Introduction: Artificial Intelligence (AI) is revolutionizing medical genetics by enabling rapid analysis of genomic data, predicting disease risks, and personalizing treatment strategies. AI-driven tools like machine learning (ML) and deep learning (DL) are enhancing the interpretation of genetic variants, CRISPR-based gene editing, and pharmacogenomics.
Methods: This study evaluates AI applications in medical genetics through:
Genomic Sequencing Analysis: ML algorithms for variant calling (e.g., DeepVariant).
Disease Risk Prediction: Polygenic risk scores (PRS) using large biobank data.
CRISPR Guide RNA Design: DL models (e.g., DeepCRISPR) for optimal gene editing.
Pharmacogenomics: AI-based drug-gene interaction prediction.
Data Sources: 1000 Genomes Project, UK Biobank, and PubMed literature (2018–2024)
Results: Key Findings
Variant Interpretation:
DeepVariant reduced variant-calling errors by 50% compared to traditional methods (Nature Biotechnology, 2021).
Disease Prediction:
AI models predicted Alzheimer’s disease risk with 85% accuracy using genomic and clinical data (Science, 2023).
CRISPR Efficiency:
DeepCRISPR improved gene-editing success rates by 35% (Cell, 2022).
Pharmacogenomics:
AI is transforming medical genetics by enabling precision diagnostics and personalized therapies. Future work must address ethical concerns and improve diversity in genomic datasets.
AI-matched cancer therapies increased patient survival by 22% (NEJM, 2023).
Conclusion: AI is transforming medical genetics by enabling precision diagnostics and personalized therapies. Future work must address ethical concerns and improve diversity in genomic datasets.
Keywords: Artificial Intelligence , Medical Genetics