Affective Intelligent Systems in Healthcare: A Systematic Review

Objectives: To investigate the current state of affective computing in healthcare, focusing on its application contexts, algorithmic trends, and the technical–ethical duality involving data privacy and security. Methods and Results: A systematic review was conducted in two phases (2013–2025) following PRISMA guidelines. A total of 170 peer-reviewed articles were selected from PubMed, IEEE Xplore, Scopus, and Web of Science based on predefined inclusion and exclusion criteria, with the sample restricted to full-text studies in English addressing affective computing in healthcare. No formal risk-of-bias tool was applied due to the computational nature of the studies, and the findings were synthesized descriptively. Discussion: The findings reveal a clear shift from classical machine learning (e.g., SVM, k-NN) toward deep learning and hybrid architectures such as CNN–LSTM and attention-based models for processing complex physiological signals. Recent years have shown a growing interest in multimodal data fusion and privacy-preserving mechanisms such as homomorphic encryption. Evidence remains limited by methodological heterogeneity and inconsistent reporting across studies. A significant gap persists in regulatory compliance, as 57% of recent publications do not adequately address data security or ethical risks associated with sensitive biometric footprints. Conclusions: Although affective computing has reached a certain level of technical maturity, future research must prioritize lightweight, secure, and privacy-by-design architectures to enable ethically aligned and trustworthy deployment in real-world healthcare scenarios.