Evidence map›Paper›PMID 33836752›Full record

ArticleBMC medical informatics and decision making2021

The role of artificial intelligence in healthcare: a structured literature review.

Silvana Secinaro, Davide Calandra, Aurelio Secinaro, Vivek Muthurangu, Paolo Biancone

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medical informatics and decision making, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 243 papers, 16 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
243citing papers in PubMed, 16 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

243 citing papers in PubMed, 16 syntheses or guidelines pooled it.

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183 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

5 authors.

Silvana SecinaroDepartment of Management, University of Turin, Turin, Italy.
Davide CalandraDepartment of Management, University of Turin, Turin, Italy. davide.calandra@unito.it.
Aurelio SecinaroOspedale Pediatrico Bambino Gesù, Rome, Italy.
Vivek MuthuranguInstitute of Child Health, University College London, London, UK.
Paolo BianconeDepartment of Management, University of Turin, Turin, Italy.

Funding

British Heart Foundation NH/18/1/33511
6 · The paper itself

Abstract

BACKGROUND/

introductionArtificial intelligence (AI) in the healthcare sector is receiving attention from researchers and health professionals. Few previous studies have investigated this topic from a multi-disciplinary perspective, including accounting, business and management, decision sciences and health professions.

methodsThe structured literature review with its reliable and replicable research protocol allowed the researchers to extract 288 peer-reviewed papers from Scopus. The authors used qualitative and quantitative variables to analyse authors, journals, keywords, and collaboration networks among researchers. Additionally, the paper benefited from the Bibliometrix R software package.

resultsThe investigation showed that the literature in this field is emerging. It focuses on health services management, predictive medicine, patient data and diagnostics, and clinical decision-making. The United States, China, and the United Kingdom contributed the highest number of studies. Keyword analysis revealed that AI can support physicians in making a diagnosis, predicting the spread of diseases and customising treatment paths.

conclusionsThe literature reveals several AI applications for health services and a stream of research that has not fully been covered. For instance, AI projects require skills and data quality awareness for data-intensive analysis and knowledge-based management. Insights can help researchers and health professionals understand and address future research on AI in the healthcare field.

Indexed as

Artificial IntelligenceDelivery of Health CareChinaClinical Decision-MakingHumansUnited KingdomArtificial intelligenceClinical decision-makingHealthcareManagementPatient data

Identifiers

PMID33836752
PMCPMC8035061

What OpenQuestion holds

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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.