Unseen Risks of Clinical Speech-to-Text Systems: Transparency, Privacy, and Reliability Challenges in AI-Driven Documentation

AI-driven speech-to-text (STT) documentation systems are increasingly adopted in clinical settings to reduce documentation burden and improve workflow efficiency. However, adoption has outpaced systematic evaluation of socio-technical risks related to transparency, reliability, patient autonomy, and organizational accountability. This study develops a socio-technical framework for identifying and governing risks associated with clinical STT systems. We synthesize interdisciplinary evidence from automatic speech recognition research, clinical workflow and human factors studies, ethical guidance on consent and autonomy, and regulatory and organizational sources. Using a structured narrative synthesis, literature was iteratively reviewed and thematically analyzed to identify recurring socio-technical risk mechanisms and inform a layered conceptual framework. Findings show that clinical STT systems operate within tightly coupled socio-technical environments where model performance, audio conditions, clinician oversight, patient understanding, workflow design, and institutional governance are interdependent. Key risks include inconsistent consent practices, performance disparities for accented speech and speech disorders, accuracy degradation in real clinical settings, automation complacency, and unclear accountability across vendors and healthcare organizations. These risks inform a six-layer governance model spanning technical, human/workflow, ethical, organizational, regulatory, and sociocultural dimensions. We propose a governance framework and implementation roadmap to support responsible deployment of clinical STT systems, emphasizing transparency, patient autonomy, documentation integrity, and accountable oversight.