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Cited by 364Artificial Intelligence in Healthcare and EducationArtificial Intelligence ApplicationsElectronic Health Records Systems

A Systematic Review of the Barriers to the Implementation of Artificial Intelligence in Healthcare

Open access
Ahmed, Molla Imaduddin, Spooner, Brendan, Isherwood, John, Lane, Mark, Orrock, Emma, Dennison, Ashley·2023·Cureus
DOI 10.7759/cureus.46454PMC PMC10623210
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Abstract

Artificial intelligence (AI) is expected to improve healthcare outcomes by facilitating early diagnosis, reducing the medical administrative burden, aiding drug development, personalising medical and oncological management, monitoring healthcare parameters on an individual basis, and allowing clinicians to spend more time with their patients. In the post-pandemic world where there is a drive for efficient delivery of healthcare and manage long waiting times for patients to access care, AI has an important role in supporting clinicians and healthcare systems to streamline the care pathways and provide timely and high-quality care for the patients. Despite AI technologies being used in healthcare for some decades, and all the theoretical potential of AI, the uptake in healthcare has been uneven and slower than anticipated and there remain a number of barriers, both overt and covert, which have limited its incorporation. This literature review highlighted barriers in six key areas: ethical, technological, liability and regulatory, workforce, social, and patient safety barriers. Defining and understanding the barriers preventing the acceptance and implementation of AI in the setting of healthcare will enable clinical staff and healthcare leaders to overcome the identified hurdles and incorporate AI technologies for the benefit of patients and clinical staff.

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  • Many AI models, especially deep neural networks, operate as black boxes, providing predictions or classifications without clear explanations for their outputs.

    “Many AI models, especially deep neural networks, operate as “black boxes,” as they provide predictions or classifications without offering clear explanations for their outputs [9].”

    From Explainable AI in Clinical Decision Support Systems: A Meta-Analysis of Methods, Applications, and Usability Challenges (2025) · cites this paper as [9]

    The Review (page unavailable) defines the black-box phenomenon as an AI algorithm reaching a conclusion without users understanding its basis, and notes the lack of human-interpretable explanations for model predictions. It supports the general claim, but the passages do not specifically identify deep neural networks as especially prone to this problem. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • Implementation barriers for sepsis AI cluster around trust and interpretability, workflow integration, technical readiness and generalizability, governance and safety, leadership alignment, and the resources required for sustainment.

    “Implementation barriers for sepsis AI cluster around trust and interpretability, workflow integration, technical readiness and generalizability, governance and safety, leadership alignment, and the resources required for sustainment (Table 4) [10,33,34,46,48,77,78].”

    From Artificial Intelligence to Facilitate SEP-1 Measure Compliance and Fluid Management in Sepsis (2026) · cites this paper as [78]

    The Review (page not supplied) discusses trust and interpretability, workflow integration, generalisability, governance and safety, clinician and leader engagement, and ongoing funding as barriers to healthcare AI. It does not establish this grouping specifically for sepsis AI, so the sepsis-specific framing exceeds what this source supports. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

  • Broader digital health and implementation science literature has described phenomena such as non-adoption, abandonment and limited sustainability, but often focuses on technologies that fail to embed rather than those that are actively withdrawn after successful pilot use.

    “Broader digital health and implementation science literature has described phenomena such as non-adoption, abandonment and limited sustainability, but often focuses on technologies that fail to embed rather than those that are actively withdrawn after successful pilot use 12 .”

    From Radiology staff perspectives after decommissioning an artificial intelligence (AI) tool for expedited lung cancer triage (2026) · cites this paper as [12]

    Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

    Experimental — accuracy not yet measured

  • The lack of adequate training on AI tools is a systemic barrier that must be urgently addressed.

    “From an internal perspective, the lack of adequate training on AI tools is a systemic barrier that must be urgently addressed—especially if the goal is to adopt AI-based care systems across multiple levels of healthcare delivery [77].”

    From Barriers and Facilitators to Artificial Intelligence Implementation in Diabetes Management from Healthcare Workers’ Perspective: A Scoping Review (2025) · cites this paper as [77]

  • Relatively few studies have examined decommissioning of functional digital tools in healthcare.

    “Relatively few studies have examined decommissioning of functional digital tools in healthcare.”

    From Radiology staff perspectives after decommissioning an artificial intelligence (AI) tool for expedited lung cancer triage (2026) · cites this paper as [12]

    The Review discusses barriers to implementing and maintaining AI tools, not the decommissioning of functional digital tools. Searches for “decommissioning” and for discontinuing or retiring working digital health technologies found no passage assessing how many studies have examined that topic. Page numbers were unavailable in our copy. (LLM, conf=0.77)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 52

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Authors
Ahmed, M. I., Spooner, B., Isherwood, J., Lane, M., Orrock, E., & Dennison, A.
Affiliations
University Hospital Coventry (GB)
Corresponding author
Ahmed, Molla Imaduddin
ORCID
Venue
Cureus
Publisher
Springer Science and Business Media LLC
Published
2023-10-04
Volume
15
Issue
10
Pages
e46454-e46454
ISSN
2168-8184 (ISSN-L)
DOI
10.7759/cureus.46454
PMC
PMC10623210
OpenAlex
W4387340031
Cited by
364
References
69
Citations by year
2026: 122 · 2025: 196 · 2024: 44 · 2023: 2
FWCI
4.28
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Cureus (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
  • Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
Keywords
artificial intelligence in healthcare, implementation barriers, technology adoption, ethical barriers, social acceptance, patient safety, healthcare delivery efficiency

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