Ilić Nikola, Sarajlija Adrijan
Clinical Genetics Outpatient Clinic, Mother and Child Health Care Institute of Serbia "Dr. Vukan Cupic", 11070 Belgrade, Serbia.
Department of Pediatrics, Faculty of Medicine, University of Belgrade, 11000 Belgrade, Serbia.
J Pers Med. 2025 Sep 1;15(9):407. doi: 10.3390/jpm15090407.
Artificial intelligence (AI) is increasingly applied in the diagnosis of pediatric rare diseases, enhancing the speed, accuracy, and accessibility of genetic interpretation. These advances support the ongoing shift toward personalized medicine in clinical genetics. Objective: This review examines current applications of AI in pediatric rare disease diagnostics, with a particular focus on real-world data integration and implications for individualized care. A narrative review was conducted covering AI tools for variant prioritization, phenotype-genotype correlations, large language models (LLMs), and ethical considerations. The literature was identified through PubMed, Scopus, and Web of Science up to July 2025, with priority given to studies published in the last seven years. AI platforms provide support for genomic interpretation, particularly within structured diagnostic workflows. Tools integrating Human Phenotype Ontology (HPO)-based inputs and LLMs facilitate phenotype matching and enable reverse phenotyping. The use of real-world data enhances the applicability of AI in complex and heterogeneous clinical scenarios. However, major challenges persist, including data standardization, model interpretability, workflow integration, and algorithmic bias. AI has the potential to advance earlier and more personalized diagnostics for children with rare diseases. Achieving this requires multidisciplinary collaboration and careful attention to clinical, technical, and ethical considerations.
人工智能（AI）在儿科罕见病诊断中的应用日益广泛，提高了基因解读的速度、准确性和可及性。这些进展推动了临床遗传学向个性化医疗的持续转变。目的：本综述探讨了AI在儿科罕见病诊断中的当前应用，特别关注真实世界数据整合及其对个性化医疗的影响。进行了一项叙述性综述，涵盖用于变异优先级排序、表型-基因型相关性、大语言模型（LLMs）以及伦理考量的AI工具。通过PubMed、Scopus和Web of Science检索截至2025年7月的文献，优先选取过去七年发表的研究。AI平台为基因组解读提供支持，特别是在结构化诊断工作流程中。整合基于人类表型本体（HPO）输入和LLMs的工具有助于表型匹配并实现反向表型分析。真实世界数据的使用增强了AI在复杂和异质性临床场景中的适用性。然而，主要挑战依然存在，包括数据标准化、模型可解释性、工作流程整合和算法偏差。AI有潜力推动对罕见病患儿更早且更个性化的诊断。实现这一目标需要多学科合作，并认真关注临床、技术和伦理考量。
