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John Isherwood is a former East Midlands Clinical Senate Leadership Fellow, current East Midlands Clinical Senate Council member and a consultant hepatobiliary and pancreatic surgeon in Leicester. Clinical Senates are non-statutory advisory bodies, which bring together multi-professional clinical leaders across its region, to provide expert clinical advice and guidance to all parts of the healthcare system to drive improvement. Health Innovation East Midlands has a position on the Clinical Senate Council.
How can we understand the barriers to AI in healthcare and how can we move forward? In this blog, John gives his perspective on why these barriers matter, the role of regional innovation partnerships and the future of AI in healthcare.
Artificial intelligence (AI) is frequently described as one of the technologies most likely to transform healthcare. From supporting earlier diagnosis to reducing administrative burden and enabling more personalised treatment, the potential benefits are significant. Yet despite this promise, the adoption of AI across healthcare systems has been slower than many expected.
As someone involved in supporting health innovation across the East Midlands, my Midlands Senates Fellow colleagues and I recently completed an independent systematic review titled: “A Systematic Review of the Barriers to the Implementation of Artificial Intelligence in Healthcare” (published in Cureus, 2023).
This comprehensive peer-reviewed analysis, drawing from 59 articles, outlines the key hurdles slowing AI adoption in clinical settings despite its promise to enhance diagnosis, reduce administrative burdens, personalise care, and help tackle post-pandemic waiting lists.
The report has been delivered into the wider healthcare economy to individuals and organisations that may find this is of interest and wish to take forward within their own organisations.
The review identified six broad categories of barriers:
In a time of workforce pressures and long waits, AI could streamline pathways and free clinicians for patient-facing care. However, an uneven uptake shows that we must address these obstacles to avoid delay and missed opportunities.
For example, one of the most significant challenges is data governance and privacy. AI systems rely on large volumes of patient data to train algorithms; however, healthcare data is highly sensitive and access to complete, comprehensive data is crucial. In addition, we must ensure information is used safely, securely, and with appropriate consent, which is essential to maintaining trust.
Another key barrier relates to technical infrastructure and integration. Healthcare organisations often operate across multiple digital systems that were not designed to work together. Ensuring system integration is important going forward; however, it can be complex and resource intensive.
The review also highlights the importance of workforce readiness. Many clinicians have had limited exposure to AI training, which can make it difficult to evaluate or confidently adopt these technologies. Supporting healthcare professionals to understand both the opportunities and limitations of AI will be crucial.
Finally, there are important questions around regulation and accountability, which links to patient safety and ethical implications too. If an AI-assisted clinical decision leads to harm, it is not always clear who is responsible – the clinician, the organisation, or the technology developer. This uncertainty can create hesitation around adoption.
These challenges are not unique to one organisation or region. They reflect broader system-level issues that require collaboration to overcome.
At Health Innovation East Midlands, one of the 15 health innovation networks across England, we work with NHS organisations, academic partners and industry innovators to support the safe adoption of new technologies. In the context of AI, this often means helping projects move from promising ideas to real-world evaluation.
Our role includes supporting organisations to:
By acting as a bridge between innovators and healthcare systems, we can help address some of the practical barriers that often slow the implementation of new technologies.
AI will almost certainly play an increasingly important role in healthcare over the coming years. However, its success will depend not only on technological progress but also on how effectively we address the barriers surrounding data governance, regulation, workforce engagement, and patient trust.
Through collaboration and shared learning across the healthcare ecosystem, we can help ensure AI technologies are implemented in ways that are safe, equitable, and genuinely beneficial for patients. Looking ahead, the Health Innovation Network will continue championing these solutions through evaluation programmes, collaborative pilots, and events that bring together innovators, clinicians, and regulators.
East Midlands Clinical Senate Council member and consultant hepatobiliary and pancreatic surgeon