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The rapid transformation of artificial intelligence across NHS services has taken on a new urgency within the last year. Since the publication of the much-anticipated 10-year plan earlier this year (2025), the publication set a bold position that the NHS is "the best-placed system in the world to harness the advances we are seeing in artificial intelligence (AI) and genomic science" (UK Government, 2025, p. 9).

With that in mind, it has become a matter of high priority that the diverse workforce which makes up the NHS are equipped and empowered to use AI to improve patient care, whilst maintaining an ethical approach to the risks involved.

In the view of Blake and Das (2023), the implementation of AI is an impertive to relieve pressures on staff in the NHS. The question which remains is not 'if', but 'how' should be utilised.

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This national policy document is a general overview suitable for NHS staff of all professions and banding to give clarity on how Copilot can be adopted as part of workplace activities. While the sections it addresses are brief in length, and it is the responsibility of individual organisations to implement as part of their own local policy, it forms a strong basis for using GenAI that can support tasks and avoid its pitfalls.

Having located this through staff internal communications, it is indicative of the increasing awareness of how staff across hospitals are beginning to engage with Copilot, which currently lacks the essential direction on how to treat this in a literate manner. Clinical and non-clinical staff will have interaction with this tool as one of the most immediately accessible AI platforms that is available, which makes informing them about what should and should not be uploaded, as well as good information governance practice, vital.

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This comprehensive article sets out a robust, but considered view, of how AI can be applied across nursing practice as a whole and make a positive impact in the care of patients. One of the areas, which may need to be balanced against the risks of applying patient data into AI systems, makes the case around improved clinical decision making and improving the results from tests that are taken. The article’s generalist approach offers insight on how AI can be applied as part of the curriculum for student nurses and should be a component of professional development in the profession.

Published in 2024, this article has already gained 111 citations, according to Web of Science, and the journal Nursing Open has a rigorous peer review process where it is a requirement that submissions should present new findings, whether good or bad. One of the authors, Khan Rony, has relevant positions in the US and Bangladesh.

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Rony, M.K.K., Parvin, Mst.R. and Ferdousi, S. (2023) ‘Advancing nursing practice with artificial intelligence: Enhancing preparedness for the future’, Nursing Open, 11(1), pp. 1–9. Available at: https://doi.org/10.1002/nop2.2070.

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Drafted by the British Medical Association (BMA), this report introduces how AI is already being delivered across NHS services. It details several examples of how it is being utilised by individual trusts, as well as global developments and apps that have the potential to impact practice. This includes decision-making tools, administration and natural language processing, which assist with transcription and interpreting patient feedback. The second section provides insights into the benefits and risks of AI, citing existing evidence (with links) which describe practical examples that have made a difference in cancer treatments and predicting future diagnoses of patients. This is balanced out with some of the greater risks of worsening health inequalities and offering a nuanced perspective of the future long-term impact and recommendations.

Supplied by the recognised professional body for doctors and medics, this document includes its own principles of AI usage, which can be disseminated within hospital settings.

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British Medical Association (2024) Principles for Artificial Intelligence (AI) and its application in healthcare. Available at: https://www.bma.org.uk/media/njgfbmnn/bma-principles-for-artificial-intelligence-ai-and-its-application-in-healthcare.pdf?utm_source=chatgpt.com. (Accessed: 24 November 2025)

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Although this particular article should be treated with some degree of caution, with reference to its publication in 2020 and the rapid developments that have emerged more recently, there are still important issues that remain relevant and correspond to other findings in this bibliography. The concerns surrounding patient data and how the majority of patients from one study are concerned about how their information is used by services remain a significant challenge. This is not just how it is used in care, but also areas such as research and, as cited, an AI tool from one NHS organisation that was rejected by patients.

The article makes a strong case for educating the wider public to have better awareness and understanding of how machine learning is safe and meets regulatory standards. Published by the World Health Organization, it meets expectations as a source that is broadly trustworthy.

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With a focus on primary and community care nursing, this article explores how pressures in this field can be supported with the integration of AI tools within aspects of patient care. What is especially useful, besides the accessible format it has been produced as, are the tables outlining explicit examples of both the tools and benefits AI tools have in areas of care. Another table also provides a summary list of the commons barriers to implementation, including digital literacy and health inequalities.

Despite concerns noted on ethics and individual capability, it does cautiously advocate for AI to be integrated as part of the work practice and skills which are important for community nursing. Published in the British Journal of Community Nursing, hosted as part of the Mark Allen Group, it is part of the reputed journal which is dedicated to this particular profession, and all content is peer reviewed.

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Palmer, S.J. (2025) ‘Artificial intelligence in primary and community care: opportunities and challenges’, British Journal of Community Nursing, 30(Sup9), pp. S25–S28. Available at: https://doi.org/10.12968/bjcn.2025.0137.

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This extensive online post from The King's Fund reports on its engagement with a cross-section of healthcare professionals, principally medical staff in hospitals and primary care, to explore the impact of AI tools. One of the most informative sections is the role of ambient voice technologies, which are known as AI scribes, to help with transcribing written information and can improve time-saving for staff on the frontline, as well as general written information like patient letters and fit notes.

There is a much broader picture detailed too, which contextualises the role of procurement of AI systems might affect patient experience, with one of the significant risks highlighting that siloed procurement and concentrating AI in some areas can still result in overwhelm elsewhere. Overall, it is an effective analysis which outlines the practical usage of AI in services while providing a high-level insight on what needs to be done centrally.

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Mistry, P. (2025) More than just hype: How emerging AI use is assisting health and Social Care. Available at: https://www.kingsfund.org.uk/insight-and-analysis/long-reads/emerging-ai-use-health-and-social-care (Accessed: 24 November 2025).

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This short opinion piece, though substantiated with other citations, makes a compelling case for how the nursing profession needs to respond to the evolution of AI. It states the current lack of clear positioning from the Nursing and Midwifery Council exact position of how AI tools should be safely regulated and used as part of patient care, which poses its own risks. There is a less favourable view around using AI, with a short section from a practising nurse indicating that these tools do not make for a valid substitute for critical decisions for patient care.

There is an inclusion of a generated response from Copilot, which seems to corroborate the view that it can create efficiencies in written documents and allow them to concentrate on patient care, but not to act as substitutes. It is important to note this piece has a distinct position, but provides a critical voice that is likely to be reflected in the workforce.

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Carlin, A. and Charalambous, L. (2025) ‘Navigating AI in nursing: the promise, perils and pragmatic considerations’, British Journal of Nursing, 34(11), pp. 538–539. Available at: https://doi.org/10.12968/bjon.2025.0255.

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This succinctly co-authored article, from a collaboration of academic and medical experts across Europe and USA, neatly elaborates in compact sections of how Large Language Models (LLM) can aid evidence synthesis, including systematic reviews. There are clear instances of how it can be used to assist, such as language translation and screening articles. This is weighed against the clear stance that individuals must remain vigilant and not allow AI to be uncritically evaluated where there are potential biases and infringing copyright, which has similarly been alluded to by the BMA and WHO. The conclusion draws a wary position that validation of LLMs needs to improve, with coordination across relevant institutions, which is consistent with the importance of keeping the human in the loop.

While researchers might be the target audience that work within healthcare based institutions, this will be relevant to library professionals and students who will be conducting literature reviews.

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Siemens, W. et al. (2025) ‘Opportunities, challenges and risks of using artificial intelligence for evidence synthesis’, BMJ Evidence-Based Medicine, p. bmjebm-113320. Available at: https://doi.org/10.1136/bmjebm-2024-113320.

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While information governance can often be assumed to have a implicit and less visible role in relation to quality patient care, it is essential across all staff categories, regardless of the extent of patient contact. Produced by the Information Commissioner’s Office, which is an arms length-body of the UK government and is the responsible authority for matters concerning data protection. Although the current guidance is currently under review, there are multiple areas for consideration by healthcare professionals and Data Protection Officers. One such area is on training AI and the possible risks when it comes to bias and prejudiced outcomes, which needs to be sensitively handled when it comes to protected characteristics, and where scepticism of how data is handled was highlighted among patients in the Thompson article.

There are issues which need to be interpreted in the context of best practice in healthcare, where AI tools for decision making can be applied when human input is ‘meaningful’, it is typically advised against with patient care.

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ICO (2023) Guidance on AI and data protection. Available at: https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/artificial-intelligence/guidance-on-ai-and-data-protection/?search=patient (Accessed: 27 November 2025).

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Blake, S.R. and Das, N. (2023) ‘Deploying artificial intelligence software in an NHS trust: a how-to guide for clinicians’, British Journal of Radiology, 97(1153), pp. 68–72. Available at: https://doi.org/10.1093/bjr/tqad043.

UK Government (2025) Fit for the Future - 10 Year Health Plan for England. UK Government.

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Patient Information Forum (2024) Balancing the risks and benefits of AI in the production of health information - HTML version. Available at: https://pifonline.org.uk/resources/balancing-the-risks-and-benefits-of-ai-in-the-production-of-health-information/html-version/. (Accessed: 28 November 2025)

Hopkins, E., Smith, S. and Wood, H. (2024) ‘Adoption and everyday use of artificial intelligence by NHS knowledge and library professionals in England’, Journal of EAHIL, 20(2), pp. 11–15. Available at: https://doi.org/10.32384/jeahil20619.

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S.C. Shelmerdine et al. (2024) ‘Artificial Intelligence (AI) Implementation within the NHS: The South West London AI Working Group Experience’, Clinical Radiology, 79(9), pp. 665–672. Available at: https://doi.org/10.1016/j.crad.2024.05.018.

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Thompson, C.L. and Morgan, H.M. (2020) ‘Ethical barriers to artificial intelligence in the national health service, United Kingdom of Great Britain and Northern Ireland’, Bulletin of the World Health Organization, 98(4), pp. 293–295. Available at: https://doi.org/10.2471/blt.19.237230.

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This multidisciplinary article, comprising both clinicians and research scientists as co-authors, offers fresh insight into practical measures to strengthen digital health literacy across organisations, staff and patients to become more adept with AI. One of the most interesting propositions is the creation of a new speciality, referred to as ‘Clinical AI’, which allows trained individuals to critique and appraise AI models, whilst also educating the wider workforce about practical application. Recognising capacity to train staff is often saturated when it comes to time pressures; it makes a strong case that electronic health literacy is critical to the ecosystem of both staff and patient care.

Incorporating health literacy practice, the article advocates for patients to be effective partners in shaping AI tools to help overcome personal reservations about how their data is used. Both Thompson and Palmer equally alluded to examples that without transparency, data consent will unlikely be approved.

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Korn Malerbi, F. et al. (2023) ‘Digital Education for the Deployment of Artificial Intelligence in Health Care’, Journal of Medical Internet Research, 25, pp. e43333–e43333. Available at: https://doi.org/10.2196/43333.

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NHS.net (2025) M365 Copilot and M365 Copilot Chat (Web) Acceptable Use Policy. Available at: https://comms-mat.s3.eu-west-1.amazonaws.com/Comms-Archive/M365+Copilot+Acceptable+Use+Policy+v1.1.pdf (Accessed: 19 November 2025).