• Getting our orientation right from the start - how might we think about AI in healthcare using our data in a way that clinicians, patients and service users will trust?
• Focusing on what matters most to best realise value - where in healthcare might AI do better than humans, and where it may not?

• Participants will be able to articulate the key infrastructure components (data interoperability, governance, cybersecurity, validation pipelines) required to support AI in health
• Participants will learn how to assess existing organisational readiness (culture, leadership, workflows) for integrating AI safely
• Participants will gain insight into strategies to phase in AI adoption (pilot to scale) while managing risk, monitoring, and continuous improvement

• Understand what we mean by “narrative latency” in complex care, and where it shows up in day-to-day practice
• Distinguish between short-form and long-form documentation workflows and understand why they require different AI approaches
• Explore how these approaches can work alongside existing expectations around clinical review, authorship and accountability
• Learn from practical examples of how AI documentation is being used in complex care environment

• Recognise, through a real-world clinical example, how decision-making and procurement policy affects SME's developing AI within healthcare
• Quantify the gap between intranet-based guideline access and AI-supported access using peer-reviewed evidence (Waldock, Darzi et al., 2026)
• Explain why AI in healthcare should augment existing clinical knowledge systems, not replace clinical judgement
• Identify the characteristics of trustworthy AI tools for guideline access, including citation, auditability, and hallucination-free design

• NHS England’s latest buyer guidance and assurance frameworks for AI scribes and tools
• How policy aligns with medical device regulations and GP IT standards
• Opportunities and challenges in embedding generative AI in front-line primary care

Linda Vernon, Head of Digital Empowerment, Lancashire & South Cumbria Integrated Care Board

Dan Howl, Head of Policy & Public Affairs, BCS, The Chartered Institute for IT

Iblal Rakha, Clinical AI Fellow, NHS England

• Explore how AI can support, not replace, clinicians and the wider workforce
• Understand the skills, training, and culture shift needed for safe and effective adoption
•  Discuss workforce planning: ensuring equitable access, reducing burden, and embedding AI into daily practice

• Demonstrate how the sandboxing can enable real-world testing of AI to inform proportionate regulation
• Surface key cross-cutting challenges (e.g. explainability, post-market monitoring, change and risk management) observed in practice
• Translate these insights into emerging regulatory expectations and mitigation strategies
• Highlight how sandbox learning is shaping adaptive, lifecycle-based regulation in the UK

• Understand key human-factors issues in deploying AI in clinical settings 

• Identify the critical data and system design challenges for trusted AI in healthcare, including data quality, algorithm transparency/explainability, bias and validation in real-world contexts

• Evaluate strategies to integrate AI/decision‐support tools effectively into clinical workflows and mobile/remote health settings, considering user-experience, trust and uptake

• Analyse how parliamentary inquiries, local policy pilots, and ethical frameworks are shaping AI governance in the UK health and care context
• Discuss how tools and frameworks developed through partnerships can inform regulators and NHS bodies
•  Identify pathways for aligning national safety regulation with community-level innovation and adoption

Saif Ahmed, Associate Medical Director Digital & Clinical Digital Lead for Transformation, Tameside and Glossop ICFT & Health Innovation Manchester 

Ahmed Binesmael, Senior Improvement Analyst, The Health Foundation

Helen Cole, Executive Lead for Health Technologies and Evaluation, Northern Health Science Alliance

Gary Leeming, Director of the Civic Data Co-operative, University of Liverpool

Damilola Ogungbemi, Data Scientist, Leeds Institute for Data Analytics

• Understand how Secure Data Environments can support safe and effective AI use in healthcare
• Explore how NHS, community, and social care data can be better connected for AI innovation
• Learn how to balance data access with privacy, security, and public trust
• Understand how the UK’s leadership in establishing a global AI regulatory network informs data governance and innovation in health AI.