ReviewAvicenna journal of medical biotechnology2026
Artificial Intelligence-Assisted CRISPR Gene Editing: Current Advances, Clinical Challenges, and Future Directions in Precision Medicine.
Review in Avicenna journal of medical biotechnology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- CRISPR/Cas system as a novel therapeutic strategy to combat multi-drug-resistant bacteria.Archives of microbiology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent advances in Artificial Intelligence (AI) have profoundly transformed the field of genome editing, particularly through integration with the Clustered Regularly Inter-spaced 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.