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

2026-01-01

arXiv.org

AI Summary

This study addresses the frequent oversight of sociotechnical risks—such as transparency, privacy, reliability, and patient autonomy—in the rapid deployment of clinical speech-to-text systems. Through an interdisciplinary review integrating AI evaluation, clinical workflows, ethical compliance, and organizational governance, the work proposes the first comprehensive sociotechnical governance framework tailored to this technology. The framework offers actionable implementation pathways, encompassing system readiness assessment, vendor vetting, pilot deployment, staff training, and ongoing monitoring. By aligning these components, it jointly safeguards patient autonomy, ensures documentation integrity, and reinforces institutional trust, thereby enabling healthcare organizations to responsibly adopt speech-to-text technologies.

Technology Category

• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Humans and AI: AI for Accessibility
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Responsible Web: Technology governance, policy, and regulations
• Security and Privacy: Privacy-enhancing technologies
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

AI-driven speech-to-text (STT) documentation systems are increasingly adopted in clinical settings to reduce documentation burden and improve workflow efficiency. However, their rapid deployment has outpaced understanding of the associated socio-technical risks, including transparency, reliability, patient autonomy, workflow alignment, and organizational governance. A clearer analysis of these risks is needed to support safe and equitable integration into healthcare practice. This study synthesizes interdisciplinary evidence from technical performance research, regulatory and ethical standards, clinical workflow analyses, and organizational policy guidance. The synthesis was used to develop a multi-layered socio-technical conceptual framework for evaluating and governing STT systems. Findings show that STT systems operate within tightly coupled socio-technical environments in which model performance, clinician oversight, patient rights, workflow design, and institutional governance are interdependent. The study offers a structured socio-technical governance framework and an implementation roadmap that outlines readiness assessment, vendor evaluation, pilot deployment, clinician training, ongoing monitoring, and iterative improvement. The framework emphasizes safeguards that protect patient autonomy, documentation integrity, and institutional trust while enabling the efficient and beneficial use of STT technologies. This work provides actionable guidance for healthcare organizations seeking to adopt STT systems responsibly and equitably.

Problem

• speech-to-text
• clinical documentation
• privacy
• transparency
• reliability

Innovation

• socio-technical framework
• clinical speech-to-text systems
• AI governance
• patient autonomy
• implementation roadmap