Presencial en Reino Unido
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MSc: 2250 hours / 180 CAT (90 ECTS)
PGDip: 1500 hours / 120 CAT (60 ECTS)
June 2026
This MSc / PGDip Artificial Intelligence for Healthcare will provide a broad introduction to AI’s enormous potential and will prepare you to lead multidisciplinary teams that develop Artificial Intelligence projects in healthcare organisations and related industries.
With this online MSc / PGDip you will gain a broad overview of the technical, regulatory, economic and ethical aspects needed to develop AI projects in the healthcare sector. You will learn basic concepts of programming and data processing, as well as the Artificial Intelligence models that can be applied to diagnosing and monitoring different pathologies. By the end of the course, you will have an understanding of the tools needed to implement AI projects and methodologies across the healthcare sector.
What clinical laboratory equipment will you use?
You will only need a computer for this course. During your training ,you will also work with Python, one of the main programming languages.
The route to your future
Why enrol in our Postgraduate Certificate in Artificial Intelligence for Healthcare? Because in addition to having prestigious professors and a curriculum aimed at preventing student drop-out, we guide our students towards achieving their professional goals.
Learn more about the employability plan you'll benefit from the moment you sign up.
Characteristics of the MSc Medical Laboratory Science
Audio visual study material
- You will have access to many hours of audio visual materials, which are essential teaching materials. Thus, you can study wherever and whenever you want.
Practical activities
- Approximately twice a week you will carry out practical activities that will be reviewed and evaluated by your specialized teachers.
Complementary material
- Class summaries, articles to stay up-to-date... You will have everything you need to keep on learning.
Master class
- You will learn from well-known experts in the medical sector thanks to our master classes, which you can watch as many times as you want.
MSc / PGDip final project
- At the end of the course you will conduct a research project on a topic of your interest. One of our teachers will supervise your project.
You have arrived at your destination! You now have your own CEMP qualification, University of Chichester university accreditation*, and EQAC accreditation*. It's time for new challenges... and new adventures!
This MSc programme opens doors to diverse careers in healthcare and technology. Graduates can excel in healthcare data analysis, medical imaging, clinical decision support, and more. Prepare for exciting AI-driven healthcare careers!
From the first to the last stage of your training, we will always be by your side to help you make the most of every step.
Enrolment and preparation
- As soon as you enrol, you will have access to the virtual platform. We will also start your employability plan. Let is get going!
Videos and online classes
- Videos, PDF summaries and live classes from your teachers.
Exercises and questionnaires
- You will regularly test your knowledge to move steadily towards your goals.
MSc final project
- You will carry out a bibliographic research project on a topic of your interest.
Optional internships
- Let is get to work! From 60 to 300 hours of optional internships available in a wide range of companies.
As your journey progresses, you will discover different modules which will help you, step by step, to reach your final goal.
Module 1. The Background
1. The fourth industrial revolution
2. A brief history of the interaction between medicine and artificial intelligence
3. What can we use AI algorithms for in the clinical setting?
4. Learning systems: A mapping of the AI environment
5. Health data: sources and characteristics
6. Data Protection: GDPR
7. Research and clinical trials
8. Ethical implications
9. 5Ps Medicine
10. Value-based decision
11. European/national/regional/regional strategy
12. Expected impact of AI in the coming years
13. Resource management success cases
14. Health care success cases
Exam
Module 2. The technique
1. Rule-based expert systems. The predecessors of AI
2. Machine Learning: regression, classification and clustering models
3. Neural networks and deep learning
4. The learning paradigm. Feature selection and model optimisation
5. What is Python? Introduction. Python and data science. Installation and working environment
6. Getting started in Python (Theory) Data types, variables, operators, loops and other structures
7. Getting started in Python.(Practical) Data types, variables, operators, loops and other structures
8. Object orientation: classes and instances, attributes and methods. Working with libraries
9. Fundamental Python libraries for working with data: Numpy and Pandas
10. Introduction to AI in Python. Libraries and levels of abstraction
11. Data analysis in Python: Spicy, Matplotlib, Seaborn, statsmodels
12. Data structuring: data sets for training, validation and testing. Data augmentation
13. Machine Learning in Python: Scikit-learn and practical examples
14. Neural Networks in Pyhton: Pytorch, Tensorflow and Keras
Exam
Module 3. Applications of AI in healthcare
1. Types of data in health
2. Hospital Information Systems (HIS) and Electronic Health Records (EHR)
3. Image Management Systems (PACS and DICOM)
4. Data interoperability in healthcare. The FHIR standard
5. Text mining and Natural Language Processing (NLP)
6. Medical image analysis. U-Nets and GANs
7. Robotic Process Automation
8. Artificial Intelligence and Cloud Computing
9. Decision Support Systems: Diagnosis and Treatment
10. AI in Drug Discovery and personalised treatments
11. Management improvements
12. Patient interaction and telemedicine
Exam
Module 4. Implementation of AI projects in Healthcare
1. Framework evaluation Outcome-Action-Pair (OAP)
2. Life cycle of an IA project
3. Design and development
4. Validation
5. Monitoring and maintenance
6. Relevant actors IA Health
7. Bias, interpretability and fairness
8. Privacy and security
9. Regulatory environment
10. Implementing an AI strategy
11. Corporate intrapreneurship and cultural change
12. Project management
13. Public and private financing tools for innovative projects
Exam
Major Project
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