Evidence map›Paper›PMID 37979006›Full record

ReviewDiabetologia2024

Diabetes and artificial intelligence beyond the closed loop: a review of the landscape, promise and challenges.

Scott C Mackenzie, Chris A R Sainsbury, Deborah J Wake

Open access · hybridAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Diabetologia, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed, 1 pooled it
27.6field-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.

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

31 citing papers in PubMed, 1 synthesis or guideline pooled it, 73 citations in OpenAlex.

  1. Pooled it
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  6. Superintelligence: A Crucial Juncture in the Development of Healthcare Sciences and Public Health.International journal of environmental research and public health · 2026
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  7. Review
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  10. Algor-Ethics in Diabetes Care: Mapping the Route.Diabetes/metabolism research and reviews · 2026
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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

3 authors at 3 institutions in 1 country.

Scott C MackenziePopulation Health and Genomics, School of Medicine, University of Dundee, Dundee, UK.ORCID 0000-0001-5823-4334
Chris A R SainsburyInstitute for Applied Health Research, University of Birmingham, Birmingham, UK.ORCID 0000-0002-7984-5721
Deborah J WakeUsher Institute, The University of Edinburgh, Edinburgh, UK. D.Wake@ed.ac.uk.ORCID 0000-0003-4376-6973
NHS Lothian · GBUniversity of Birmingham · GBUniversity of Dundee · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The discourse amongst diabetes specialists and academics regarding technology and artificial intelligence (AI) typically centres around the 10% of people with diabetes who have type 1 diabetes, focusing on glucose sensors, insulin pumps and, increasingly, closed-loop systems. This focus is reflected in conference topics, strategy documents, technology appraisals and funding streams. What is often overlooked is the wider application of data and AI, as demonstrated through published literature and emerging marketplace products, that offers promising avenues for enhanced clinical care, health-service efficiency and cost-effectiveness. This review provides an overview of AI techniques and explores the use and potential of AI and data-driven systems in a broad context, covering all diabetes types, encompassing: (1) patient education and self-management; (2) clinical decision support systems and predictive analytics, including diagnostic support, treatment and screening advice, complications prediction; and (3) the use of multimodal data, such as imaging or genetic data. The review provides a perspective on how data- and AI-driven systems could transform diabetes care in the coming years and how they could be integrated into daily clinical practice. We discuss evidence for benefits and potential harms, and consider existing barriers to scalable adoption, including challenges related to data availability and exchange, health inequality, clinician hesitancy and regulation. Stakeholders, including clinicians, academics, commissioners, policymakers and those with lived experience, must proactively collaborate to realise the potential benefits that AI-supported diabetes care could bring, whilst mitigating risk and navigating the challenges along the way.

Indexed as

Artificial IntelligenceDiabetes Mellitus, Type 1Health Status DisparitiesHumansArtificial intelligenceBarriersBig dataClinical informaticsDecision supportDiabetesFacilitatorsMachine learningPersonalised medicineReviewType 2 diabetes

Identifiers

PMID37979006
PMCPMC10789841
OpenAlexW4388798087

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.