Economic implications of artificial intelligence-driven recommended systems in healthcare
A focus on neurological disorders
Bibliographic Data
| ID | 22089404 |
|---|---|
| Authors | Jing Zhang (0000-0002-6431-9286, Panzhihua University), Shihui Xiang (Advanced Laser Technology (United Kingdom), corresponding author), Li Li (0000-0001-9700-9682, Taizhou Vocational and Technical College) |
| Year | 2025 |
| Volume | 13 |
| Pages | 1588270-1588270 |
| Publication date | 2025-05-15 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Public Health (JOURNAL) |
| Journal identifiers | ISSN: 2296-2565 • E-ISSN: 2296-2565 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fpubh.2025.1588270 |
| PMID | 40443926 |
| OpenAlex | W4410417117 |
| Language | EN |
| References cited | 44 |
Introduction: 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. Results: 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. Discussion: 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
Business · Economics · Health care · Incentive · Management science · Microeconomics · Reinforcement learning · Stakeholder · Artificial Intelligence in Healthcare and Education · Computer Science · Digital Mental Health Interventions · Machine Learning in Healthcare · Artificial Intelligence
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| Highly cited | No |
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