This systematic literature review (SLR) explores the application of Artificial Intelligence (AI) and deep learning methods in emotion recognition and patient-centric healthcare. The objective is to evaluate the effectiveness of these technologies in enhancing patient care through emotional analysis. We examine research studies utilizing AI to advance sentiment analysis, categorize emotions, and predict patient outcomes using textual data from clinical narratives, patient feedback, and online health discussions. The review highlights progress in emotion recognition algorithms, integration of cognitive and emotional analysis techniques, and creation of AI-powered systems for clinical decisionmaking. AI applications have shown significant improvements in personalizing therapy plans, facilitating early detection of mental health disorders, and providing robust support for clinical interventions. Despite advancements, challenges remain in ensuring ethical AI application, maintaining patient confidentiality, and addressing algorithmic biases. Nevertheless, AI's potential to transform healthcare practices is clear, pointing towards more informed, efficient, empathetic, and patient-centered healthcare. This study emphasizes AI's transformative impact on healthcare by providing a thorough understanding of its role in analyzing emotional content in healthcare texts and identifying emerging trends and future research directions. The findings advocate for harmonizing AI's analytical prowess with the human aspects of healthcare, ensuring technological progress supports patients' emotional well-being.

This systematic literature review (SLR) explores the broad application of Artificial Intelligence (AI) in healthcare, with a focus on integrating emotional and cognitive analysis techniques. The review aims to assess the impact of AI technologies on enhancing patient care by analyzing emotional content and supporting patient-centric applications. We examine studies that leverage AI for sentiment analysis, emotion categorization, and predictive modeling using textual data from clinical narratives, patient feedback, and online health discussions. The review identifies key advancements in emotion recognition algorithms, the synergy of cognitive and emotional analysis methods, and the development of AI-powered clinical decision support systems. These applications have significantly improved personalized therapy plans, facilitated early detection of mental health disorders, and enhanced support for clinical interventions. Despite these developments, challenges such as ethical AI implementation, patient confidentiality, and algorithmic biases remain. Nevertheless, AI's potential to transform healthcare practices is evident, promising a future of more informed, efficient, empathetic, and patient-centered healthcare. This study highlights AI's transformative impact on healthcare by providing a comprehensive overview of its role in emotional and cognitive analysis, identifying emerging trends, and advocating for the integration of AI capabilities with the human aspects of healthcare.