Evidence map›Paper›PMID 39348889›Full record

SynthesisJournal of medical Internet research2024

Clinical Decision Support and Natural Language Processing in Medicine: Systematic Literature Review.

Hans Eguia, Carlos Luis Sánchez-Bocanegra, Franco Vinciarelli, Fernando Alvarez-Lopez, Francesc Saigí-Rubió

Abstract readSystematic Review
In one paragraph

Synthesis in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 1 pooled it
–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

18 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  5. Artificial intelligence for personalized multiple micronutrient supplementation in maternal health.International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2026
    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

5 authors.

Hans Eguia *SEMERGEN New Technologies Working Group, Madrid, Spain.ORCID 0000-0001-6315-8559
Carlos Luis Sánchez-Bocanegra *Faculty of Health Sciences, Universitat Oberta de Catalunya (UOC), Barcelona, Spain.ORCID 0000-0001-7033-5971
Franco Vinciarelli *SEMERGEN New Technologies Working Group, Madrid, Spain.ORCID 0000-0003-2995-6957
Fernando Alvarez-Lopez *Faculty of Health Sciences, Universidad de Manizales, Manizales, Colombia.ORCID 0000-0002-2614-7735
Francesc Saigí-Rubió *Faculty of Health Sciences, Universitat Oberta de Catalunya (UOC), Barcelona, Spain.ORCID 0000-0001-9616-1551

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEnsuring access to accurate and verified information is essential for effective patient treatment and diagnosis. Although health workers rely on the internet for clinical data, there is a need for a more streamlined approach.

objectiveThis systematic review aims to assess the current state of artificial intelligence (AI) and natural language processing (NLP) techniques in health care to identify their potential use in electronic health records and automated information searches.

methodsA search was conducted in the PubMed, Embase, ScienceDirect, Scopus, and Web of Science online databases for articles published between January 2000 and April 2023. The only inclusion criteria were (1) original research articles and studies on the application of AI-based medical clinical decision support using NLP techniques and (2) publications in English. A Critical Appraisal Skills Programme tool was used to assess the quality of the studies.

resultsThe search yielded 707 articles, from which 26 studies were included (24 original articles and 2 systematic reviews). Of the evaluated articles, 21 (81%) explained the use of NLP as a source of data collection, 18 (69%) used electronic health records as a data source, and a further 8 (31%) were based on clinical data. Only 5 (19%) of the articles showed the use of combined strategies for NLP to obtain clinical data. In total, 16 (62%) articles presented stand-alone data review algorithms. Other studies (n=9, 35%) showed that the clinical decision support system alternative was also a way of displaying the information obtained for immediate clinical use.

conclusionsThe use of NLP engines can effectively improve clinical decision systems' accuracy, while biphasic tools combining AI algorithms and human criteria may optimize clinical diagnosis and treatment flows.

trial registrationPROSPERO CRD42022373386; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=373386.

Indexed as

Decision Support Systems, ClinicalElectronic Health RecordsNatural Language ProcessingArtificial IntelligenceHumansAIartificial intelligenceCDSSclinical decision support systemclinical information extractiondiagnosiselectronic health recordhealth recommender systemhealth workersnatural language processingpatientsystematic literature reviewtreatment

Identifiers

PMID39348889
PMCPMC11474138

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.