Vikram Rawat
Independent Researcher
Uttarakhand, India
Abstract
The integration of artificial intelligence (AI) in pharmacovigilance and drug side effect prediction has brought transformative advances in healthcare decision-making. By analyzing large datasets such as clinical trials, electronic health records, and pharmacological databases, AI models can detect adverse drug reactions (ADRs) with increased speed and precision. However, this technological leap raises serious ethical concerns that must be critically examined. Issues around data privacy, algorithmic bias, lack of transparency, patient autonomy, and informed consent dominate the discourse surrounding AI-based prediction systems. Additionally, unequal access to AI tools could deepen health disparities across different populations. This paper investigates the ethical landscape surrounding AI in the context of drug side effect prediction, focusing on literature up to 2015. It provides a comprehensive review of early AI applications in pharmacology, outlines emerging ethical dilemmas, and evaluates frameworks proposed to regulate AI ethics in biomedical applications. A qualitative methodology grounded in case study analysis and ethical frameworks is employed to structure the argument. The findings highlight that while AI holds immense potential for enhancing drug safety, a rigorous ethical foundation must guide its development and deployment. The study concludes with recommendations for ensuring ethical compliance in future AI-driven pharmacological systems.
Keywords
AI Ethics, Drug Side Effects, Adverse Drug Reactions, Pharmacovigilance, Patient Autonomy, Algorithmic Bias, Data Privacy, Biomedical Informatics, Transparency, Predictive Modeling
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