Artificial Intelligence in Predicting Adverse Drug Reactions for Personalized Medicine

DOI: https://doi.org/10.63345/ijrmp.v10.i12.1

Simran Kaur

Independent Researcher

Mohali, Punjab, India

Abstract

Adverse drug reactions (ADRs) remain a major challenge in healthcare, impacting patient safety and increasing healthcare costs. Recent advances in artificial intelligence (AI) have enabled more precise prediction of ADRs by harnessing large, heterogeneous datasets and sophisticated machine learning algorithms. This manuscript reviews the evolution of AI in predicting ADRs and its application in personalized medicine. We present a literature review of studies up to 2020, outline a statistical analysis comparing different predictive models, and describe the methodological framework used to integrate AI with clinical decision support systems. Although AI-driven approaches demonstrate improved sensitivity and specificity, challenges such as data quality, model interpretability, and integration into clinical practice still exist. The paper concludes by discussing future directions, scope, and limitations in the quest for safer and more individualized therapy.

Keywords

Artificial Intelligence , Adverse Drug Reactions , Personalized Medicine , Machine Learning , Pharmacovigilance , Predictive Analytics

References

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