View Specification DocumentArtificial Intelligence (AI) in Healthcare: principles and practical application MED1022E
- Academic Session: 2026-27
- School: School of Health and Wellbeing
- Credits: 10
- Level: Level 1 (SCQF level 7)
- Typically Offered: Summer
- Available to Visiting Students: Yes
- Collaborative Online International Learning: No
- Curriculum For Life: Yes
Short Description
This short course will provide you with an introduction to the principles and practical application of artificial intelligence (AI) in healthcare and biomedicine, covering the key concepts involved in designing and evaluating approaches to artificial intelligence methods. The course will focus on applied AI methods for problems in prevention, diagnosis, therapy, aetiology, and prognosis related areas of healthcare. You will be given a practical introduction to common AI approaches, offering you experience in using different AI and machine learning algorithms and concepts (including decision trees, logistic regression, support vector machines, artificial neural nets, ensembles and deep learning) in the context of healthcare.
Timetable
Two weeks with 4 hours per day.
Requirements of Entry
No advanced math or coding is required, but students should be comfortable interpreting results, graphs, and simple equations. Students don't need to calculate formulas by hand or program in R/Python. Instead, they should understand what these concepts mean, why they matter, and how to interpret outputs or graphs generated by AI tools.
Excluded Courses
None
Co-requisites
None
Assessment
The summative assessment will be in form of an individual presentation related to practical work, based on the training and skills they have learned throughout the course.
Course Aims
Provide a learning opportunity for learners without computing science background to obtain a principle understanding of AI technologies as well as practical experiences on how they can be applied to healthcare, and equally understand limitations and caveats of AI related applications.
Intended Learning Outcomes of Course
Learners of this short course are expected to obtain the following knowledge and practical experience.
1. The conceptual understanding of AI and its essential terminology and the potential areas of its application in healthcare.
2. The key components of adapting and tuning AI models and how to properly evaluate them in solving clinical problems.
3. The foundational concepts of how machine learns through 'traditional' machine learning algorithms including probabilistic learning as well as tree-based methods using healthcare use cases.
4. The principle understanding of neural networks and deep learning and how they can be used to a multidimensional feature space of health data.
5. The latest developments AI in medicine (e.g., clinical natural language processing, generative AI for biomedicine and deep learning for medical imaging).
6. 6) Understand the caveats of applying machine learning in health including bias and inequalities embedded in the data and/or induced by AI models