Artificial intelligence and perspective for rare genetic kidney diseases

Abstract

The integration of big data and artificial intelligence (AI) has revolutionized biomedicine, enhancing our understanding of diseases and health care practices. Although AI has shown remarkable success in some medical fields, its application in nephrology faces challenges because of the complex disease mechanisms and intricate physiology. These obstacles are further compounded in rare diseases, affecting <1 in 2000 people, where data scarcity and clinical complexities create additional challenges for AI in accurate disease characterization and prediction. Rare kidney diseases encompass >150 different conditions, with significant clinical and genetic heterogeneity, posing unique challenges for AI applications. Embracing AI for rare kidney diseases is essential, not only for driving the discovery of novel genes, pathways, and mechanisms relevant to both rare and common diseases, but also for shortening the diagnostic odyssey faced by patients with rare conditions, a goal regarded as the most urgent and transformative need in rare disease care. Recent reviews highlight AI applications in nephrology, focusing on big data sources, decision support systems, imaging data, multi-omics integration, and genotype-phenotype analysis. This review explores the current landscape of AI in rare genetic kidney diseases, examining key challenges and advancements in disease characterization and clinical decision support, with an emphasis on hypothesis generation using unsupervised methods and generative AI. It shows how AI can empower physicians to interpret complex data sets, identify patterns, and generate insights that can lead to improved patient outcomes and innovative medical research for rare genetic kidney conditions.

Keywords: artificial intelligence; clinical decision support; disease characterization; hypothesis generation; rare genetic kidney disease.