The Convergence of Artificial Intelligence and CRISPR-Cas9: A Review of Innovations, Challenges, and Future Vistas in Genome Editing

Abstract

CRISPR-Cas9 has become an effective genome-editing methodology, but the practical deployment of this approach is limited due to challenges such as unintended off-target effects, inconsistent editing efficiency, and lower predictability in complex biological systems. Simultaneously, the development of artificial intelligence (AI) has made it possible to model genomic processes based on data and help us to increase the accuracy and reliability of CRISPR-based treatment. This review is a critical assessment of recent applications of AI combined with CRISPR-Cas9, and specifically guides RNA optimisation, off-target prediction, data enhancement methods, and identification of synthetic lethal interactions applicable to cancer therapy. Instead of a chronological summary of existing methods, the paper includes a comparative analysis of AI-CRISPR-based techniques, along with methodologies, strengths, limitations, and readiness to translate into clinical practice. To conclude, a structured comparative table is highlighted to outline the unresolved gaps that should be bridged in order to translate clinical knowledge to practice. Lastly, recent issues, ethical aspects, and future research are addressed in the direction of responsible AI-driven genome editing.