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

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 individu...

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Published in:Diabetologia Vol. 67; no. 6; pp. 985 - 994
Main Authors: Teixeira, Pedro F., Battelino, Tadej, Carlsson, Anneli, Gudbjörnsdottir, Soffia, Hannelius, Ulf, von Herrath, Matthias, Knip, Mikael, Korsgren, Olle, Elding Larsson, Helena, Lindqvist, Anton, Ludvigsson, Johnny, Lundgren, Markus, Nowak, Christoph, Pettersson, Paul, Pociot, Flemming, Sundberg, Frida, Åkesson, Karin, Lernmark, Åke, Forsander, Gun
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2024
Springer Nature B.V
Subjects:
Artificial Intelligence
ASSET
Autoimmunity
Children
Clinical Medicine
Diabetes
Diabetes mellitus (insulin dependent)
Diabetes Mellitus, Type 1 - diagnosis
Endocrinology and Diabetes
Endokrinologi och diabetes
For Debate
Health care
Human Physiology
Humans
Internal Medicine
Klinisk medicin
Mass Screening - methods
Medical and Health Sciences
Medicin och hälsovetenskap
Medicine
Medicine & Public Health
Metabolic Diseases
Precision Medicine
Prevention
Risk assessment
Screening
Type 1 diabetes
Prevention
Screening
Precision medicine
Type 1 diabetes
AI
ASSET
Children
Artificial intelligence
ISSN:0012-186X, 1432-0428, 1432-0428
Online Access:Get full text
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Summary: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. Graphical Abstract
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ISSN:0012-186X
1432-0428
1432-0428
DOI:10.1007/s00125-024-06089-5