ArticleFrontiers in public health2025
Economic implications of artificial intelligence-driven recommended systems in healthcare: a focus on neurological disorders.
Article in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Multimodal deep learning for inflammatory bowel disease: a new frontier in cellular and molecular biomarker discovery to clinical translation.Journal of biological engineering · 2026Review
- Artificial intelligence-based biomarkers for the diagnosis and treatment of neurological conditions: a narrative review.Molecular brain · 2026Review
- Patient-centered gastrointestinal function assessment technologies: a paradigm shift from traditional approaches to non-invasive innovations.Frontiers in physiology · 2026Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.