Kim, Min-gyeong Go, Min-ji Kang, Seung-Hun Jeong, Soo-hwan Lim, Kayeong 2025-08-26T02:00:51Z 2025-08-26T02:00:51Z 2025-08-20 2025-07 1226-3613 https://pubs.kist.re.kr/handle/201004/153032 Genome engineering has made remarkable strides, evolving from DNA-binding proteins such as zinc fingers and transcription activator-like effectors to CRISPR-Cas systems. CRISPR technology has revolutionized the field through its simplicity and ability to target specific genome regions via guide RNA and Cas proteins. Progress in CRISPR tools-CRISPR nucleases, base editors and prime editors-has expanded the toolkit to induce targeted insertions or deletions, nucleotide conversions and a wider array of genetic alterations. Nevertheless, variations in editing outcomes across cell types and unintended off-target effects still present substantial hurdles. Artificial intelligence (AI), which has seen rapid advances, provides high-level solutions to these problems. By leveraging large datasets from diverse experiments, AI enhances guide RNA design, predicts off-target activities and improves editing efficiency. In addition, AI aids in discovering and designing novel CRISPR systems beyond natural limitations. These developments provide new modalities essential for the innovation of personalized therapies and help to ensure efficiency, precision and safety. Here we discuss the transformative role of AI in advancing CRISPR technology. We highlight how AI contributes to refining nuclease-based editing, base editing and prime editing. Integrating AI with CRISPR technology enhances existing tools and opens doors to next-generation medicine for gene therapy. English Springer Nature Revolutionizing CRISPR technology with artificial intelligence Article 10.1038/s12276-025-01462-9 1 Experimental & Molecular Medicine, v.57, no.7, pp.1419 - 1431 Experimental & Molecular Medicine 57 7 1419 1431 Y scie scopus kci ART003231049 001541190000001 2-s2.0-105012277064 Biochemistry & Molecular Biology Medicine, Research & Experimental Biochemistry & Molecular Biology Research & Experimental Medicine Review BASE EDITING OUTCOMES GENOMIC DNA RNA PREDICTION NUCLEASES LIBRARY DESIGN GUIDE ENDONUCLEASE DETERMINANTS