Challenges, and Future Directions in Precision Medicine

Abstract

Recent advances in Artificial Intelligence (AI) have profoundly transformed the field of genome editing, particularly through integration with the Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) technology. This review highlights how AI-driven computational models are reshaping guide RNA (gRNA) design, off-target prediction, and editing precision in CRISPR–Cas systems. A PRISMA-informed literature survey was conducted using PubMed, Scopus, EMBASE, and Google Scholar databases to identify studies exploring AI-assisted CRISPR applications in gene therapy and biomedical research. The results demonstrate that deep learning, machine learning, and reinforcement learning approaches significantly enhance prediction accuracy, algorithmic efficiency, and translational potential across genetic diseases such as β-thalassemia, muscular dystrophy, and cancer. Moreover, ethical challenges, algorithmic bias, and data security concerns remain critical barriers to clinical adoption. This review also discusses the emerging landscape of AI-assisted CRISPR research in Iran, emphasizing national progress, infrastructural constraints, and future opportunities. Overall, the convergence of AI and CRISPR technologies promises to advance precision medicine by accelerating the development of personalized, efficient, and ethically responsible genome-editing solutions.