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ORIGINAL RESEARCH article
Front. Public Health
Sec. Health Economics
Volume 13 - 2025 | doi: 10.3389/fpubh.2025.1588270
Economic Implications of Artificial Intelligence-Driven Recommended Systems in Healthcare: A Focus on Neurological Disorders
Provisionally accepted- Taizhou Vocational College of Science and Technology, Taizhou, China
The rapid advancement of Artificial Intelligence (AI)-driven recommendation systems in healthcare presents significant economic implications, particularly in the context of neurological disorders. These systems offer opportunities to enhance diagnostic accuracy, optimize resource allocation, and improve patient outcomes. However, conventional economic models fail to address the dynamic complexities of AI integration in healthcare, including market inefficiencies and stakeholder behaviors. Methods: To bridge this gap, we propose a Dynamic Equilibrium Model for Health Economics (DEHE), incorporating reinforcement learning and stochastic optimization. This model captures uncertainty in healthcare decision-making and includes dynamic pricing, behavioral incentives, and adaptive insurance premium mechanisms. Findings: Our experimental results demonstrate that DEHE improves economic efficiency by optimizing AI-driven recommendations while balancing healthcare cost and accessibility. Through multi-agent simulations, the model shows strong real-world applicability and stability. It effectively addresses asymmetric information, moral hazard, and market dynamics. Conclusion: This study offers a novel economic framework for integrating AI-driven systems in neurological healthcare.We recommend the adoption of adaptive policy mechanisms and stakeholder-specific incentives to enhance cost-effectiveness and equitable access. These insights contribute to the development of more sustainable and inclusive AI-based healthcare policies.
Keywords: AI-Driven Recommendation Systems, healthcare economics, neurological disorders, Dynamic equilibrium model, machine learning, Cost-Effectiveness, policy optimization
Received: 07 Mar 2025; Accepted: 03 Apr 2025.
Copyright: © 2025 Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Li Li, Taizhou Vocational College of Science and Technology, Taizhou, China
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