Evidence map›Paper›PMID 30256722›Full record

ReviewPopulation health management2019

Transforming Diabetes Care Through Artificial Intelligence: The Future Is Here.

Irene Dankwa-Mullan, Marc Rivo, Marisol Sepulveda, Yoonyoung Park, Jane Snowdon, Kyu Rhee

Registry-linked trialOpen access · hybridAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Population health management, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04132401 (Artificial Intelligence for the Detection of Central Retinal Disease and Non-mydriatic Glaucoma in the Context of Patients With Diabetes Mellitus in Primary Care), which is not on this map. Cited by 63 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
63citing papers in PubMed, 5 pooled it
11.1field-weighted citation impact, top 1% of its field
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.

NCT04132401 completednot on this mapstarted 2021, after this paper: background citation

Artificial Intelligence for the Detection of Central Retinal Disease and Non-mydriatic Glaucoma in the Context of Patients With Diabetes Mellitus in Primary Care: A Prospective Study Comparing the Diagnostic Capacity of an AI Algorithm

TypeobservationalSponsorFundacio d'Investigacio en Atencio Primaria Jordi Gol i GurinaRan2021 to 2023Enrolled902ConditionsDiabetic Retinopathy, Glaucoma, Age-Related Macular Degeneration, Choroidal NevusArmsalgorithm
3 · Its place in the literature

Who cites it

63 citing papers in PubMed, 5 syntheses or guidelines pooled it, 207 citations in OpenAlex.

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  5. Coaching Through Technology: A Systematic Review into Efficacy and Effectiveness for the Ageing Population.International journal of environmental research and public health · 2020
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3 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

6 authors at 3 institutions in 1 country.

Irene Dankwa-Mullan1 IBM Corporation, Watson Health, Bethesda, Maryland.
Marc Rivo2 Population Health Innovations, Inc., Miami Beach, Florida.
Marisol Sepulveda3 Occupational Medicine, University of Iowa, Iowa City, Iowa.
Yoonyoung Park4 IBM Corporation, IBM Research, Cambridge, Massachusetts.
Jane Snowdon5 IBM Corporation, Watson Health, Yorktown Heights, New York.
Kyu Rhee6 IBM Corporation, Watson Health, Cambridge, Massachusetts.
IBM (United States) · USHealth Innovations (United States) · USUniversity of Iowa · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

An estimated 425 million people globally have diabetes, accounting for 12% of the world's health expenditures, and yet 1 in 2 persons remain undiagnosed and untreated. Applications of artificial intelligence (AI) and cognitive computing offer promise in diabetes care. The purpose of this article is to better understand what AI advances may be relevant today to persons with diabetes (PWDs), their clinicians, family, and caregivers. The authors conducted a predefined, online PubMed search of publicly available sources of information from 2009 onward using the search terms "diabetes" and "artificial intelligence." The study included clinically-relevant, high-impact articles, and excluded articles whose purpose was technical in nature. A total of 450 published diabetes and AI articles met the inclusion criteria. The studies represent a diverse and complex set of innovative approaches that aim to transform diabetes care in 4 main areas: automated retinal screening, clinical decision support, predictive population risk stratification, and patient self-management tools. Many of these new AI-powered retinal imaging systems, predictive modeling programs, glucose sensors, insulin pumps, smartphone applications, and other decision-support aids are on the market today with more on the way. AI applications have the potential to transform diabetes care and help millions of PWDs to achieve better blood glucose control, reduce hypoglycemic episodes, and reduce diabetes comorbidities and complications. AI applications offer greater accuracy, efficiency, ease of use, and satisfaction for PWDs, their clinicians, family, and caregivers.

Indexed as

Artificial IntelligenceDecision Support TechniquesDiabetes MellitusForecastingHumansRetinaRisk AssessmentSelf-Managementartificial intelligenceartificial pancreascognitive computingdiabetes careglucose monitoringretinal imaging

Identifiers

PMID30256722
PMCPMC6555175
OpenAlexW2893457521

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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 OpenQuestion graph.