Evidence map›Paper›PMID 29848472›Full record

ReviewJournal of medical Internet research2018

Artificial Intelligence for Diabetes Management and Decision Support: Literature Review.

Ivan Contreras, Josep Vehi

Registry-linked trialAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Journal of medical Internet research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04517201 (Study to Assess Artificial Intelligence-Assisted Insulin Titration System on Inpatients Glucose Control), which is not on this map. Cited by 180 papers, 11 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
180citing papers in PubMed, 11 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.

NCT04517201 naunknown statusnot on this mapstarted 2020, after this paper: background citation

Study to Assess Artificial Intelligence-Assisted Insulin Titration System on Inpatients Glucose Control

TypeinterventionalSponsorShanghai Zhongshan HospitalRan2020 to 2021Enrolled120ConditionsType 2 DiabetesArmsiNCDSS based insulin regime, Physician based insulin regime
3 · Its place in the literature

Who cites it

180 citing papers in PubMed, 11 syntheses or guidelines pooled it.

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120 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

2 authors.

Ivan ContrerasModeling, Identification and Control Laboratory, Institut d'Informatica i Aplicacions, Universitat de Girona, Girona, Spain.ORCID 0000-0001-6896-818X
Josep VehiModeling, Identification and Control Laboratory, Institut d'Informatica i Aplicacions, Universitat de Girona, Girona, Spain.ORCID 0000-0001-6884-9789

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial intelligence methods in combination with the latest technologies, including medical devices, mobile computing, and sensor technologies, have the potential to enable the creation and delivery of better management services to deal with chronic diseases. One of the most lethal and prevalent chronic diseases is diabetes mellitus, which is characterized by dysfunction of glucose homeostasis.

objectiveThe objective of this paper is to review recent efforts to use artificial intelligence techniques to assist in the management of diabetes, along with the associated challenges.

methodsA review of the literature was conducted using PubMed and related bibliographic resources. Analyses of the literature from 2010 to 2018 yielded 1849 pertinent articles, of which we selected 141 for detailed review.

resultsWe propose a functional taxonomy for diabetes management and artificial intelligence. Additionally, a detailed analysis of each subject category was performed using related key outcomes. This approach revealed that the experiments and studies reviewed yielded encouraging results.

conclusionsWe obtained evidence of an acceleration of research activity aimed at developing artificial intelligence-powered tools for prediction and prevention of complications associated with diabetes. Our results indicate that artificial intelligence methods are being progressively established as suitable for use in clinical daily practice, as well as for the self-management of diabetes. Consequently, these methods provide powerful tools for improving patients' quality of life.

Indexed as

Artificial IntelligenceChronic DiseaseDecision Support TechniquesDiabetes MellitusHumansMachine LearningQuality of Lifeartificial intelligenceblood glucosediabetes managementmachine learningmobile computing

Identifiers

PMID29848472
PMCPMC6000484

What Socratic holds

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Registered trials

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.