An Artificial Intelligence-Based Framework for Market Optimization of the Pharmaceutical Industry

Divanshu Mittal

Authors

Divanshu Mittal Masters in Analytics, Dakota State University, Madison, SD, USA Author https://orcid.org/0009-0002-2395-9516

Keywords:

Pharmaceutical pricing, Dynamic pricing, Deep reinforcement learning, Ethical governance, Market optimization

Abstract

Pharmaceutical pricing is increasingly difficult because firms must respond to shifting demand, competitive pressure, public-health trends, and strict regulatory and ethical expectations at the same time. Static and rule-based pricing methods often react too slowly to these changing conditions and struggle to balance profitability with patient access and compliance. This study proposes an AI-based dynamic pricing framework that combines deep reinforcement learning with market dynamics modeling inspired by partial differential equations. The framework learns pricing policies directly from evolving market signals, including inventory conditions, competitive behavior, and public-health indicators represented through an Ornstein–Uhlenbeck process. An ethical governance layer is built into the system through reward penalties and action constraints so that pricing decisions remain aligned with responsible healthcare practice and regulatory requirements. A distributed training architecture is also introduced to support large pharmaceutical portfolios and real-time decision environments. Experimental results across six therapeutic areas show that the proposed approach outperforms conventional pricing strategies, delivering higher profit while preserving strong market share, patient access, and full regulatory compliance. These findings suggest that AI-driven pricing can support more adaptive, evidence-based, and ethically grounded decision-making in pharmaceutical markets. Overall, the study demonstrates that combining reinforcement learning, stochastic market modeling, and built-in governance can produce a scalable and practical framework for sustainable pharmaceutical price optimization.

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