ArticleFrontiers in genetics2026
Exploring the strengths and limitations of AI-driven variant prioritization versus manual curation in inborn errors of immunity.
Article in Frontiers in genetics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- The Genetic Landscape of Fibrotic Interstitial Lung Diseases: Clinical Implications and Diagnostic Challenges in Familial Pulmonary Fibrosis.Journal of clinical medicine · 2026Review
- Review
- Clinical description, genetic analysis and characterization of two NLRP12 heterozygous VUS variants.Frontiers in immunology · 2026Article
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Authors and funding
13 authors.
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Abstract
Introduction: Next-generation sequencing (NGS) has transformed the genetic diagnosis of human diseases, yet many patients remain unsolved due to the complexity of variant interpretation. Manual curation of candidate variants is effective but time-consuming and requires specialized expertise. Artificial intelligence (AI)-driven platforms have emerged as scalable tools for variant prioritization, yet their performance compared with manual curation remains insufficiently evaluated. The aim of this study was to evaluate the performance of AI-driven platforms for variant prioritization in a cohort of patients with inborn errors of immunity (IEI) and to compare their strengths and limitations with manual curation. Methods: We analyzed 22 unsolved IEI cases that had previously undergone inconclusive NGS studies. Whole-genome sequencing was performed, and variant prioritization was carried out using two AI-driven platforms -AIMARRVEL and AION (Nostos Genomics)- and by manual curation. Selected variants were classified according to clinical relevance (very high, high, medium, or low), integrating both molecular and phenotypic evidence. Results: Across the cohort, AI platforms efficiently prioritized variants with clear pathogenic features, often reaching the same conclusions as manual curation but in a fraction of the time. One patient (5%) received a conclusive diagnosis ( Discussion: Our results show that AI-driven tools are powerful for detecting clearly pathogenic variants and can markedly accelerate the diagnostic process. However, their strong reliance on curated databases, limited incorporation of phenotypic data, and challenges in handling VUS may reduce their effectiveness. Enhancing phenotype integration, expanding annotations (including non-coding regions), and incorporating up-to-date literature could improve their performance. Ultimately, AI tools should complement expert curation, with future models evolving toward integrative approaches that better capture the complexity of human disorders.
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