Document Type

Article

Publication Date

2-2026

Identifier

PMCID: PMC12921402

Abstract

Helping patients self-managing diseases like type 1 diabetes (T1D) requires informatics tools delivering real-time predictions with explainable, actionable guidance. However, many healthcare AI solutions lack actionable recommendations and user-friendly explanations, limiting clinical impacts. We introduce APEA, a pediatric T1D self-management Ambient-AI assistance tool, integrating glucose multi-trajectory-scenarios Prediction, interactive, context-aware large language model Explanations, and just-in-time adaptive intervention policy optimization for Actionable real-time suggestions through reinforcement learning. Using T1DEXIP dataset (262 pediatric T1D patients, multi-center), our results showed improved glucose control outcomes: 45% over human management, 69% over infusion-pump management. Although constrained by small sample size and severe class imbalance, APEA addresses healthcare AI implementation gaps by bridging what might happen, what can be done about it, and why it makes clinical sense. APEA offers a transferable framework for other chronic conditions that demand continuous, personalized, just-in-time adaptive interventions.

Journal Title

AMIA ... Annual Symposium proceedings [electronic resource] / AMIA Symposium. AMIA Symposium

Volume

2025

First Page

257

Last Page

266

MeSH Keywords

Diabetes Mellitus, Type 1; Humans; Self-Management; Artificial Intelligence; Child; Self Care

PubMed ID

41726422

Keywords

Diabetes Mellitus, Type 1; Self-Management; Artificial Intelligence; Self Care

Comments

This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose.

Publisher's Link: https://knowledge.amia.org/change

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