ArticleJournal of diabetes science and technology2018
Artificial Intelligence Methodologies and Their Application to Diabetes.
Article in Journal of diabetes science and technology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 37 papers, 2 of them syntheses that pooled 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.
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.
A Study to Assess the Effect and Safety of Artificial Intelligence Assisted Insulin Titration System on Glucose Control in Type 2 Diabetes Mellitus Patients: A Single-center, Open-labeled, Parallel, Randomized Controlled Trial
Study to Assess the Efficacy and Safety of iNCDSS Assisted Insulin Dosage Titration System on Glucose Control in Type 2 Diabetes Mellitus Patients : a Muticenter, Single-blind, Randomized Controlled Study
Who cites it
37 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Stakeholder Perspectives on the Integration of AI in Diabetes Care: Systematic Review of Qualitative Studies.Journal of medical Internet research · 2026Pooled it
- Accuracy of Machine Learning Classification Models for the Prediction of Type 2 Diabetes Mellitus: A Systematic Survey and Meta-Analysis Approach.International journal of environmental research and public health · 2022Pooled it
- Decision Support in Diabetes Care: The Challenge of Supporting Patients in Their Daily Living Using a Mobile Glucose Predictor.Journal of diabetes science and technology · 2018Trial
- Artificial Intelligence for Blood Glucose Level Prediction in Type 1 Diabetes: Methods, Evaluation, and Emerging Advances.Sensors (Basel, Switzerland) · 2026Review
- Artificial intelligence in mobile health applications: A comprehensive review of its role in diabetes care.World journal of methodology · 2026Review
- Comparative Performance of ChatGPT-5 and Gemini 2.5 on the Official Clinical Diabetology Specialty Examination.Cureus · 2026Article
- Statistical and machine-learning assessment of attitudinal, knowledge, and perceptual factors on diabetes awareness in Kuwait.BMC medical informatics and decision making · 2025Article
- Federated multimodal AI for precision-equitable diabetes care.Frontiers in digital health · 2025Review
- A Combination of Artificial Intelligence with Genetic Algorithms on Static Time-Lapse Images Improves Consistency in Blastocyst Assessment, An Interpretable Tool to Automate Human Embryo Evaluation: A Retrospective Cohort Study.International journal of fertility & sterility · 2024Article
- Artificial intelligence in diabetes management: Advancements, opportunities, and challenges.Cell reports. Medicine · 2023Review
- Effective questionnaire-based prediction models for type 2 diabetes across several ethnicities: a model development and validation study.EClinicalMedicine · 2023Article
- Intelligent Insulin vs. Artificial Intelligence for Type 1 Diabetes: Will the Real Winner Please Stand Up?International journal of molecular sciences · 2023Review
- Effect of Artificial Intelligence-based Health Education Accurately Linking System (AI-HEALS) for Type 2 diabetes self-management: protocol for a mixed-methods study.BMC public health · 2023Article
- Six Digital Health Technologies That Will Transform Diabetes.Journal of diabetes science and technology · 2023Article
- Exploring the progress of artificial intelligence in managing type 2 diabetes mellitus: a comprehensive review of present innovations and anticipated challenges ahead.Frontiers in clinical diabetes and healthcare · 2023Review
- Predicting the Risk of Incident Type 2 Diabetes Mellitus in Chinese Elderly Using Machine Learning Techniques.Journal of personalized medicine · 2022Article
- Continuous Glucose Monitoring in Healthy Adults-Possible Applications in Health Care, Wellness, and Sports.Sensors (Basel, Switzerland) · 2022Review
- Bridging Gaps in Diabetic Nephropathy Care: A Narrative Review Guided by the Lived Experiences of Patient Partners.Canadian journal of kidney health and disease · 2022Review
- Computer Based Diagnosis of Some Chronic Diseases: A Medical Journey of the Last Two Decades.Archives of computational methods in engineering : state of the art reviews · 2022Review
- Multi-step ahead predictive model for blood glucose concentrations of type-1 diabetic patients.Scientific reports · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the past decade diabetes management has been transformed by the addition of continuous glucose monitoring and insulin pump data. More recently, a wide variety of functions and physiologic variables, such as heart rate, hours of sleep, number of steps walked and movement, have been available through wristbands or watches. New data, hydration, geolocation, and barometric pressure, among others, will be incorporated in the future. All these parameters, when analyzed, can be helpful for patients and doctors' decision support. Similar new scenarios have appeared in most medical fields, in such a way that in recent years, there has been an increased interest in the development and application of the methods of artificial intelligence (AI) to decision support and knowledge acquisition. Multidisciplinary research teams integrated by computer engineers and doctors are more and more frequent, mirroring the need of cooperation in this new topic. AI, as a science, can be defined as the ability to make computers do things that would require intelligence if done by humans. Increasingly, diabetes-related journals have been incorporating publications focused on AI tools applied to diabetes. In summary, diabetes management scenarios have suffered a deep transformation that forces diabetologists to incorporate skills from new areas. This recently needed knowledge includes AI tools, which have become part of the diabetes health care. The aim of this article is to explain in an easy and plane way the most used AI methodologies to promote the implication of health care providers-doctors and nurses-in this field.
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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.