## 3 Results
### Technological Innovation and AI in Enhancing Healthcare
In the field of prevention, AI is being used to identify high-risk patients and tailor preventive interventions, utilizing data from wearable devices, electronic health records, and genomic information [65; 66]. This approach not only optimizes resource allocation but also enhances the effectiveness of preventive care, reducing the overall burden on healthcare systems [67; 68]. The adoption of AI and telemedicine is transforming clinical pathways by allowing patient-physician contact to be based on clinical need rather than set schedules. This shift significantly reduces unnecessary visits and hospitalizations, optimizing healthcare resources and improving the efficiency of care delivery [69; 70]. Studies show that AI facilitates continuous health monitoring, enabling the prediction of acute conditions and the timely initiation of preventive measures. Specifically, integrating machine-learning models into telemedicine has demonstrated high predictive accuracy, contributing to more effective prevention and enhancing both healthcare quality and system efficiency [71]. In managing acute conditions, AI-driven triage systems are facilitating faster and more accurate diagnoses in emergency situations by processing real-time data on symptoms and vital signs [72; 73]. This enables healthcare providers to respond more efficiently, improving patient outcomes. AI is also transforming the management of chronic diseases, enabling continuous patient monitoring and personalized adjustments to therapy. For example, wearable devices equipped with AI can track vital signs in real time, allowing for individualized treatment plans for conditions like Parkinson's disease [74; 75]. This reduces the strain on healthcare professionals while ensuring better adherence to treatment by patients [76]. Rehabilitation, though not as widely discussed, is another area where AI is making significant strides. Personalized rehabilitation programs that adapt to the patient's progress are now possible through AI, and remote monitoring via telemedicine ensures that patients can continue their rehabilitation at home, maintaining continuity in their care while reducing the need for hospital visits [77; 78; 79]. In the evolving healthcare sector, AI and emerging technologies play a critical role in providing personalized, high-quality care. These tools address current challenges and pave the way for a future where healthcare is enhanced by AI integration, underscoring the importance of innovation, collaboration, and adaptation. Studies have highlighted AI's use in customizing care, enabling targeted treatments [48], and its application in early disease diagnosis, opening new avenues for preventive health [49]. The synergy between healthcare professionals and AI systems has been explored to optimize care [50]. Research has also delved into specific areas like AI in diagnosing and treating mental illnesses, presenting innovative solutions to improve patient life quality [53], chronic disease management using AI, Big Data, IoT, and knowledge graphs [54], and the centralized, secure management of health data leveraging cloud and AI capabilities [55]. Analyses of deep-learning methods in various medical conditions provides an innovative perspective on these technologies' potential to enhance diagnosis and treatment [56]. Further research explores chronic disease management using AI, Big Data, IoT, and knowledge graphs for optimized long-term care [57]. The maturity of hospital management models, particularly in light of lessons learned from COVID-19, indicates improvement areas for future models [58; 59; 60; 61; 62; 63; 64]. Advanced data analysis and sophisticated methods like feature engineering in EEG are opening new frontiers in diagnosing and treating mental disorders, demonstrating how technology-medical integration can significantly improve patient life quality [53]. Developments in telemedicine technologies, ranging from handwriting analysis to neonatal sound analysis, are revolutionizing remote care access and quality [54].