A) Min 0 of:
Students are advised to seek guidance on module selection.
 RDGY41850 Data Challenges in Medical Imaging AI: From Models to Clinical Insights 2 Trimester duration (Spr-Sum)  5
Stage 1 Options - A) Min 0 of:
Students are advised to seek guidance on module selection.
 COMP47460 Machine Learning (Blended Delivery) Autumn  5
Stage 1 Options - A) Min 0 of:
Students are advised to seek guidance on module selection.
 MDCS42240 Medical Research Design, Regulations & Ethics Autumn  5
Stage 1 Options - A) Min 0 of:
Students are advised to seek guidance on module selection.
 MDSA40280 Professional Skills and Career Development Autumn  5
Stage 1 Options - A) Min 0 of:
Students are advised to seek guidance on module selection.
 PHPS41040 Clin Info and Decision Support Autumn  10
Stage 1 Options - A) Min 0 of:
Students are advised to seek guidance on module selection.
 PHPS41150 Introduction to Biostatistics Autumn  5
Stage 1 Options - A) Min 0 of:
Students are advised to seek guidance on module selection.
 STAT30340 Data Programming with R (Blended) Autumn  5
Stage 1 Options - A) Min 0 of:
Students are advised to seek guidance on module selection.
 STAT40730 Data Programming with R (Online) Autumn  5
Stage 1 Options - A) Min 0 of:
Students are advised to seek guidance on module selection.
 STAT40800 Data Prog with Python (online) Autumn  5
Stage 1 Options - A) Min 0 of:
Students are advised to seek guidance on module selection.
 COMP40400 Bioinformatics Spring  5
Stage 1 Options - A) Min 0 of:
Students are advised to seek guidance on module selection.
 COMP41680 Data Science in Python Spring  5
Stage 1 Options - A) Min 0 of:
Students are advised to seek guidance on module selection.
 COMP41840 AI for Health Spring  5
Stage 1 Options - A) Min 0 of:
Students are advised to seek guidance on module selection.
 COMP47590 Advanced Machine Learning Spring  5
Stage 1 Options - A) Min 0 of:
Students are advised to seek guidance on module selection.
 COMP47650 Deep Learning Spring  5
Stage 1 Options - A) Min 0 of:
Students are advised to seek guidance on module selection.
 COMP47970 Information Visualisation (Blended Delivery) Spring  5
Stage 1 Options - A) Min 0 of:
Students are advised to seek guidance on module selection.
 MDSA40310 Entrepreneurship in Prec. Med Spring  5
Stage 1 Options - A) Min 0 of:
Students are advised to seek guidance on module selection.
 PHAR40050 Drug Discovery and Development I Spring  5
Stage 1 Options - A) Min 0 of:
Students are advised to seek guidance on module selection.
 PHPS40880 Intro to Genetic Epidemiology Spring  5
Stage 1 Options - A) Min 0 of:
Students are advised to seek guidance on module selection.
 STAT41010 Stat Network Analysis Spring  5
Stage 1 Options - A) Min 0 of:
Students are advised to seek guidance on module selection.
 PATH40260 AI in Pathology Summer  5

Artificial Intelligence for Medicine & Medical Research (MSc) Full-time (X903) Full Time
EU          fee per year - € 11925
nonEU    fee per year - € 27905

***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.



Please note, though this major is delivered in a blended fashion, teaching is predominantly in person.


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

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

Full-Time
Commencing September 2026
Graduate Taught
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