{
  "abstract": "Background\n\nArtificial 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\n\nTo systematically evaluate evidence from randomized controlled trials (RCTs) investigating AI-assisted insulin management systems in individuals with type 1 or type 2 diabetes.Methods\n\nA 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\n\nWe 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\n\nCurrent 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_shown": true,
  "affiliations": [
    "Department of Biochemistry and Pharmacology, School of Medicine, Faculdade de Medicina de Marília (FAMEMA), Marília 17519-030, SP, Brazil.",
    "Graduate Program in Structural and Functional Interactions in Rehabilitation, School of Medicine, Universidade de Marília (UNIMAR), Marília 17525-902, SP, Brazil.",
    "Graduate Program in Medical Sciences, Faculdade de Medicina, Universidade de São Paulo (USP), São Paulo 01246-903, SP, Brazil."
  ],
  "article_type": "review article",
  "arxiv": null,
  "author_total": 9,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Victória Dogani Rodrigues"
    },
    {
      "affs": [
        1
      ],
      "name": "Virgínia Maria Cavallari Strozze Catharin"
    },
    {
      "affs": [
        1
      ],
      "name": "Mauro Audi"
    },
    {
      "affs": [
        1
      ],
      "name": "Eliana de Souza Bastos Mazuqueli Pereira"
    },
    {
      "affs": [
        1
      ],
      "name": "Jesselina Francisco Dos Santos Haber"
    },
    {
      "affs": [
        1
      ],
      "name": "Débora Audi"
    },
    {
      "affs": [
        1
      ],
      "name": "Vitor Cavallari Strozze Catharin"
    },
    {
      "affs": [
        1
      ],
      "name": "Maria Angélica Miglino"
    },
    {
      "affs": [
        2
      ],
      "name": "Lucas Fornari Laurindo",
      "orcid": "0000-0003-3159-0982"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7abc36jtc6mhj.bib",
  "cited_by": 0,
  "doi": "10.3390/diseases14100378",
  "fulltext_url": "https://europepmc.org/articles/PMC13703121",
  "id": "7abc36jtc6mhj",
  "issns": [
    "2079-9721"
  ],
  "landing_url": "https://europepmc.org/article/MED/42930005",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2712|Diabetes Management and Research"
    ]
  ],
  "openalex": "W7221607741",
  "pdf_url": "https://europepmc.org/articles/PMC13703121?pdf=render",
  "pmcid": "PMC13703121",
  "pmid": "42930005",
  "published": "2026-10-13",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/7abc36jtc6mhj.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:22:01Z",
      "native_id": "MED:42930005",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42930005"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2712|Diabetes Management and Research",
      "subfield": "endocrinology-metabolism"
    }
  ],
  "title": "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.",
  "title_text": "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.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:22:01Z",
  "url": "https://research.draconitic.com/paper/7abc36jtc6mhj",
  "venue": "Diseases (Basel, Switzerland)",
  "versions": []
}