Evidence map›Paper›PMID 39966286›Full record

SynthesisJournal of medical systems2025

Artificial Intelligence (AI) - Powered Documentation Systems in Healthcare: A Systematic Review.

Aisling Bracken, Clodagh Reilly, Aoife Feeley, Eoin Sheehan, Khalid Merghani, Iain Feeley

Abstract readSystematic Review
In one paragraph

Synthesis in Journal of medical systems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.

0numbers the graph read from it
0cells of the map it votes in
47citing 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

47 citing papers in PubMed.

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  11. Artificial Intelligence in Spiritual Care: Modified Delphi Study.Journal of medical Internet research · 2026
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  15. Review
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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.

Aisling BrackenRoyal College of Surgeons in Ireland (RCSI), 123 Stephen's Green, Dublin 2, Ireland. Aislingbracken24@rcsi.com.
Clodagh ReillyUniversity College Dublin (UCD), Belfield, Dublin 4, Ireland.
Aoife FeeleyRoyal College of Surgeons in Ireland (RCSI), 123 Stephen's Green, Dublin 2, Ireland.
Eoin SheehanSchool of Medicine, University of Limerick (UL), Castletroy, Limerick, Ireland.
Khalid MerghaniMidlands Regional Hospital Tullamore, Arden Rd., Tullamore, Offaly, Ireland.
Iain FeeleyMidlands Regional Hospital Tullamore, Arden Rd., Tullamore, Offaly, Ireland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial Intelligence (AI) driven documentation systems are positioned to enhance documentation efficiency and reduce documentation burden in the healthcare setting. The administrative burden associated with clinical documentation has been identified as a major contributor to health care professional (HCP) burnout. The current systematic review aims to evaluate the efficiency, quality, and stakeholder opinion regarding the use of AI-driven documentation systems. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines a comprehensive search was conducted across PubMed, Embase and Cochrane library. Two independent reviewers applied inclusion and exclusion criteria to identify eligible studies. Details of AI technology, document type, document quality and stakeholder experience were extracted. The review included 11 studies. All included studies utilised Chat generated pretrained transformer (Chat GPT, Open AI, CA, USA) or an ambient AI technology. Both forms of AI demonstrated significant potential to improve documentation efficiency. Despite efficiency gains, the quality of AI-generated documentation varied across studies. The heterogeneity of methods utilised to assess document quality influenced interpretation of results. HCP opinion was generally positive, users highlighted ease of use and reduced task load as primary benefits. However, HCPs also expressed concerns about the reliability and validity of AI-generated documentation. Chat GPT and ambient AI show promise in enhancing the efficiency and quality of clinical documentation. While the efficiency benefits are clear, the challenges associated with accuracy and consistency need to be addressed. HCP experiences indicate a cautious optimism towards AI integration, however reliability will depend on continued refinement and validation of the technology.

Indexed as

Artificial IntelligenceDocumentationHumansAmbient intelligenceArtificial Intelligence (AI)Chat GPTMedical documentation

Identifiers

PMID39966286
PMCPMC11835907

What Socratic 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 Socratic graph.