Clinical Radiology AI: Understanding Data Science and Bias 2026

Clinical Radiology AI: Understanding Data Science and Bias 2026 is organized by The Royal College of Radiologists (RCR) and will be held on Jan 27, 2026.

Description:
Their AI blended learning programme, understanding data science and bias, is a unique programme designed to equip clinical radiologists and healthcare professionals with the essential knowledge to navigate the evolving world of AI in radiology.

Developed by RCR Learning in collaboration with the Clinical Radiology AI Faculty, it blends flexible, self-paced e-learning with a dynamic expert-led online workshop. Whether you're new to AI or looking to deepen your understanding, you'll gain a solid foundation into data science and bias in this rapidly evolving field. 

Before the workshop, you’ll build essential foundational knowledge through two self-paced interactive online modules, each offering 2-3 hours of learning, covering an introduction to AI in radiology and healthcare, and the core principles of building AI concepts. On 27 January 2026, you'll elevate your knowledge and understanding and connect with peers at their exclusive live online workshop, led by leading AI experts in the field of radiology.

12 CPD points will be awarded on completion of the full course. You will receive an automated certificate for 6 CPD credits once you have completed the e-learning, and a separate certificate for the remaining 6 CPD credits for attending the online workshop. Please note this event will not be available to view on-demand post event. 

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 be able to: 

1. Describe the fundamental principles of AI and explain how these technologies are applied in radiology and healthcare settings.
2. Identify and compare various AI techniques, outlining their advantages and disadvantages, and justify the use of these methods for specific applications.
3. Describe the core steps involved in creating AI models, with particular emphasis on data gathering, annotation, and the significance of data management and security.
4. Recognise the challenges associated with open-source datasets and evaluate how bias and unintended outcomes can potentially influence AI models.

Past delegates noted the course would impact their ways of working, and over 80% rated the course as excellent or very good. What our previous delegates have to say:

"Excellent course and faculty."

“Lectures were excellent with very useful examples to illustrate challenging concepts.”