ReviewDiseases (Basel, Switzerland)2026
Clinical Effectiveness, Safety, and Implementation of Artificial Intelligence-Assisted Insulin Management Systems in Type 1 and Type 2 Diabetes: A Systematic Review of Randomized Controlled Trials.
Review in Diseases (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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.
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.
Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI) is increasingly being integrated into insulin management through decision-support systems, adaptive bolus calculators, conversational platforms, and automated insulin delivery (AID) technologies. However, the clinical effectiveness and safety of AI-assisted insulin management remain uncertain.
objectiveTo systematically evaluate evidence from randomized controlled trials (RCTs) investigating AI-assisted insulin management systems in individuals with type 1 or type 2 diabetes.
methodsA systematic literature review was conducted following PRISMA guidelines. PubMed/MEDLINE, Embase, and Cochrane CENTRAL were searched for RCTs evaluating AI-enabled insulin management interventions in insulin-treated individuals with diabetes. Eligible studies compared AI-based systems with standard care, clinician-guided insulin titration, conventional bolus calculators, or other AID algorithms. Outcomes included glycemic control, insulin titration, safety, and implementation-related measures. Owing to substantial heterogeneity, findings were synthesized narratively.
resultsWe included seven RCTs published between 2020 and 2025. Interventions comprised clinical decision-support systems, adaptive bolus calculators, conversational AI platforms, neural-network-based artificial pancreas algorithms, and exercise-aware AID systems. AI-assisted interventions consistently demonstrated favorable safety profiles, with no increase in severe hypoglycemia, diabetic ketoacidosis, or serious adverse events. Some systems, particularly fully automated closed-loop platforms and conversational AI-guided insulin titration, showed improvements in time in range, glucose control, adherence, and insulin optimization. User acceptance and adherence emerged as important determinants of effectiveness.
conclusionsCurrent randomized evidence suggests that AI-assisted insulin management systems are safe, feasible, and capable of achieving glycemic outcomes comparable to conventional care. Although certain technologies demonstrate promising clinical benefits, the evidence base remains limited by small sample sizes, short follow-up durations, and heterogeneity across interventions. Larger, long-term randomized trials are needed to establish comparative effectiveness, optimize human-AI interaction, and support broader clinical implementation.
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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 OpenQuestion graph.