Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics

Xueqin Xie; Changchun Wu; Ziru Huang; Yuwei Zhou; Jian Huang; Fuying Dao; Dan Yan; Kejun Deng; Hao Lyu; Caiyi Ma; Hao Lin

doi:10.1093/bib/bbag083

journal article Open Access Jan 01, 2026

Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics

Xueqin Xie Changchun Wu

Fuying Dao Dan Yan

Caiyi Ma Hao Lin

Briefings in Bioinformatics Vol. 27 No. 1 · Oxford University Press (OUP)

View at Publisher Save 10.1093/bib/bbag083

Abstract

Abstract
Diabetic complications are a major cause of disability and mortality among patients, and early identification of high-risk individuals is essential for precision prevention and management. In recent years, the rapid advancement of artificial intelligence (AI) has provided transformative tools for risk prediction and clinical decision support in diabetes care. In this narrative review, we systematically surveyed studies published between January 2015 and June 2025 in PubMed, Web of Science, and Scopus that applied AI-based predictive modeling for three major diabetic complications: diabetic retinopathy (DR), diabetic nephropathy (DN), and diabetic cardiovascular disease (CVD). A total of 58 studies were included, encompassing models based on clinical features, molecular omics, medical imaging, and multimodal data integration. Cross-scale and multimodal data fusion has emerged as a promising new paradigm, demonstrating improved predictive performance over single-modality approaches in three major diabetic complications. We also summarize the evolution from traditional machine learning to deep learning and, more recently, to large language models and agent-based systems, comparing their methodological characteristics, strengths, and suitable application scenarios. Finally, we proposed an actionable six-step framework and clinical translation pathway for AI in diabetic complications, outlining key steps from data curation and model development to validation, regulatory compliance, and real-world implementation. Together, these insights provide a roadmap toward developing robust, transparent, and clinically deployable AI systems capable of transforming the prevention and management of diabetic complications.

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Details

Published: Jan 01, 2026
Vol/Issue: 27(1)
License: View

Authors

X

Xueqin Xie

Department of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Life Science and Technology, University of Electronic Science and Technology of China , 2006 Xiyuan Avenue, West Hi-Tech Zone, Chengdu 610054 ,

C

Changchun Wu

Department of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Life Science and Technology, University of Electronic Science and Technology of China , 2006 Xiyuan Avenue, West Hi-Tech Zone, Chengdu 610054 ,

Z

Ziru Huang

Department of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Life Science and Technology, University of Electronic Science and Technology of China , 2006 Xiyuan Avenue, West Hi-Tech Zone, Chengdu 610054 ,

Y

Yuwei Zhou

Department of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Life Science and Technology, University of Electronic Science and Technology of China , 2006 Xiyuan Avenue, West Hi-Tech Zone, Chengdu 610054 ,

J

Jian Huang

Department of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Life Science and Technology, University of Electronic Science and Technology of China , 2006 Xiyuan Avenue, West Hi-Tech Zone, Chengdu 610054 ,

F

Fuying Dao

School of Biological Sciences, Nanyang Technological University , 50 Nanyang Avenue, Singapore 639798 ,

D

Dan Yan

Beijing Friendship Hospital, Capital Medical University , 10 Xitoutiao Road, Youanmenwai Avenue, Fengtai District, Beijing 100069 ,

K

Kejun Deng

Department of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Life Science and Technology, University of Electronic Science and Technology of China , 2006 Xiyuan Avenue, West Hi-Tech Zone, Chengdu 610054 ,

H

Hao Lyu

Department of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Life Science and Technology, University of Electronic Science and Technology of China , 2006 Xiyuan Avenue, West Hi-Tech Zone, Chengdu 610054 ,

C

Caiyi Ma

School of Computer Science and Technology, Aba Teachers College , Shuimo Town, Wenchuan 623002 ,

H

Hao Lin

Department of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Life Science and Technology, University of Electronic Science and Technology of China , 2006 Xiyuan Avenue, West Hi-Tech Zone, Chengdu 610054 ,

Funding

National Natural Science Foundation of China Award: 82130112

Fundamental Research Funds for the Central Universities Award: ZYGX2024Z011

China Postdoctoral Science Foundation Award: 2023TQ0047

Science and Technology Department of Sichuan Province Award: 2025ZNSFSC1465

Cite This Article

Xueqin Xie, Changchun Wu, Ziru Huang, et al. (2026). Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics. Briefings in Bioinformatics, 27(1). https://doi.org/10.1093/bib/bbag083

Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics

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