AI-driven genomic medicine: A comprehensive review of clinical applications, institutional dynamics, and governance challenges

AI and genomic medicine are coming together increasingly to change modern healthcare, especially when it comes to choosing treatments, making diagnoses, and precision medicine. The development of high-throughput sequencing, molecular biology, and machine learning has made it possible to create and analyze large amounts of genomic data, which opens new possibilities for helping doctors make decisions. This article does not present new experimental or computational results; instead, it offers a critical review of AI-driven genomic medicine as a technological advancement and a sociotechnical transformation in healthcare systems. The review integrates interdisciplinary literature concerning essential application domains, such as precision oncology, rare disease diagnosis, pharmacogenomics, and infectious disease genomics. It further employs established sociological frameworks—Bourdieu’s forms of capital, world-systems theory, and institutional isomorphism—to analyze how institutional resources, professional authority, competition, and global inequalities influence the development and adoption of AI-genomic systems. A qualitative case study from a European precision oncology context illustrates the integration of AI-driven genomic decision-support tools into clinical workflows and their interpretation by healthcare professionals. The analysis delineates significant challenges linked to AI-driven genomic medicine, encompassing algorithmic bias, data governance and privacy issues, inequitable global access, and conflicts between automated systems and clinical judgment. This review brings together technical, institutional, and ethical points of view to make clear what we don't know and what we should do in the future to make sure that AI-driven genomic medicine is used fairly and responsibly in clinical practice. Unlike predominantly technical reviews, this work situates AI-driven genomic medicine within broader sociological and institutional frameworks, offering an integrated perspective on technology, power, and healthcare transformation. • Provides a comprehensive review of AI-driven genomic medicine across clinical and institutional domains. • Synthesizes applications in precision oncology, rare disease diagnosis, and pharmacogenomics. • Examines variant interpretation and multi-omics integration using modern AI techniques. • Analyzes governance, bias, and global inequality in genomic AI deployment. • Integrates sociotechnical and institutional perspectives to frame future research directions.