# Assisting the implementation of screening for type 1 diabetes by using artificial intelligence on publicly available data

## TL;DR

This paper discusses how artificial intelligence can improve early screening for type 1 diabetes by analyzing public data to identify at-risk individuals and design personalized monitoring plans.

## Contribution

The paper introduces ASSET, a public/private initiative leveraging AI to advance precision medicine in type 1 diabetes prevention.

## Key findings

- AI can help identify and stratify individuals at risk for type 1 diabetes using longitudinal data.
- AI has the potential to design individualized screening and follow-up plans for early disease detection.
- ASSET aims to address operational feasibility and cost-effectiveness of AI-driven screening programs.

## Abstract

The type 1 diabetes community is coalescing around the benefits and advantages of early screening for disease risk. To be accepted by healthcare providers, regulatory authorities and payers, screening programmes need to show that the testing variables allow accurate risk prediction and that individualised risk-informed monitoring plans are established, as well as operational feasibility, cost-effectiveness and acceptance at population level. Artificial intelligence (AI) has the potential to contribute to solving these issues, starting with the identification and stratification of at-risk individuals. ASSET (AI for Sustainable Prevention of Autoimmunity in the Society; www.asset.healthcare) is a public/private consortium that was established to contribute to research around screening for type 1 diabetes and particularly to how AI can drive the implementation of a precision medicine approach to disease prevention. ASSET will additionally focus on issues pertaining to operational implementation of screening. The authors of this article, researchers and clinicians active in the field of type 1 diabetes, met in an open forum to independently debate key issues around screening for type 1 diabetes and to advise ASSET. The potential use of AI in the analysis of longitudinal data from observational cohort studies to inform the design of improved, more individualised screening programmes was also discussed. A key issue was whether AI would allow the research community and industry to capitalise on large publicly available data repositories to design screening programmes that allow the early detection of individuals at high risk and enable clinical evaluation of preventive therapies. Overall, AI has the potential to revolutionise type 1 diabetes screening, in particular to help identify individuals who are at increased risk of disease and aid in the design of appropriate follow-up plans. We hope that this initiative will stimulate further research on this very timely topic.