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Background: Digitalization is fundamentally reshaping health financing, enabling more efficient management of healthcare resources, improving service delivery, and increasing accessibility. This systematic review explores the intersection of digital tools and financial health systems, examining their transformative potential.Objective: The primary aim of this review is to identify the impact of digitalization on healthcare financial management, highlighting its benefits and addressing the challenges that arise during its implementation.Methods: A systematic review of 10 studies was conducted, focusing on digital health financing and employing PRISMA guidelines to ensure rigorous selection and analysis. The data extraction process identified thematic relevance, methodological rigor, and contextual insights.Results: The findings reveal that digitalization enhances resource allocation, patient accessibility, and administrative efficiency. Technologies such as blockchain and artificial intelligence optimize transparency and predictive financial modeling. However, significant challenges include data security vulnerabilities and the integration of digital tools with legacy systems.Conclusion: Digital technologies present transformative potential for healthcare financing. However, strategic implementation, robust governance, and cross-sector collaboration are critical to overcoming challenges and maximizing the benefits of digitalization. By addressing these needs, digitalization can create sustainable, inclusive, and equitable healthcare systems for the future.

This paper compares various machine learning models in their ability to predict financial trends, with a focus on time-series analysis. We evaluate models such as linear regression, decision trees, support vector machines, and deep learning, measuring their performance based on accuracy, computational cost, and interpretability. Our results reveal that deep learning models offer superior accuracy but are less interpretable, while simpler models, though less accurate, provide better insight into the underlying data. This research provides guidelines for selecting suitable models based on specific financial applications.

This paper introduces a hybrid optimization approach that combines genetic algorithms with gradient descent for effective nonlinear function approximation in highdimensional data. Traditional methods struggle with computational efficiency and accuracy in such complex spaces. By integrating genetic algorithms to provide a global search strategy with gradient descent for finetuning, the proposed method achieves faster convergence and improved accuracy. Simulations and case studies demonstrate its effectiveness in applications like data mining, image recognition, and financial modeling.