Ground Floor, Churchill Auditorium

Join us as we officially open day one of our 2nd Annual Global AI Conference 2026. The day begins with a warm welcome and inspiring keynote to set the tone for two-days of learning, exploration and collaboration.  

09:00 - 09:05 Opening remarks - Dr Stephen Harden, President of the RCR

09:05 Keynote address: To be announced

AI is here to stay in our clinical and research practice, but as we move to increasingly complex machine learning models, how do we keep abreast of the raw computational cost of AI, and the huge energy requirements? In this session, we will examine this question from two angles. The first is to look forward at the hope of quantum computing, which offers unique potential in image acquisition and numerical optimisation that underpins our workflow. The second is to look at initiatives that optimise our use of high performance computing through better choice of task appropriate algorithms.

Join our multi-industry session to uncover what is really going on behind the scenes we need to be aware of. We are increasingly introducing AI in our NHS hospitals without necessarily knowing we are procuring them as they come part of a package. Are we aware of this, how is it happening, what do we need to know?

In addition, industry partners who provide our technologies are using AI to enhance clinical workflows, both in software and imaging hardware. The session offers a unique opportunity to learn about best practice in AI development across sectors, and to understand how these innovations are shaping the future of care delivery from key industry partners.

Discover how artificial intelligence is transforming oncology by driving innovation across drug development, delivery, and imaging. This session explores the potential of AI to inform the design of novel drug radiation conjugates and optimise their delivery and response monitoring.

You'll learn how AI tools are being applied to target DNA damage repair mechanisms in cancer, offering new avenues for precision treatment. Gain insights into the clinical impact of these technologies and how they are reshaping therapeutic strategies and patient outcomes.

Artificial intelligence (AI) is an incredibly powerful tool for building systems that support the work of radiologists. From its early roots in digital image analysis, AI has evolved into a key driver of innovation, developing machine learning methods to support healthcare decision making. This sparked high interest and explosive growth in the use of AI and machine learning methods to analyse medical imaging data.

These promising techniques create systems that perform some diagnostic tasks at the level of expert radiologists. The systems have the potential to provide real-time assistance to radiologists, thereby reducing diagnostic errors, detecting disease early, improving patient outcomes, and reducing costs.

This session will explore the origins of AI and its applications to medical imaging, define key terminology, and showcase examples of real-world applications that suggest how AI and large language models may change the practice of medicine.

We'll also address the key limitations and challenges of AI that may limit the application of these new methods. Finally, we will present a forward-looking model predicting how AI will affect the radiology workforce in the next five years.

Ground Floor, Churchill Auditorium

Join us as we officially open day one of our 2nd Annual Global AI Conference 2026. The day begins with a warm welcome and inspiring keynote to set the tone for two-days of learning, exploration and collaboration.  

09:00 - 09:05 Opening remarks - Dr Stephen Harden, President of the RCR

09:05 Keynote address: To be announced

Navigating AI regulation shouldn't require a law degree. This session cuts through the complexity to deliver what procurement professionals and decision-makers actually need to know. We'll map the current regulatory landscape, from the EU AI Act to UK MDR updates, and translate dense policy into practical guidance.

Learn which regulations apply to your AI purchases, what compliance really looks like in practice, and how to ask the right questions of vendors. Whether you're procuring diagnostic algorithms or administrative tools, leave with a clear framework for making informed, compliant decisions without getting lost in regulatory weeds.

This session bridges the gap between cutting-edge research and practical governance standards for AI safety. You'll discover how to manage risk at every stage of clinical trials, from protocol design through post-market surveillance. You'll gain actionable strategies for monitoring algorithm drift and safeguarding data integrity. Learn how to maintain safety as AI systems evolve in real-world settings, and walk away with practical tips for conducting robust AI trials that satisfy good clinical practice and regulators, while advancing responsible innovation.

Healthcare leaders face mounting pressure to invest in AI, but when does it actually pay off? Drawing on a systematic review of health economic outcomes research in radiology AI, this session examines the evidence behind the promises.

We'll explore NICE's Early Value Assessments and rules-based frameworks that help decision-makers separate hype from genuine value. This session will highlight which AI applications demonstrate measurable ROI, what timelines are realistic for break-even, and how to evaluate economic claims critically. You'll discover practical tools for assessing whether AI investments will deliver both financial and clinical returns within your system and organisation.

Artificial intelligence (AI) is an incredibly powerful tool for building systems that support the work of radiologists. From its early roots in digital image analysis, AI has evolved into a key driver of innovation, developing machine learning methods to support healthcare decision making. This sparked high interest and explosive growth in the use of AI and machine learning methods to analyse medical imaging data.

These promising techniques create systems that perform some diagnostic tasks at the level of expert radiologists. The systems have the potential to provide real-time assistance to radiologists, thereby reducing diagnostic errors, detecting disease early, improving patient outcomes, and reducing costs.

This session will explore the origins of AI and its applications to medical imaging, define key terminology, and showcase examples of real-world applications that suggest how AI and large language models may change the practice of medicine.

We'll also address the key limitations and challenges of AI that may limit the application of these new methods. Finally, we will present a forward-looking model predicting how AI will affect the radiology workforce in the next five years.

Ground Floor, Churchill Auditorium

Join us as we officially open day one of our 2nd Annual Global AI Conference 2026. The day begins with a warm welcome and inspiring keynote to set the tone for two-days of learning, exploration and collaboration.  

09:00 - 09:05 Opening remarks - Dr Stephen Harden, President of the RCR

09:05 Keynote address: To be announced

A showcase of successful NHS and industry AI deployments that have moved from concept to clinic, exploring real-world impact, integration challenges, and lessons from those leading AI-enabled service transformation.

From imaging to histopathology and treatment planning, this session explores how AI is reshaping multidisciplinary cancer care, providing decision support, prognostic insight, and workflow efficiency across the patient pathway.

AI innovation can only move as fast as the infrastructure beneath it. In this session, leading experts share how scalable, secure and interoperable foundations, from federated datasets and clinical data platforms, enables safe, effective and equitable AI deployment across health systems. 

Artificial intelligence (AI) is an incredibly powerful tool for building systems that support the work of radiologists. From its early roots in digital image analysis, AI has evolved into a key driver of innovation, developing machine learning methods to support healthcare decision making. This sparked high interest and explosive growth in the use of AI and machine learning methods to analyse medical imaging data.

These promising techniques create systems that perform some diagnostic tasks at the level of expert radiologists. The systems have the potential to provide real-time assistance to radiologists, thereby reducing diagnostic errors, detecting disease early, improving patient outcomes, and reducing costs.

This session will explore the origins of AI and its applications to medical imaging, define key terminology, and showcase examples of real-world applications that suggest how AI and large language models may change the practice of medicine.

We'll also address the key limitations and challenges of AI that may limit the application of these new methods. Finally, we will present a forward-looking model predicting how AI will affect the radiology workforce in the next five years.

Ground Floor, Churchill Auditorium

Join us as we officially open day one of our 2nd Annual Global AI Conference 2026. The day begins with a warm welcome and inspiring keynote to set the tone for two-days of learning, exploration and collaboration.  

09:00 - 09:05 Opening remarks - Dr Stephen Harden, President of the RCR

09:05 Keynote address: To be announced

Join leading voices in medical education as they explore the future of training and AI.
This session brings together speakers with deep experience across medical education and clinical practice to discuss how AI is reshaping learning, widening participation, and supporting the next generation of clinicians. We’ll examine practical approaches to teaching and learning with AI, how residents and trainees can get involved, and the day‑to‑day implications for clinical practice and professional development.

Artificial intelligence (AI) is an incredibly powerful tool for building systems that support the work of radiologists. From its early roots in digital image analysis, AI has evolved into a key driver of innovation, developing machine learning methods to support healthcare decision making. This sparked high interest and explosive growth in the use of AI and machine learning methods to analyse medical imaging data.

These promising techniques create systems that perform some diagnostic tasks at the level of expert radiologists. The systems have the potential to provide real-time assistance to radiologists, thereby reducing diagnostic errors, detecting disease early, improving patient outcomes, and reducing costs.

This session will explore the origins of AI and its applications to medical imaging, define key terminology, and showcase examples of real-world applications that suggest how AI and large language models may change the practice of medicine.

We'll also address the key limitations and challenges of AI that may limit the application of these new methods. Finally, we will present a forward-looking model predicting how AI will affect the radiology workforce in the next five years.