Artificial Intelligence in Healthcare: Principles and Application

About

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 AI 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

Target group

Primarily for medical students but students from any healthcare background are welcome to apply. Students can be at undergraduate or postgraduate level.

- GPA of 3.0 (or equivalent)
- You should be currently enrolled at an international higher education institution.

If your first language is not English, you must meet our minimum proficiency level:

International English Language Testing System (IELTS) Academic module (not General Training) overall score of 6.0, with no sub test less than 5.5
we also accept equivalent scores in other recognised qualifications such as ibTOEFL, CAE, CPE and more

Course aim

This course will 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.

By the end of this course, you will be able to:

- Describe the core concepts of AI and its essential terminology and the potential areas of its application in healthcare.
- Demonstrate the ability in understanding of foundational concepts of how machine learns through 'traditional' machine learning algorithms including probabilistic learning as well as tree-based methods using healthcare use cases.
- Analyse the latest methodological developments in the field of AI and it’s potential in medicine (e.g., clinical natural language processing, generative AI for biomedicine and deep learning for medical imaging).
- Outline the caveats of applying machine learning in health including bias and inequalities embedded in the data and/or induced by AI models

Fee info