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Artificial Intelligence for Medicine & Medical Research

GradDip (NFQ Level 9)

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

Duration:
9 Months
Attendance:
Full Time
Delivery:
Blended
NFQ Level:
9 (60 credits)
Level:
Graduate Taught
Award:
Graduate Diploma
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 Graduate Diploma 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 covers introductory modules (eight mandatory modules and seven optional modules) leading to a graduate diploma (60 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 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 Options - A)45CR:
Students are advised to seek guidance on module selection.
 MEIN40420 Internship Research Project 10 2 Trimester duration (Spr-Sum)  10
Stage 1 Options - A)45CR:
Students are advised to seek guidance on module selection.
 MEIN40430 Internship research project 20 2 Trimester duration (Spr-Sum)  20
Stage 1 Options - A)45CR:
Students are advised to seek guidance on module selection.
 COMP47460 Machine Learning (Blended Delivery) Autumn  5
Stage 1 Options - A)45CR:
Students are advised to seek guidance on module selection.
 MDCS42240 Medical Research Design, Regulations & Ethics Autumn  5
Stage 1 Options - A)45CR:
Students are advised to seek guidance on module selection.
 MDSA40280 Professional Skills and Career Development Autumn  5
Stage 1 Options - A)45CR:
Students are advised to seek guidance on module selection.
 PHPS41040 Clin Info and Decision Support Autumn  10
Stage 1 Options - A)45CR:
Students are advised to seek guidance on module selection.
 PHPS41150 Introduction to Biostatistics Autumn  5
Stage 1 Options - A)45CR:
Students are advised to seek guidance on module selection.
 STAT30340 Data Programming with R (Blended) Autumn  5
Stage 1 Options - A)45CR:
Students are advised to seek guidance on module selection.
 STAT40730 Data Programming with R (Online) Autumn  5
Stage 1 Options - A)45CR:
Students are advised to seek guidance on module selection.
 STAT40800 Data Prog with Python (online) Autumn  5
Stage 1 Options - A)45CR:
Students are advised to seek guidance on module selection.
 COMP40400 Bioinformatics Spring  5
Stage 1 Options - A)45CR:
Students are advised to seek guidance on module selection.
 COMP41680 Data Science in Python Spring  5
Stage 1 Options - A)45CR:
Students are advised to seek guidance on module selection.
 COMP41840 AI for Health Spring  5
Stage 1 Options - A)45CR:
Students are advised to seek guidance on module selection.
 COMP47590 Advanced Machine Learning Spring  5
Stage 1 Options - A)45CR:
Students are advised to seek guidance on module selection.
 COMP47650 Deep Learning Spring  5
Stage 1 Options - A)45CR:
Students are advised to seek guidance on module selection.
 COMP47970 Information Visualisation (Blended Delivery) Spring  5
Stage 1 Options - A)45CR:
Students are advised to seek guidance on module selection.
 IS30370 Digital Media Ethics (formerly Information Ethics) Spring  5
Stage 1 Options - A)45CR:
Students are advised to seek guidance on module selection.
 MDSA40310 Entrepreneurship in Prec. Med Spring  5
Stage 1 Options - A)45CR:
Students are advised to seek guidance on module selection.
 PHPS40880 Intro to Genetic Epidemiology Spring  5
Stage 1 Options - A)45CR:
Students are advised to seek guidance on module selection.
 STAT41010 Stat Network Analysis Spring  5
Stage 1 Options - A)45CR:
Students are advised to seek guidance on module selection.
 MEIN40390 Research-based internship Spring and Summer (separate)  5
Stage 1 Options - A)45CR:
Students are advised to seek guidance on module selection.
 PATH40260 AI in Pathology Summer  5

Artificial Intelligence for Medicine & Medical Research (X904) Full Time
EU          fee per year - € 8630
nonEU    fee per year - € 19135

***Fees are subject to change

Entry requirements are a Bachelor’s degree (min 2H2), good computing skills, basic programming skills, and a
sound 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: No


The programme is aimed at computer scientists, data scientists, mathematicians, and statisticians. We also offer the course for biologists who have good computer skills. 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 Artificial Intelligence for Medicine & Medical Research:

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X904
Artificial Intelligence for Medicine & Medical Research
Graduate Diploma

Full-Time
Commencing September 2026
Graduate Taught
Not available to International applicantsApply
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