# Federated multimodal AI for precision-equitable diabetes care

## TL;DR

This paper explores how AI can help reduce diabetes care disparities by using multimodal data and addressing challenges like bias and access.

## Contribution

The paper introduces federated multimodal AI as a novel approach to achieve precision-equitable diabetes care.

## Key findings

- AI models integrating multimodal data improve early detection and risk prediction for diabetes.
- AI systems can screen for complications like diabetic retinopathy with high accuracy.
- Challenges include data heterogeneity, algorithmic bias, and the digital divide.

## Abstract

Type 2 diabetes mellitus (T2DM) constitutes a rapidly expanding global epidemic whose societal burden is amplified by deep-rooted health inequities. Socio-economic disadvantage, minority ethnicity, low health literacy, and limited access to nutritious food or timely care disproportionately expose under-insured populations to earlier onset, poorer glycaemic control, and higher rates of cardiovascular, renal, and neurocognitive complications. Artificial intelligence (AI) is emerging as a transformative counterforce, capable of mitigating these disparities across the entire care continuum. Early detection and risk prediction have progressed from static clinical scores to dynamic machine-learning (ML) models that integrate multimodal data—electronic health records, genomics, socio-environmental variables, and wearable-derived behavioural signatures—to yield earlier and more accurate identification of high-risk individuals. Complication surveillance is being revolutionised by AI systems that screen for diabetic retinopathy with near-specialist accuracy, forecast renal function decline, and detect pre-ulcerative foot lesions through image-based deep learning, enabling timely, targeted interventions. Convergence with continuous glucose monitoring (CGM) and wearable technologies supports real-time, AI-driven glycaemic forecasting and decision support, while telemedicine platforms extend these benefits to remote or resource-constrained settings. Nevertheless, widespread implementation faces challenges of data heterogeneity, algorithmic bias against minority groups, privacy risks, and the digital divide that could paradoxically widen inequities if left unaddressed. Future directions centre on multimodal large language models, digital-twin simulations for personalised policy testing, and human-in-the-loop governance frameworks that embed ethical oversight, trauma-informed care, and community co-design. Realising AI's societal promise demands coordinated action across patients, clinicians, technologists, and policymakers to ensure solutions are not only clinically effective but also equitable, culturally attuned, and economically sustainable.