AI in variant analysis: fast track to genetic diagnoses.

Area of Science:

• Genomics
• Bioinformatics
• Clinical Decision Support

Background:

• Genomic sequencing costs are decreasing, but interpreting complex genetic data remains a significant clinical challenge.
• The identification of variants of uncertain significance (VUSs) has increased, widening the gap between data generation and clinical actionability.
• This interpretation gap delays diagnoses for rare disease patients, impacting clinical utility and potentially causing patient distress.

Purpose of the Study:

• To propose artificial intelligence (AI) as a critical clinical decision-support tool for optimizing genetic variant interpretation.
• To highlight AI's role in bridging the interpretation gap and shortening the diagnostic odyssey for rare diseases.
• To advocate for the integration of AI across the genetic diagnostic workflow.

Main Methods:

• Review of current AI-assisted approaches in variant analysis.
• Discussion of challenges including data privacy, bias, model explainability, and validation.
• Recommendations for "human-in-the-loop" design and workflow integration.

Main Results:

• AI can serve as an essential aggregator and prioritizer of genetic variants, supporting upcoming quantitative ACMG v4 guidelines.
• AI integration can facilitate data-driven, personalized treatment strategies.
• Standardizing AI across the variant analysis pipeline can accelerate genetic diagnoses.

Conclusions:

• AI is crucial for optimizing variant interpretation and overcoming diagnostic bottlenecks in genomics.
• Addressing challenges like data bias and model explainability is key for AI adoption.
• Integrating AI with human oversight ensures precision, reproducibility, and transparency, enabling faster personalized medicine.