Clinical Radiology Artificial Intelligence (AI): Blended learning course

We are thrilled to announce the return of our new blended learning programme combining the experience of virtual peer-to-peer learning in our interactive workshop with online self-paced e-learning material for a seamless and complementary flow of learning. This course is designed for radiologists and allied healthcare professionals. Whether you’re just starting your AI journey or have some level of experience, this course is for anyone looking to gain a global overview of Artificial Intelligence (AI) in radiology and what it means for the speciality. Launching in October, participants will receive access to two interactive e-learning modules. Each module offers 2-3 hours of in-depth learning. Then elevate your knowledge and network with peers at our exclusive workshop on 10 December 2024, led by industry leading AI experts in the field of radiology.

12 CPD points will be awarded on completion of the e-learning modules and attending the online workshop. Please note this event will not be available to view on-demand post event.

Who should attend?

• Clinical radiology consultants
• Clinical radiology trainees
• Allied healthcare professionals

This course is for healthcare clinicians at any level of training or experience who are working in the UK and overseas.

Learning outcomes:

You will be introduced to AI in radiology and healthcare and the fundamental concepts when creating an AI algorithm. By the end of this course, you will:

1. Gain an understanding of the fundamental principles that form the basis of AI
2. Be able to describe various AI techniques and their advantages and disadvantages, as well as justify the use of these methods
3. Acquire basic knowledge of how AI models are created, with a particular focus on data gathering, annotation, and the importance of data management and security
4. Understand the challenges associated with open-source datasets, be aware of AI grand challenges, and understand how bias and unintended outcomes can potentially affect AI models.

View the programme.