📄 Abstract
Pharmacogenomics has seen a dramatic change as a result of artificial intelligence (AI), which has made it possible to create customized treatment models based on a patient's genetic composition. Thus, many facets of genetic data might be analyzed by machine and deep learning algorithms of artificial intelligence to precisely predict how patients will react to specific drugs and prescriptions. The synergy also contributes to better drug efficacy, reduced adverse drug effects, and enhanced efficiency in drug development. This is not yet the case, though, as AI in pharmacogenomics has certain drawbacks, such as ethical and data privacy issues and the requirement for sufficient framework validation before it can be used in reality.
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📚 How to Cite:
Shreyam Dubey, Vishwanath Dubey , REVIEW ON AI IN THE FIELD OF PHARMACOGENOMICS , Volume 10 , Issue 10, october 2025, EPRA International Journal of Research & Development (IJRD) ,