Aug 2026· Revue medicale suisse· Vol 22 971, pp.
1313-1317
· 0 citations
Medicine
TL;DR
Artificial intelligence-powered transcription systems, also referred to as ambient scribing, are emerging as potential solutions to reduce documentation burden, but these tools also have limitations, including risk of errors or generation of inaccurate content.
Abstract
Clinical documentation accounts for an increasing proportion of clinicians' working time, reducing the time devoted to patients and affecting personal satisfaction. In this context, artificial intelligence-powered transcription systems, also referred to as ambient scribing, are emerging as potential solutions to reduce documentation burden. Several studies suggest a reduction in documentation time and a perceived improvement in clinicians' experience. However, these tools also have limitations, including risk of errors or generation of inaccurate content, necessitating systematic human validation. Their adoption should be embedded within a structured approach that considers integration with electronic health records, performance, confidentiality, data security, and regulatory framework.
A practical framework for safe implementation in outpatient and hospital care in the Czech context is proposed and potential reductions in documentation time, lower cognitive workload and improved clinician attention to patients are suggested.
BACKGROUND
Clinical documentation accounts for a substantial proportion of ophthalmologists' working time and increasingly consumes scarce resources. Ambient scribe systems, which automatically transcribe clinical consultations and generate structured clinical notes, could reduce the documentation burden. To date, data...
J. Terheyden, Simon Haas, Corinna Schmalohr et al.· Die Ophthalmologie· 0 citations
It is argued that the successful implementation of ambient AI scribes depends less on technical capability alone and more on prudent healthcare leadership and multidisciplinary governance, which requires robust consent and data-protection frameworks, workflow redesign, clinician training, and ongoing monitoring of qual...
K. Zhou, Q. Ng, H. Tan et al.· Journal of Healthcare Leader...· 0 citations
This mixed-methods evaluation suggests that a deliberately constrained, language-focused AI system can improve the accessibility of medical notes while preserving clinical accuracy and safety while extending into clinical interpretation.
Nicholas Lamb· Frontiers in Digital Health· 0 citations
As AI systems are increasingly deployed in high-stakes settings, two concerns become particularly important: cautiousness (awareness of model limitations for risk-aware decisions) and explainability (providing human-understandable justifications). My PhD research develops methods that enable both, from theoretical and...
Victor F. Lopes de Souza· Proceedings of the Thirty-Fi...· 0 citations
This paper argues that ambient AI scribes should be understood not merely as workflow tools, but as emerging infrastructure that will materially shape the capacity and capabilities of LHSs, and outlines a governance agenda grounded in LHS principles.
T. Togunwa, Jodyn E. Platt· Learning Health Systems· 0 citations
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