AI in variant analysis: fast track to genetic diagnoses

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Not reported — no quantitative effect estimates are synthesised

• Primary Outcome: Qualitative synthesis proposing AI as a scalable clinical decision-support framework to bridge the interpretation gap between genomic data generation and clinical actionability, with particular focus on variants of uncertain significance (VUSs) in rare disease patients

• Nnt Or Sensitivity: Not applicable — no diagnostic accuracy metrics, NNT, or hazard ratios are reported; the review does not synthesise performance characteristics of specific AI tools

• Confidence Interval: Not reported

Clinical Application

Implementation feasibility is moderate to high in well-resourced tertiary genomic medicine centres but remains limited in primary and regional care settings due to infrastructure requirements, specialist workforce constraints, and the need for validated AI tools with regulatory approval. The authors' recommendation for intuitive workflow integration and human-in-the-loop design is pragmatically sound but requires substantial institutional investment. Australia has a growing genomic medicine infrastructure through the Australian Genomics Health Alliance and Medicare-funded genomic sequencing for specific rare disease indications. The Human Genetics Society of Australasia (HGSA) provides variant classification guidelines broadly aligned with ACMG standards, making the transition to ACMG v4 discussed in this review directly relevant. The TGA regulates software as a medical device (SaMD), meaning AI-assisted variant interpretation tools would require TGA conformity assessment before clinical deployment. The PBS does not currently fund AI-assisted genomic interpretation as a standalone service. Equity considerations are particularly salient in Australia given the genomic underrepresentation of Aboriginal and Torres Strait Islander peoples in training datasets, a limitation the review acknowledges in general terms but does not address specifically. Patients with suspected rare genetic diseases undergoing whole genome or whole exome sequencing, particularly those receiving inconclusive results due to VUSs; clinical geneticists, genetic counsellors, and genomic medicine specialists managing complex variant interpretation

Abstract

While falling costs have expanded access to genomic sequencing, clinical utility is frequently hindered by the challenge of interpreting complex genetic data. Variant analysis for rare disease patients especially requires significant time and expertise, creating a bottleneck that delays diagnostics. Although advances in genetic variant classification have improved diagnostic precision, they have also increased the identification of variants of uncertain significance (VUSs), widening the interpretation gap between data generation and clinical actionability. The high prevalence of VUSs can lead to false reassurance or psychological distress by misinterpretting inconclusive results. We propose that artificial intelligence (AI) is a critical clinical decision-support tool for bridging this gap, offering a scalable framework to optimize variant interpretation and shorten the diagnostic odyssey. While reclassification ultimately requires biological evidence that AI cannot replace, these tools serve as essential aggregators and prioritizers, especially as guidelines transition toward the upcoming quantitative ACMG v4 framework. We advocate integrating AI throughout the genetic diagnostic workflow-from initial phenotyping to variant prioritization-to facilitate data-driven, personalized treatment. We outline current AI-assisted approaches and discuss anticipated challenges in this pursuit, such as privacy, training data bias and quality, model explainability, and the necessity of a total product life cycle for validation. To address these challenges, we provide recommendations for "human-in-the-loop" design and intuitive workflow integration to ensure AI tools meet the highest standards of precision, reproducibility, and transparency to maximize adoption. By standardizing AI across the variant analysis pipeline, we can fast-track the path to genetic diagnoses, effectively bridging the interpretation gap and enabling rapid delivery of personalized medical interventions.

References

1. 1.Wilk, E. J., Taluri, S., Howton, T. C., Crumley, A. B., Mrug, M., & Lasseigne, B. N. (2026). AI in variant analysis: fast track to genetic diagnoses. Human Genetics. https://doi.org/10.1016/j.gim.2023.100837