| The Convergence of AI, CRISPR, and Molecular Genetics in Cancer Diagnosis and Therapeutics |
| Paper ID : 1183-IGA |
| Authors |
|
Sahar Saki * Department of Biology, Faculty of converging Sciences and Technologies, Science and Research Branch, Islamic Azad University, Tehran, Iran |
| Abstract |
| Background and Aim: Cancer is a major global health issue, with AI, CRISPR, and molecular genetics offering promising advances in diagnosis and treatment. This review explores how these technologies converge to improve diagnostics, personalize treatments, and predict responses, while addressing clinical challenges and opportunities. Materials and Methods: This review article was conducted using databases such as PubMed, Scopus, and Google Scholar to identify relevant published articles. Relevant articles from the last 10 years that addressed the use of artificial intelligence, CRISPR, and molecular genetics in cancer diagnosis and treatment were selected. Data were extracted on the applications of the technology, clinical outcomes, and challenges. A qualitative and comparative analysis of the studies was conducted to assess the effectiveness of these technologies when used together. Results: The use of artificial intelligence, CRISPR, and molecular genetics in cancer diagnosis and treatment has resulted in significant changes. These technologies have improved diagnostic accuracy, personalized treatments, and treatment response predictions. AI analyzes medical data, CRISPR edits genes, and molecular genetics identifies cancer-causing mutations and aids in developing new treatments. Conclusion: The convergence of AI, CRISPR, and molecular genetics is transforming cancer diagnosis and treatment through increased precision, targeted gene editing, and improved tumor profiling. Despite these advances, challenges such as ethical considerations, off-target effects, and integration into the clinic remain. Future research should focus on optimizing this synergy to advance personalized medicine and improve cancer treatment. |
| Keywords |
| Artificial Intelligence, CRISPR-Cas9, Cancer Diagnosis, Molecular Genetics |
| Status: Accepted |
