Implementing AI in the NHS Proves More Complex than Anticipated, Study Finds

Key Takeaways:

• A UCL-led study revealed that introducing AI diagnostic tools across NHS hospitals faced major challenges with governance, contracts, IT integration and staff training.
  
• By June 2025, 18 months after contracting was meant to be completed, over one-third of trusts (23 out of 66) had not yet implemented the AI systems in clinical practice.
  
• Researchers recommend stronger project management, more staff education on AI, and realistic timelines to ensure successful integration.
  
  

Background to the study

A major study led by researchers at University College London (UCL) has found that implementing artificial intelligence (AI) in NHS hospitals is considerably more difficult than policymakers and healthcare leaders initially expected. The study, published in The Lancet eClinicalMedicine on 10 September 2025, examined a £21 million NHS England programme launched in 2023. The programme aimed to introduce AI technology for diagnosing chest conditions, including lung cancer, across 66 NHS hospital trusts.

The research team conducted in-depth interviews with hospital staff and AI suppliers to understand how the tools were procured, set up, and used, while also identifying both the challenges encountered and the strategies that proved helpful in supporting implementation.

Delays and barriers to implementation

The study revealed that contracting and procurement processes took between four and ten months longer than expected. By June 2025, 18 months after contracting had been scheduled for completion, one-third of the hospital trusts (23 out of 66) were still not using the AI diagnostic systems in clinical practice.

Dr Angus Ramsey, principal research fellow at the UCL Department of Behavioural Sciences and Health and first author of the study, explained:

“Our study provides important lessons that should help strengthen future approaches to implementing AI in the NHS.

We found it took longer to introduce the new AI tools in this programme than those leading the programme had expected.

A key problem was that clinical staff were already very busy – finding time to go through the selection process was a challenge, as was supporting integration of AI with local IT systems and obtaining local governance approvals.

Services that used dedicated project managers found their support very helpful in implementing changes, but only some services were able to do this.

Also, a common issue was the novelty of AI, suggesting a need for more guidance and education on AI and its implementation.”

Key challenges identified

Researchers highlighted a range of challenges that slowed or complicated implementation, including:

• Staff workload pressures – Clinicians were already under heavy demand, making engagement with selection and integration processes difficult.
  
• Technological integration – Many NHS hospitals operate on ageing or varied IT systems, which created barriers for embedding the new AI tools.
  
• Governance processes – Local approvals and oversight requirements delayed progress.
  
• Lack of understanding and scepticism – Many staff expressed uncertainty or caution about the reliability and usefulness of AI in clinical care.
  

The study found that trusts that employed dedicated project managers were more successful at managing implementation, underscoring the importance of structured leadership in rolling out new technology.

Lessons for the future

The authors of the study cautioned that although AI tools hold promise for enhancing diagnostic services, they may not resolve workforce and system pressures as easily or quickly as policymakers might hope. As they wrote:

“AI tools may offer valuable support for diagnostic services, they may not address current healthcare service pressures as straightforwardly as policymakers may hope.”

The researchers recommended that:

• NHS staff should receive training on how AI can be used safely and effectively.
  
• Dedicated project management should be built into large-scale AI implementation programmes.
  
• Timelines for introducing AI should realistically reflect the complexity of NHS structures and systems.
  

Professor Naomi Fulop, senior author and professor of health care organisation and management at UCL, emphasised the complexity of the task:

“The NHS is made up of hundreds of organisations with different clinical requirements and different IT systems and introducing any diagnostic tools that suit multiple hospitals is highly complex.”

Funding and next steps

The research was funded by the National Institute for Health and Care Research and conducted collaboratively by teams from UCL, the Nuffield Trust and the University of Cambridge. The group is now conducting further studies on how AI tools perform once they are more firmly embedded in NHS practice.The findings are expected to offer valuable insights for the government’s 10-year health plan, published on 3 July 2025, which identified AI as a central element in modernising and improving NHS services.