• Understanding the regulatory landscape for AI-enabled medical devices and clinical applications
• Navigating evidence requirements, validation and safety assurance before deployment
• Managing algorithm updates, post-market monitoring and emerging risks
• Balancing patient protection with the need to accelerate safe innovation

• Assessing the data quality, interoperability and technical infrastructure required for effective AI deployment
• Connecting AI initiatives with existing NHS digital services and operational workflows
• Addressing information governance, cyber resilience and the risks associated with fragmented systems
• Developing a sustainable approach to investment, implementation and long-term maintenance

Stephen Firman, Regional Director of Digital Transformation, NHS England

Andy Haywood, Chief Digital and Data Officer, Health Innovation Manchester

This panel will discuss how to overcome the systemic barriers that currently prevent successful AI pilots from transitioning into business-as-usual NHS services. Participants will explore practical strategies for aligning national digital priorities directly with regional and local implementation plans, while tackling critical operational hurdles including interoperability, legacy infrastructure, procurement, and organisational readiness. Finally, the session will establish how trusts and integrated care systems can effectively measure tangible benefits and replicate these successful approaches across the wider healthcare landscape.

• Bridging the gap between emerging AI capabilities and practical health and care applications

• Identifying the partnerships needed between researchers, clinicians, policymakers and technology developers
• Addressing barriers to adoption, including evidence generation, implementation capacity and evaluation
• Defining what sustainable, patient-centred AI innovation should look like across the health system

Aswin Thampy, Fellow in Clinical AI, NHS

Saif Ahmed, Chief Medical Information Officer, Tameside and Glossop Integrated Care NHS FT

• Understand how existing clinical governance frameworks can be applied to AI adoption, ensuring patient safety, effectiveness, equity and accountability

• Identify how NHS organisations can balance the risks of AI with the need to support innovation, avoiding unnecessary bureaucracy and barriers to implementation

• Recognise the importance of ongoing validation, performance monitoring, workforce capability and clinical oversight when deploying AI-enabled tools

• Exploring how AI and advanced analytics can turn complex health data into actionable clinical insights
• Examining opportunities for predictive modelling, earlier intervention and more personalised treatment
• Understanding the importance of representative datasets, data quality and external validation
• Translating research advances into scalable applications that improve outcomes across diverse patient populations

• Translating national AI ambitions into practical priorities for the NHS and wider health system
• Identifying where AI can have the greatest impact on productivity, access and patient outcomes
• Aligning investment, policy and organisational readiness to accelerate responsible adoption
• Defining the measures needed to demonstrate progress and long-term value

• Describe patient, public and healthcare professional attitudes towards AI in healthcare across five European countries
• Identify key factors influencing acceptance of AI, including trust, explainability, regulation, training and human oversight
• Recognise the importance of incorporating stakeholder perspectives into the development and implementation of AI-enabled healthcare tools
• Understand the aims of the ARISTOTELES project and how its AI-enabled tools seek to support the integrated management of atrial fibrillation and multimorbidity