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
In brief
Seven trials found artificial intelligence insulin tools safe, with some improving glucose control
Across seven randomized trials, AI-assisted insulin systems showed no increase in severe hypoglycemia, diabetic ketoacidosis, or serious adverse events; some improved time in range, adherence, or insulin dosing. Overall, results were comparable to conventional care, but small studies, short follow-up, and varied technologies leave their long-term benefits and best role in practice uncertain.
- Journal
- Diseases (Basel, Switzerland) (Q2)
- Published
- 13 October 2026
- Study design
- Systematic review / meta-analysis of RCTs
- Evidence level
- Level 1, High (CEBM 1a)
- Authors
- Victória Dogani Rodrigues, Virgínia Maria Cavallari Strozze Catharin, Mauro Audi, Eliana de Souza Bastos Mazuqueli Pereira, Jesselina Francisco Dos Santos Haber, Débora Audi, et al.
- PMID
- 42930005
- DOI
- 10.3390/diseases14100378
Why clinicians should know about it
- Picked for Health Informatics (top studies of the week, 1 November 2026): Systematic review evaluates clinical decision support, safety, and implementation
Abstract
BACKGROUND: Artificial 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. OBJECTIVE: To systematically evaluate evidence from randomized controlled trials (RCTs) investigating AI-assisted insulin management systems in individuals with type 1 or type 2 diabetes. METHODS: A 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. RESULTS: We 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. CONCLUSIONS: Current 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.
Abstract as published, via PubMed.
For healthcare professionals. The summary is generated by AI from the published abstract, and the evidence level is assigned automatically from the study design on the Oxford CEBM hierarchy. Neither is medical advice. Read the full paper before changing practice.