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Raphael Fraser, Rebekah J. Walker, Jennifer A. Campbell | npj Digital Medicine | (2025)
Primary Link Pubmed DOI Openalex Full text (OA)

Abstract

Artificial intelligence and wearable technology are increasingly used in healthcare and hold significant potential for improving the management of diabetes. Wearable devices enable continuous monitoring and real-time data collection, supporting AI-driven personalized interventions. This systematic review evaluated peer-reviewed studies that examined the integration of AI and wearable technology in diabetes management, with a focus on clinical and self-management outcomes. Sixty studies were included following a review of over 5000 records. AI models paired with wearable devices showed promise in glycemic monitoring, adaptive insulin management, and predicting diabetes-related events. Continuous glucose monitors and other wearables also enhanced self-management and informed clinical decision-making. However, key challenges persist, including limited demographic diversity, variable data quality, a lack of standardized benchmarks for evaluating AI performance, and limited interpretability of complex models. Future research should prioritize improving model transparency, addressing demographic disparities, and establishing clear benchmarks to support equitable and effective implementation in diabetes care.

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Plain English Takeaway

Smart devices and computer programs are starting to help people with diabetes track their health and manage their condition better, but more work is needed to make sure these tools work well for everyone.

Study Aim

The main goal of this paper is to systematically review and assess research on how artificial intelligence (AI) and wearable technology, like continuous glucose monitors and smartwatches, are being used together to help manage type 2 diabetes (T2D) and prediabetes. The authors aim to highlight both the benefits and challenges of these technologies, focusing on how they affect clinical outcomes and self-management for people with diabetes. Simply put: The study wants to find out how well smart devices and AI work together to help people manage diabetes.

Study Design

This research is a systematic review, meaning the authors searched several large databases for studies published between 2014 and 2024. They included only peer-reviewed studies that used AI models with data from wearable devices for diabetes management. Out of over 5,000 records, 60 studies met the strict criteria. The review looked at study design, participant demographics, types of wearables, AI methods, and how well the models worked. Most studies focused on adults with type 2 diabetes, used continuous glucose monitors, and applied advanced AI techniques like deep learning. Simply put: The authors carefully collected and compared many studies about using smart devices and AI for diabetes care.

Findings

The review shows that combining AI with wearable devices can help predict blood sugar changes, guide insulin use, and spot diabetes-related events in real time. These tools can support both doctors and patients in making better decisions and managing diabetes more personally. However, the authors found big challenges: most studies had small or non-diverse groups, data quality varied, and many AI models were hard for doctors to understand. There is a need for more research with larger, more diverse groups, better data standards, and clearer ways to explain how AI makes decisions. The authors recommend focusing on fairness, transparency, and practical use to make sure these tools help everyone with diabetes. Simply put: Smart devices and AI can help people with diabetes, but they need to be tested more fairly and explained more clearly before everyone can benefit.

Referenced In

Mercedes C.
a month ago
AI Papers and Discussion

🤖 AI Wearables for Managing Diabetes ~

In this paper - Fraser et al., 2025 - Researchers reviewed 60 studies on AI-powered wearables - continuous glucose monitors (CGMs), for Type 2 diabetes care. The tech is advancing fast—but major gaps remain before it reaches your doctor's office.

What's New:

Smarter Glucose Prediction: Deep learning models,particularly LSTMs and transformers, can predict blood sugar swings 30-120 minutes ahead of time by spotting patterns humans miss. One model achieved R² = 0.989 accuracy, essentially forecasting your glucose curve in real-time.

AI That Acts, Not Just Predicts: We're moving from forecasting to actual intervention. Reinforcement learning models, like Warren et al., 2024 now can suggest insulin doses autonomously, with results showing HbA1c drops by 1.3%.

Non-Invasive Hacks: Researchers are ditching finger pricks entirely—using smartphone photoplethysmography (PPG), ECG signals, even tear fluid infrared sensors to estimate glucose. One PPG-based deep learning model reached 90.6% accuracy for diabetes detection.

The AI Toolkit Deep-Dive:

>> LSTMs & GRUs (45% of studies): These recurrent neural networks are the workhorses—they process CGM time-series data sequentially, remembering past glucose values to predict future trends.

>> Transformers: They use "attention mechanisms" to weigh which past data points matter most — handling longer prediction windows across diverse populations better than LSTMs.

>> Temporal Fusion Transformers: Combine static data (age, BMI) with time-varying data (glucose, heart rate) in one model. Emerging but promising for personalized predictions.

>> Multi-Agent Reinforcement Learning (MARL): Multiple AI agents compete to identify which patient features (lab values, meds, demographics) most predict adverse glycemic events in hospitals. One study achieved 92.8% precision for hypoglycemia detection.

>> XGBoost & Random Forests: Traditional machine learning still dominates when doctors need interpretability. XGBoost ranked features by importance (SHAP values), showing clinicians why the AI flagged a patient as high-risk.

>> Deep-Ensemble Learning: Stacks multiple neural networks together—CNNs for pattern recognition, BiLSTMs for sequence memory, meta-learners for combining outputs.

Despite the great advances made, there are still some hard truths to tackle:

  • 60% use "black box" models that doctors can't explain - the pattern is unknown.

  • Only 7% report race/ethnicity data.

  • Most studies are tiny (median 150 people).

  • No long-term follow-ups exist - What would we see in 1 year's time?

The authors close off the review --- The tech works in pilots but we need diverse data, transparent models, and real-world validation before this becomes standard care.

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