Evidence map›Paper›PMID 40475792›Full record

ArticleHealth science reports2025

Navigating the Transformative Impact of Artificial Intelligence in Health Services Research.

Guosong Wu, Fengjuan Yang

Abstract read
In one paragraph

Article in Health science reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Guosong WuDepartment of Health Care Analytics, Shannon School of Business Cape Breton University Sydney Canada.ORCID 0000-0003-0440-0784
Fengjuan YangThe Centre for Health Informatics, Cumming School of Medicine University of Calgary Calgary Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and Aims: Artificial intelligence (AI) is transforming health services research by providing novel insights, enhancing care quality, and improving patient outcomes. This review sought to assess AI's impact on health services research, highlighting its applications, benefits, and associated challenges. Methods: We conducted a comprehensive review of recent literature on AI applications in health services research. Key areas of focus included image processing and language processing. The review also addressed the ethical and practical challenges of integrating AI into healthcare. Results: Over the past decade, AI-related research has markedly increased. AI has significantly advanced health services research by improving diagnostic precision, care quality, decision-making, hospital operations, and personalized care. The benefits of AI in image processing and language processing have been substantial, resulting in positive impacts on healthcare practices. However, integrating AI into healthcare presents considerable ethical and practical challenges, including the need for robust data security, the mitigation of algorithmic biases, and the achievement of interoperability among diverse data systems. Conclusions: AI offers significant potential to advance health services research and enhance patient care through powerful applications in image processing, language processing, diagnostic precision, decision-making, and hospital operations. By leveraging AI's capabilities, healthcare systems can achieve more personalized, efficient, and accurate care. Addressing key challenges is essential for the effective and equitable integration of AI into healthcare systems.

Indexed as

artificial intelligencehealth services researchquality of health care

Identifiers

PMID40475792
PMCPMC12138046

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.