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Peer-reviewedCC BY 4.0PLOS Digital Health

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 Maaz1
  • Samar Maaz2
  • Islam Maaz3
  • Randa Dardari4
  • Helene Dardari5
  • Dured Dardari6
6 affiliations
  1. 1Universitatea de Medicină și Farmacie „Carol Davila”, București, Romania
  2. 2Faculty of Medicine, University of Aleppo, Aleppo, Syria
  3. 3Pediatric Unit, Montrouge, France
  4. 4Diabetology Department, Centre Hospitalier Sud Francilien, Corbeil-Essonnes, France
  5. 5Laboratoire de Biologie de l’Exercice pour la Performance et la Santé, Université d’Évry, Institut de Recherche Biomédicale des Armées, Université Paris-Saclay, Évry, France
  6. 6Kremlin-Bicêtre Medical School, Université Paris-Saclay, Le Kremlin-Bicêtre, France
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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.

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Cite

BibTeXRIS
BibTeX
@article{maaz2026toward,
  title = {{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}},
  author = {Mounzer Maaz and Samar Maaz and Islam Maaz and Randa Dardari and Helene Dardari and Dured Dardari},
  journal = {PLOS Digital Health},
  year = {2026},
  month = sep,
  doi = {10.1371/journal.pdig.0001734},
  url = {https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0001734},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - 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
AU  - Mounzer Maaz
AU  - Samar Maaz
AU  - Islam Maaz
AU  - Randa Dardari
AU  - Helene Dardari
AU  - Dured Dardari
PY  - 2026
DA  - 2026/09/18
JO  - PLOS Digital Health
DO  - 10.1371/journal.pdig.0001734
UR  - https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0001734
AB  - 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.
SN  - 2767-3170
ER  -