This paper explores the use of Artificial Intelligence (AI)-driven voice biomarkers for early detection and continuous monitoring of schizophrenia. Traditional diagnostic approaches are often subjective, timeconsuming, and limited in scalability. In contrast, AI offers automated, non-invasive alternatives that can detect vocal indicators linked to core symptoms. A systematic literature review was conducted using the PRISMA framework. Thereafter, the selected studies were evaluated through a systems approach guided by eight Human Factors and Ergonomics (HF/E) principles. The review revealed promising but heterogeneous model performance across classification, prediction, and correlation-based tasks using features extracted from speech across multiple languages and contexts. However, limitations related to generalizability, explainability, workload, and ethical issues were noted. While AI-based voice biomarkers show high potential for enhancing psychiatric diagnosis and monitoring, most solutions remain at the proof-of-concept stage. A systems approach is essential to bridge the gap between technological innovation and real-world clinical application, ensuring usability, safety, and scalability.