Artificial Intelligence in the Diagnosis of Pediatric Rare Diseases: From Real World Data Towards a Personalized Medicine Approach

Background: Artificial intelligence (AI) is increasingly used in the diagnosis of pediatric rare diseases, helping to improve the speed, accuracy, and accessibility of genetic interpretations. This development supports the ongoing shift toward personalized medicine in clinical genetics. Objective: This review explores current applications of AI in pediatric rare disease diagnostics, with an emphasis on real-world data integration and implications for individualized care. Methods: A narrative review was conducted covering AI tools for variant prioritization, phenotype-genotype correlation, large language models (LLMs), and ethical considerations. Literature was identified through PubMed, Scopus, and Web of Science up to July 2025. Results: AI platforms offer promising support for genomic interpretation, especially in structured diagnostic workflows. Tools integrating HPO-based inputs and LLMs enable phenotype matching and support reverse phenotyping. Real-world data enhance AI’s applicability in complex, heterogeneous cases. However, challenges remain regarding data standardization, interpretability, workflow integration, and bias. Conclusion: AI has the potential to support earlier and more personalized diagnostics for children with rare diseases. To fully realize this, multidisciplinary collaboration and careful attention to clinical, technical, and ethical considerations are essential.