AI in Predicting Adverse Drug Reactions: Enhancing Patient Safety

EasyChair Preprint 14728

Abstract

Adverse Drug Reactions (ADRs) represent a significant challenge in healthcare, impacting patient safety and escalating healthcare costs. Traditional methods for identifying ADRs often fall short due to their reactive nature and reliance on post-market surveillance, which can delay the detection of potential risks. Artificial Intelligence (AI) offers transformative potential in predicting and preventing ADRs through proactive, data-driven approaches. This paper explores the role of AI in revolutionizing ADR prediction and enhancing patient safety. By leveraging advanced machine learning algorithms and predictive models, AI can analyze vast datasets—including electronic health records, genetic information, and drug interaction data—to identify patterns and predict individual susceptibility to ADRs. AI-driven tools enable early detection of risks, personalized medication plans, and real-time monitoring, significantly improving patient outcomes and reducing the incidence of ADRs. The paper discusses successful case studies of AI applications in drug safety, highlighting their impact on healthcare efficiency and cost reduction. However, challenges such as data quality, ethical considerations, and integration with existing systems remain. Future directions involve advancing AI technologies and fostering collaboration between stakeholders to optimize ADR prediction and prevention. This study underscores the potential of AI to enhance pharmacovigilance, improve patient safety, and contribute to a more proactive and cost-effective approach to medication management.

Keyphrases: Drug-Patient Interactions, data analysis, drug-drug interactions, machine learning, patient safety, real-time monitoring

@booklet{EasyChair:14728,
  author    = {Joseph Oluwaseyi},
  title     = {AI in Predicting Adverse Drug Reactions: Enhancing Patient Safety},
  howpublished = {EasyChair Preprint 14728},
  year      = {EasyChair, 2024}}