The recent publication of the AI Opportunities Action Plan marks a pivotal moment for healthcare innovation in the UK. It’s a commitment to lay the foundations for and to embrace AI technologies, benefiting people’s lives across the UK.  This is an exciting time, in particular for fellow technologist contributing to this change. AI holds immense potential to transform healthcare, offering unprecedented opportunities to address systemic challenges and improve care delivery.   

However, with great opportunity comes great responsibility. Ensuring these tools are safe, ethical, and aligned with clinical needs is paramount. Questions about trust, fairness, and data sharing remain central to discussions about AI in the NHS. Addressing these challenges, whilst still innovating at pace, is as critical as developing the technology itself. The UK is in a unique position legislatively, culturally and geographically between the US and EU, meaning it can choose its own path to AI independence, whilst collaborating closely with international partners.  

 

Keir Starmer Plan for ChangeA catalyst for change 

The AI Opportunities Action Plan reflects the UK’s ambition to become a global leader in both AI and health technology. In addition to this, recent funding and policy announcements are laying the groundwork for bold transformation, with the government focused on tackling long-standing NHS challenges. A key step in this effort is a £150 million procurement drive aimed at securing AI solutions for areas like medical imaging, predictive analytics and operational efficiency. This initiative underscores a strong commitment to integrating AI into healthcare and ensuring innovations are swiftly adopted within NHS operations. 

The timing is critical. The NHS is under increasing pressure to tackle inefficiencies, reduce waiting lists, and improve access to care. AI presents a unique opportunity to address these challenges and could act as a powerful catalyst for change. However, its success hinges on overcoming significant roadblocks. 

The Action Plan identifies the compute capabilities required to both train and host AI technologies. An area I’m personally more excited about is unlocking public data sets via the creation of the National Data Library (NDL), which almost certainly healthcare will be one of 5 prioritised high-impact data sets. During this change, the conversation is bound to shift from AI technologies themselves to the need for high-volumes of quality, representative data. 


Identifying the roadblocks

While the potential of AI is immense, its success depends on overcoming three challenges. These are not theoretical concerns, I’ve encountered them firsthand, and overcoming them is essential to ensuring AI delivers on its promise to transform the NHS.

Trust and transparency

Trust is essential when it comes to AI in healthcare. The public’s confidence in how AI systems are used, and how patient data is handled, remains a significant challenge. Recent findings from the UK’s Public Attitudes to Data and AI Tracker Survey reveal widespread concerns. 79% of people are anxious about how their personal data is used, and 63% are unsure about how it’s handled. Over 50% of people say they would trust AI more if they understood how it works and how decisions are made.  
 
These findings highlight the importance of addressing transparency in AI systems, particularly in healthcare, where patients need to trust the technology that impacts their wellbeing. Without trust, even the most advanced AI tools will struggle to gain traction. This trust is earned through transparent communication on how data is used. There is also an education element to increasing AI awareness, and the potential benefits and risk of developing and deploying such technologies.
 

Data quality, bias, and equity

AI relies on high-quality, accessible data to function effectively. The NHS’s Federated Data Platform (FDP) aims to integrate operational data, but challenges around governance, privacy, and standardisation persist.  As of November 2024, only 87 NHS acute hospital trusts and 28 Integrated Care Boards (ICBs) have adopted the FDP, with adoption varying across regions. These types of government initiative always need to be treated with a degree of scepticism. For example, The National Program for IT for the NHS aimed at creating integrated patient records was dismantled in 2011 following a series of delays, opposition form stakeholders and costing over £10 billion. 
 
While the platform holds promise, concerns over data governance and privacy need addressing to maximise its potential. AI is only as good as the data it’s built on, and incomplete or biased training datasets lead to sub-optimal models. This can unintentionally reinforce health inequalities, particularly if the data doesn’t accurately represent diverse patient populations. Additionally, digital exclusion, where patients lack access to technology or the internet, can prevent some individuals from benefiting from AI-driven solutions, exacerbating healthcare disparities. Addressing these issues is crucial to ensuring AI benefits all patients, not just a select few. 

 Integration and adoption

The integration of AI into the NHS presents significant challenges due to the scale and complexity of the organisation. The Darzi report highlights a critical opportunity for the NHS to reinvent itself through AI, describing it as a “revolution” that requires a fundamental shift in how healthcare is delivered. However, the NHS remains in the "foothills of digital transformation," with many areas still operating on outdated IT systems that hinder AI implementation.  
 
Fragmented procurement processes and resistance to change further complicate matters. The report points to "digital inertia" within the NHS, where resistance often stems from concerns about disruption or a lack of understanding. Overcoming these barriers will require significant effort to achieve widespread AI adoption and integration. The healthcare workforce is under such immense pressure, that even if a particular technology offers significant benefits, there simply is no bandwidth to adopt it. 


Progress made and lessons learned 

As the UK government pushes for AI adoption across the NHS, overcoming these challenges requires more than identifying roadblocks; it demands thoughtful strategies. At Definition Health, our work in developing AI-powered healthcare solutions has provided valuable insights into navigating these complexities. 

 

  • 1. Collaboration
  • 2. Evidence
  • 3. Accountability
Collaboration is key

Working closely with NHS clinicians throughout the development process ensures AI tools are grounded in real-world applications. Co-designing solutions with healthcare professionals helps align AI tools with clinical workflows, ensuring they support NHS staff rather than disrupt them. This fosters trust and enhances decision-making.  

Engaging with stakeholders early is critical to preventing any fundamental issues “baked in” the technology. Development needs to be driven by specific, high-value use cases, keeping the human in the loop and having a common understanding of the AI limitations.

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Robust evidence generation

AI tools must undergo rigorous testing through clinical pilots to ensure their reliability and real-world applicability before scaling. This process not only validates effectiveness but also refines algorithms based on clinician feedback and patient outcomes.

Presenting a thorough evidence-based case builds confidence among clinicians, patients, and regulators by demonstrating AI's practical value, paving the way for broader adoption.

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Accountability and ethical standards

Beyond proving functionality, AI tools must meet stringent regulatory, ethical, and safety standards. At Definition Health, we rigorously test our systems to ensure they remain reliable and adaptable in diverse healthcare settings.

These safeguards are essential to build trust among clinicians and patients, ensuring AI tools deliver value without compromising safety. This evidence mentioned previously, is needed when applying for regulatory approval - often seen as hindrance by technology companies, but actually a barrier to entry and a critical element to adoption.

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Looking to the future

The scale of the transformation required in healthcare systems is immense. The future of AI in healthcare lies in its ability to transition the NHS from a reactive system to one that anticipates needs and personalises care. We need to shift care from hospitals to primary care and the community. The analysis of biomarkers  and multi-modal data is key in support patients much earlier in the disease progression. We need to introduce efficiencies into the system to release clinicians time to focus on the most severe cases. AI technologies are perfectly positioned to do so.  

surgeons-performing-operation-operation-roomImagine a world where AI predicts health issues before they arise, tailors treatment plans to individual patients, and empowers clinicians to focus on care rather than administrative tasks. This move toward precision medicine and preventative care will be essential for managing the increasing demand on healthcare services, improving patient outcomes, and alleviating the burden on NHS staff.  

At Definition Health, my team and I are proud to contribute to this transformation. By predicting surgical outcomes and personalising care pathways, we help clinicians make informed decisions that ultimately enhance patient care. The potential of AI is truly transformative. As we continue developing tools that empower clinicians, we believe that with ongoing investment, collaboration, and equitable access, we can build an NHS fit for the future.  

The journey to integrating AI into the NHS is not without its challenges, but the rewards are immense. By addressing issues of trust, data quality, and integration, we can unlock AI’s full potential to revolutionise healthcare. The AI Opportunities Action Plan is a promising step forward, but its success will depend on sustained collaboration, robust evidence generation, and a commitment to ethical standards. Together, we can ensure that AI not only transforms the NHS but also delivers equitable, high-quality care for all. 

 



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