Journal of Shandong University (Health Sciences) ›› 2025, Vol. 63 ›› Issue (10): 117-124.doi: 10.6040/j.issn.1671-7554.0.2025.0094

• Review • Previous Articles    

Multimodal models in the field of kidney disease

WU Qiqi, CHENG Miaomiao, XIAO Xiaoyan   

  1. Department of Nephrology, Qilu Hospital of Shandong University, Jinan 250012, Shandong, China
  • Published:2025-10-17

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Abstract: Kidneys are vital organs for maintaining human health, and diseases associated with their dysfunction are caused by a variety of aetiological factors. Based on the progression of the disease, they are usually categorised as acute kidney injury and chronic kidney disease. It is detected clinically by laboratory tests, imaging and renal tissue biopsy. In the rapid development of artificial intelligence(AI), multimodal model-based AI is an emerging and evolving field of expertise and an efficient way to analyse and mine data, which providing the possibility of individualised and precise diagnosis and treatment of kidney disease. In recent years, multimodal model-based AI techniques have been widely used in a variety of clinical scenarios, including acute kidney injury, chronic kidney disease, hamodialysis, kidney transplantation, and renal tumour, to support precise management and treatment of diseases. In this paper, we provide an overview of multimodal modeling methodologies, applications of multimodal models in the field of renal diseases, current challenges and future perspectives, with the aim of providing references for the further popularization and application of multimodal models in the field of renal diseases, as well as revealing their great advantages and potentials in the application of clinical practice.

Key words: Artificial intelligence, Large language model, Multimodal models, Acute kidney injury, Chronic kidney disease

CLC Number: 

  • R319
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