The rapid introduction of Natural Language Processing (NLP)-powered virtual assistants into healthcare has moved faster than the development of supporting governance and security controls, with extensive regions remaining unexplored in privacy, security, and ethics. Although promising, comprehensive consideration of threats to data confidentiality, integrity, and availability and of ethical issues around AI autonomy within clinical settings remains nascent. This literature systematic review therefore attempted to synthesize academic knowledge to date by establishing the primary security issues in NLP- harbored health systems, examining cryptographic solutions to their protection, outlining a regulatory conformity framework, and evaluating the sufficiency of state-of-the-art privacy-enhancing technologies. Using the PRISMA-2020 guidelines, a systematic search of the Scopus and ScienceDirect databases for articles from 2016 to 2025 yielded 4,780 records. Out of a stringent filtering procedure, 16 relevant research papers were shortlisted for further examination. The results show a bountiful threat environment comprising user-centric challenges like “AI hesitancy,” AI-scarred vulnerabilities like data poisoning, and significant hindrances in compliance with regulations. The findings expose the extreme trade-off between privacy robustness and artificial intelligence model performance, but they also underline the need for multi-layer protection applying cryptographic techniques such as homomorphic encryption and federated learning. The study also underlines the need to replace traditional anonymizing methods with safer ones since they are insufficient to preclude re-identification efforts and increasing trust, openness, and regulatory compliance, this evaluation underlines the importance of applying a comprehensive, socio-technical strategy to guarantee the safety of NLP-based healthcare assistants and so support their moral and safe deployment.