Evidence map›Paper›PMID 37927664›Full record

ReviewCureus2023

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

Molla Imaduddin Ahmed, Brendan Spooner, John Isherwood, Mark Lane, Emma Orrock, Ashley Dennison

Open access · diamondAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Cureus, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 227 papers, 17 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
227citing papers in PubMed, 17 pooled it
10.6field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

227 citing papers in PubMed, 17 syntheses or guidelines pooled it, 277 citations in OpenAlex.

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  11. Artificial intelligence in rehabilitation: a living systematic mapping review - first release.European journal of physical and rehabilitation medicine · 2025
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167 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 3 institutions in 2 countries.

Molla Imaduddin AhmedPaediatric Respiratory Medicine, University Hospitals of Leicester NHS Trust, Leicester, GBR.
Brendan SpoonerIntensive Care and Anaesthesia, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, GBR.
John IsherwoodHepatobiliary and Pancreatic Surgery, University Hospitals of Leicester NHS Trust, Leicester, GBR.
Mark LaneOphthalmology, Birmingham and Midland Eye Centre, Birmingham, GBR.
Emma OrrockHead of Clinical Senates, East and West Midlands Clinical Senate, Leicester, GBR.
Ashley DennisonHepatobiliary and Pancreatic Surgery, University Hospitals of Leicester NHS Trust, Leicester, GBR.
University Hospitals of Leicester NHS Trust · GBBirmingham and Midland Eye Centre · GBServiço Nacional de Aprendizagem Comercial · BR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

ai & robotics in healthcareartificial intelligence in healthcarebarriers to implementationhealth care deliveryliterature reviewmachine learning (ml)

Identifiers

PMID37927664
PMCPMC10623210
OpenAlexW4387340031

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.