## 5 Conclusions  
In addressing the research question of \"The role of artificial intelligence (AI) in healthcare management\", a complex picture emerges where AI is positioned as a transformative force with significant impacts, inherent limitations, and promising prospects. AI is revolutionizing healthcare by offering innovative tools for diagnostics, personalized treatment, crisis management, and resource optimization. Through analyzing large data volumes and learning from complex patterns, AI enhances care quality, diagnostic accuracy, and operational efficiency, contributing to more informed and outcome-based healthcare management. The impacts of AI are tangible and multifaceted. In health crises, like the COVID-19 pandemic, AI has proven to be a vital tool for rapid data analysis, virus spread forecasting, and early diagnosis assistance. Moreover, AI-based intelligent systems are enhancing healthcare resource management, optimizing hospital bed allocation, staff distribution, and medication stock management. These systems not only make healthcare more responsive but also contribute to more strategic and forward-thinking planning. The widespread acceptance of internet-connected mobile devices, like smartphones and wearables, has positioned AI and telemedicine as ideal solutions for enhancing healthcare in a practical and cost-effective way. This shift aligns perfectly with the growing demand for faster, more accessible, and personalized care, while also optimizing the effectiveness and efficiency of healthcare services. AI has proven effective in identifying high-risk patients, managing acute conditions through advanced triage systems, and enabling personalized monitoring of chronic diseases via wearable devices. Additionally, AI-based telemedicine supports home rehabilitation by ensuring continuity of care and reducing the need for hospital visits. These advancements reinforce AI's role as a crucial tool for real-time healthcare management, fostering a more personalized and responsive approach to patient care. AI and telemedicine are reshaping clinical pathways by replacing scheduled appointments with interactions based on real clinical needs. This flexible model, aligned with the growing use of digital health devices, reduces unnecessary interventions and optimizesresources and time. Additionally, AI's predictive capacity provides a preventive edge, allowing timely interventions that ensure more targeted and effective care. However, integrating AI into healthcare is not without limits and challenges. Issues related to privacy, data security, and the risk of algorithmic bias require constant attention. The \"black box\" nature of many AI systems raises concerns about transparency and explainability in AI-based medical decisions. Additionally, resistance to change within healthcare institutions and the need for proper medical staff training represent further obstacles to widespread AI adoption. Our study has both theoretical and practical objectives, supported by concrete recommendations. Theoretically, it clarifies AI's role in healthcare management through a combined approach of systematic review and predictive algorithms, providing an overview of its impact on processes, patient care, and emergency response. Practically, it examines how AI enhances efficiency, reduces costs, and optimizes resources at operational, strategic, and emergency response levels, with specific focus on the period 2019-2023 and the challenges posed by the pandemic. We provide recommendations to support responsible AI adoption in healthcare, emphasizing a collaborative approach that includes all stakeholders, robust data privacy and security, and continuous training for healthcare professionals. Finally, we highlight the importance of ongoing research to improve AI capabilities and address emerging ethical challenges.