Evidence map›Paper›PMID 41191795›Full record

ReviewJMIR AI2025

AI in Health Care Service Quality: Systematic Review.

Eman Alghareeb, Najla Aljehani

Abstract readReview
In one paragraph

Review in JMIR AI, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
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

2 authors.

Eman AlghareebEastern Health Cluster, Saudi Electronic University, Abi Bakr As Siddiq Road, P.O. Box 93499, Riyadh, 11673, Kingdom of Saudi Arabia, ‪+966 50 549 1908‬.ORCID http://orcid.org/0009-0004-6150-0348
Najla AljehaniDepartment of Public Health, College of Health Sciences, Saudi Electronic University, Riyadh, Kingdom of Saudi Arabia.ORCID http://orcid.org/0000-0003-0890-0698

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Artificial intelligence (AI) is a rapidly evolving technology with the potential to revolutionize the health care industry. In Saudi Arabia, the health care sector has adopted AI technologies over the past decade to enhance service efficiency and quality, aligning with the country's technological thrust under the Saudi Vision 2030 program. Objective: This review aims to systematically examine the impact of AI on health care quality in Saudi Arabian hospitals. Methods: A meticulous and comprehensive systematic literature review was undertaken to identify studies investigating AI's impact on health care in Saudi Arabia. We collected several studies from selected databases, including PubMed, Google Scholar, and Saudi Digital Library. The search terms used were "Artificial Intelligence," "health care," "health care quality," "AI in Saudi Arabia," "AI in health care," and "health care providers." The review focused on studies published in the past 10 years, ensuring the inclusion of the most recent and relevant research on the effects of AI on Saudi Arabian health care organizations. The review included quantitative and qualitative analyses, providing a robust and comprehensive understanding of the topic. Results: A systematic review of 12 studies explored AI's influence on health care services in Saudi Arabia, highlighting notable advancements in diagnostic accuracy, patient management, and operational efficiency. AI-driven models demonstrate high precision in disease prediction and early diagnosis, while machine learning optimizes telehealth, electronic health record compliance, and workflow efficiency, despite adoption challenges like connectivity limitations. Additionally, AI strengthens data security, reduces costs, and facilitates personalized treatment, ultimately enhancing health care delivery. Conclusions: The review underscores that AI technologies have significantly improved diagnostic accuracy, patient management, and operational efficiency in Saudi Arabia's health care system. However, challenges such as data privacy, algorithmic bias, and robust regulations require attention to ensure successful AI integration in health care.

Indexed as

AIartificial intelligencehealth care qualitySaudi Arabiatechnology

Identifiers

PMID41191795
PMCPMC12594439

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.