Artificial Intelligence and Genomic Data Analysis: New Frontiers in Precision Medicine

Area of Science:

• Genomic Medicine
• Artificial Intelligence
• Computational Biology

Background:

• Next-generation sequencing generates vast genomic data, posing challenges for clinical translation.
• Artificial intelligence (AI) offers solutions across the genomic medicine pipeline, from variant detection to treatment response modeling.

Purpose of the Study:

• To synthesize contemporary AI applications in genomic medicine from a clinical perspective.
• To discuss computational paradigms and factors influencing AI model robustness and clinical utility.

Main Methods:

• Review of current AI applications in genomic medicine.
• Analysis of machine learning, deep learning, multimodal AI, and foundation models.
• Examination of case studies in rare genetic disorders, cardiovascular genetics, and precision oncology.

Main Results:

• AI enhances variant detection, risk prediction, disease subtyping, and biomarker discovery.
• Model robustness depends on dataset quality, ancestry representation, and external validation.
• Common failures include overfitting, limited transportability, and inadequate interpretability.

Conclusions:

• Successful clinical translation of genomic AI requires methodological innovation, rigorous validation, and robust governance.
• Addressing ethical and regulatory challenges is crucial for equitable implementation.
• Sustained expert oversight is essential for real-world genomic AI systems.