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, 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.



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.



NFQ Level: 9 (90 credits)

Level: Graduate Taught

Award: Master of Science

Stage 1 Options - A)4 of:

Year 1 CORE Modules

ANAT40040: Biological Principles and Cellular Organisation

CLIP40010: High Throughput Technologies

PATH40060: Precision Oncology

MEIN40330: AI for Personalised Medicine



Stage 1 Options - B) Min 0 of:

Students are advised to seek guidance on module selection.

COMP47460: Machine Learning (Blended Delivery)

MDCS42240: Medical Research Design, Regulations & Ethics

PHPS41040: Clin Info and Decision Support

PHPS41150: Introduction to Biostatistics

STAT30340: Data Programming with R

STAT40730: Data Programming with R (Online)

STAT40800: Data Prog with Python (online)

COMP40400: Bioinformatics

COMP41680: Data Science in Python

COMP47590: Advanced Machine Learning

COMP47650: Deep Learning

COMP47970: Information Visualisation (Blended Delivery)

IS41020: Information Ethics

MDSA40310: Entrepreneurship in Prec. Med

PHAR40050: Drug Discovery and Development I

PHPS40880: Intro to Genetic Epidemiology

STAT41010: Stat Network Analysis

PATH40050: AI & Digital Pathology: Theory & Practice

RDGY41710: AI For Medical Image Analysis



Stage 1 Options - D) Min 0 of:

Students are advised to seek guidance on module selection

MEIN40400: Research Internship

COMP47460: Machine Learning (Blended Delivery)

MDCS42240: Medical Research Design, Regulations & Ethics

MDSA40280: Professional Skills and Career Development

PHPS41040: Clin Info and Decision Support

PHPS41150: Introduction to Biostatistics

STAT30340: Data Programming with R

STAT40730: Data Programming with R (Online)

STAT40800: Data Prog with Python (online)

COMP40400: Bioinformatics

COMP41680: Data Science in Python

COMP47590: Advanced Machine Learning

COMP47650: Deep Learning

COMP47970: Information Visualisation (Blended Delivery)

IS41020: Information Ethics

MDSA40310: Entrepreneurship in Prec. Med

PHAR40050: Drug Discovery and Development I

PHPS40880: Intro to Genetic Epidemiology

STAT41010: Stat Network Analysis

PATH40050: AI & Digital Pathology: Theory & Practice

RDGY41710: AI For Medical Image Analysis

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 (https://tinyurl.com/2ae2ffax) for further information. Any exceptions are also listed on this webpage.

2 Years, Part-Time:

Delivery: Blended

X984: MSc Artificial Intelligence for Medicine & Medical Research

Part-Time: Master of Science



Part-Time: Commencing September 2025

Graduate Taught