ArticleNature medicine2023
Optimized glycemic control of type 2 diabetes with reinforcement learning: a proof-of-concept trial.
Article in Nature medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers, 2 of them syntheses that pooled it.
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
61 citing papers in PubMed, 2 syntheses or guidelines pooled it, 128 citations in OpenAlex.
- Pooled it
- Digital management of diabetes global research trends: a bibliometric study.Frontiers in medicine · 2025Pooled it
- Data, Process, and Data-Driven Representations of Digital Twins in Diabetes: Scoping Review.JMIR diabetes · 2026Review
- Artificial intelligence in orthopaedics should be built with surgeons, not just for them!Journal of experimental orthopaedics · 2026Article
- Shared mechanisms of musculoskeletal dysfunction in type 2 diabetes mellitus: Insights into future therapeutic directions (Review).International journal of molecular medicine · 2026Review
- Algorithm-guided insulin therapy in hospitalized patients: current evidence, implementation challenges, and future perspectives.Reviews in endocrine & metabolic disorders · 2026Review
- Artificial intelligence in geriatric healthcare: a scoping review.BMC geriatrics · 2026Article
- AI-driven personalization of food systems: From precision nutrition to climate-resilient supply chains.NPJ science of food · 2026Review
- A generalist precision medication framework using temporal causal inference based on treatment-free physiological profiles.Nature communications · 2026Article
- A novel intelligent hybrid reinforcement learning framework for autonomous decision making in complex health cognitive systems.Scientific reports · 2026Article
- Ontology-Based Medication Named Entity Recognition Using Pretrained Transformer Models From a Thai Hospital: Model Fine-Tuning and Validation Study.JMIR formative research · 2026Article
- Liver transplant donor-recipient matching with offline reinforcement learning.NPJ digital medicine · 2026Article
- Applying large language model for automated quality scoring of radiology requisitions using a standardized criteria.European radiology · 2026Article
- Review
- Explainable deep learning framework incorporating medical knowledge for insulin titration in diabetes.Communications medicine · 2026Article
- Fine-tuning foundational models to code diagnoses from veterinary health records.PLOS digital health · 2026Article
- Algor-Ethics in Diabetes Care: Mapping the Route.Diabetes/metabolism research and reviews · 2026Review
- Medical AI across Data Regimes to Promote Proactive Health.Health data science · 2026Review
- A reliable and explainable deep learning framework for clinical-grade endocrine disorder risk prediction and decision support.Frontiers in artificial intelligence · 2026Article
- Advancing Precision Oncology Through Modeling of Longitudinal and Multimodal Data.IEEE reviews in biomedical engineering · 2026Review
1 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 6 institutions in 2 countries.
Funding
Abstract
The personalized titration and optimization of insulin regimens for treatment of type 2 diabetes (T2D) are resource-demanding healthcare tasks. Here we propose a model-based reinforcement learning (RL) framework (called RL-DITR), which learns the optimal insulin regimen by analyzing glycemic state rewards through patient model interactions. When evaluated during the development phase for managing hospitalized patients with T2D, RL-DITR achieved superior insulin titration optimization (mean absolute error (MAE) of 1.10 ± 0.03 U) compared to other deep learning models and standard clinical methods. We performed a stepwise clinical validation of the artificial intelligence system from simulation to deployment, demonstrating better performance in glycemic control in inpatients compared to junior and intermediate-level physicians through quantitative (MAE of 1.18 ± 0.09 U) and qualitative metrics from a blinded review. Additionally, we conducted a single-arm, patient-blinded, proof-of-concept feasibility trial in 16 patients with T2D. The primary outcome was difference in mean daily capillary blood glucose during the trial, which decreased from 11.1 (±3.6) to 8.6 (±2.4) mmol L
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What OpenQuestion holds
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.