Artificial intelligence and the NHS: a qualitative exploration of the factors influencing adoption
Open accessAbstract
BACKGROUND: AI has the potential to improve healthcare. However, there is limited research investigating the factors which influence the adoption of AI within a healthcare system. RESEARCH AIMS: I aimed to use innovation theory to understand the barriers and facilitators that influence AI adoption in the NHS; and to explore solutions to overcome these barriers, and examine these factors, particularly within radiology, pathology and general practice. METHODOLOGY: Twelve semi-structured, one-to-one interviews were conducted with key informants. Interview data were analysed using thematic analysis. FINDINGS: A range of barriers and facilitators to the adoption of AI within the NHS were identified, including IT infrastructure and language clarity. Several solutions to overcome the barriers were proposed by participants, including education strategies and innovation champions. CONCLUSION: Future research should explore the importance of IT infrastructure in supporting AI adoption, examine the terminology around AI and explore specialty-specific barriers to AI adoption in greater depth.
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Clinicians expressed the need to understand both the scientific and clinical bases of the recommendations provided by the AI to confidently validate and apply the decision.
“Clinicians expressed the need to understand both the scientific and clinical bases of the recommendations provided by the AI to confidently validate and apply the decision [19,20,36-38].”
From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [36]
The Challenges ahead section reports participants raising questions about explanations for AI-influenced treatment decisions, and the Conclusion calls for education to improve healthcare professionals’ understanding of AI. Neither passage says clinicians needed to understand both the scientific and clinical bases of individual recommendations to confidently validate and apply them. Our copy provides no page numbers for these passages. (LLM, conf=0.76)
Experimental — accuracy not yet measuredShortages of personnel with the required skills and the quality of IT infrastructure available for AI implementation were reported as barriers.
“Shortages of personnel with the required skills were reported as barriers, along with the quality of IT infrastructure available for AI implementation [32,35,36,40,45,50].”
From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [36]
In “Socio-political context of the NHS in 2020” (page unavailable), nine participants mentioned the quality of NHS IT infrastructure as a barrier to AI adoption, though the Conclusion notes that participants disagreed. The paper discusses a need for education and for IT project managers, but the passages found do not report a shortage of personnel with the required skills as a barrier. (LLM, conf=0.84)
Experimental — accuracy not yet measuredSpecific concerns related to adoption include models being trained on data not representative of the patient population or not containing diverse data as related to social determinants of health.
“Specific concerns related to adoption include models being trained on data not representative of the patient population or not containing diverse data as related to social determinants of health [19,35,36].”
From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [36]
In “Challenges ahead” (page not provided), a participant describes an AI tool performing poorly at a second site with a different ethnic group, and others raise the risk of biased algorithms. This supports a concern about patient-population differences in adoption, but the paper does not specifically identify unrepresentative training data or missing social-determinants-of-health data as the cause. (LLM, conf=0.84)
Experimental — accuracy not yet measuredMorrison quoted stakeholders seeking clarity on the extent to which there is a need to provide transparency on AI output to patients and how this is directed by legislation or data protection laws.
“Morrison [36] particularly quoted stakeholders seeking clarity on the extent to which there is a need to provide transparency on AI output to patients and how this is directed by legislation or data protection laws.”
From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [36]
A lack of an agreed standard and benchmark for accuracy is an impediment to implementation and, subsequently, adoption.
“Morrison [36] identified a lack of an agreed standard and benchmark for accuracy (how accurate does an AI tool need to be before it is approved for clinical practice) as an impediment to implementation and, subsequently, adoption”
From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [36]
There is no clarity in the area of regulatory structures with regard to which regulatory body should be consulted for AI developments and deployments.
“Other researchers noted that there was no clarity in the area of regulatory structures with regard to which regulatory body should be consulted for AI developments and deployments [36,53].”
From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [36]
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- Authors
- Morrison, K.
- Affiliations
- University of Birmingham (GB)
- Corresponding author
- Morrison, Kirsty
- ORCID
- Morrison, Kirsty
- Venue
- Future Healthcare Journal
- Publisher
- Elsevier BV
- Published
- 2021-11
- Volume
- 8
- Issue
- 3
- Pages
- e648-e654
- ISSN
- 2514-6645 (ISSN-L)
- PMC
- PMC8651325
- OpenAlex
- W3196560242
- Cited by
- 43
- References
- 39
- Citations by year
- 2026: 8 · 2025: 10 · 2024: 12 · 2023: 8 · 2022: 5
- FWCI
- 1.81
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Future Healthcare Journal (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- artificial intelligence adoption, National Health Service, barriers and facilitators, innovation theory, radiology, pathology, general practice, IT infrastructure, AI terminology, innovation champions, health technology adoption
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