Evidence map›Paper›PMID 42758745›Full record

ArticlePLOS digital health2026

Toward fully algorithmic care for type 1 diabetes: The case for AI autonomy, and a recalibrated role for the diabetologist in an ageing type 2 diabetes population.

Mounzer Maaz, Samar Maaz, Islam Maaz, Randa Dardari, Helene Dardari, Dured Dardari

Abstract read
In one paragraph

Article in PLOS digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Mounzer MaazUniversitatea de Medicină și Farmacie "Carol Davila", București, Romania.
Samar MaazFaculty of Medicine, University of Aleppo, Aleppo, Syria.
Islam MaazUniversitatea de Medicină și Farmacie "Carol Davila", București, Romania.
Randa DardariFaculty of Medicine, University of Aleppo, Aleppo, Syria.
Helene DardariPediatric Unit, Montrouge, France.
Dured DardariDiabetology Department, Centre Hospitalier Sud Francilien, Corbeil-Essonnes, France.ORCID https://orcid.org/0000-0002-7172-4300

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence can already automate selected diabetes tasks, but the evidence is uneven. Automated insulin delivery is established in type 1 diabetes; in type 2 diabetes, validated screening and insulin-adjustment tools support a tiered, human-governed model rather than wholesale autonomy.

Identifiers

PMID42758745
PMCPMC13588365

What OpenQuestion holds

Textmetadata
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.