AI-Driven Drug Price Optimization: A Comparative Study of Market Trends

DOI: https://doi.org/10.63345/ijrmp.org.v8.i12.1

Aryan Verma

Independent Researcher

Rajasthan, India

Abstract

This manuscript investigates the emerging intersection of artificial intelligence (AI) and drug price optimization, focusing on how advanced computational techniques can refine pricing strategies in the pharmaceutical industry. The study compares historical market trends with current optimization practices and evaluates the role of AI in predicting market shifts, consumer behavior, and competitive pricing. Using a mixed-methods approach, we combine quantitative statistical analysis with qualitative survey data from industry experts and practitioners. The statistical component, based on a representative sample of drug pricing models from leading companies, illustrates significant shifts in pricing dynamics when AI is implemented. Meanwhile, the survey explores perceptions regarding the benefits and challenges of deploying AI for price optimization. Our findings suggest that AI-driven strategies not only enhance decision-making through predictive analytics but also offer scalable solutions for managing complex market variables. Despite these promising insights, barriers such as data quality, ethical concerns, and regulatory hurdles persist. The implications of these findings underscore the need for further research into refining AI models, improving data integration, and developing industry guidelines to maximize the benefits of AI-driven optimization. Overall, this study provides a comprehensive baseline for both academic research and practical application, paving the way for future innovations in pharmaceutical pricing strategies.

Keywords

Artificial Intelligence, Drug Price Optimization, Market Trends, Comparative Study, Statistical Analysis

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