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1. 1

   For the record: a narrative review of ambient voice technology in clinical documentation for dentistry and wider healthcare.

   British dental journal (O et al.)Published Sep 25, 2026Checked Oct 8, 2026

   “Performance varied between systems, with reduced accuracy for dental terminology and clinically significant errors and hallucinations observed. No dental studies evaluated AI-generation of clinical records or letters. Studies across wider healthcare reported variable performance, including errors with the potential to cause harm. Implementation studies suggest reductions in documentation time and burden with AVT; however, the magnitude of effect was inconsistent, with uptake varying between clinicians and note-length increased. Patient attitudes were favourable although conditional on the accuracy and privacy of using these tools.Conclusions Ambient AI may reduce documentation time, but clinicians remain responsible for the final record produced by these systems. Ongoing research into safety and efficiency, alongside improved AI-literacy is required to ensure that the benefits of this technology are realised without compromising record accuracy or patient trust.”

2. 2

   Ethical considerations for clinical adoption of ambient digital scribe technology.

   Journal of the American Medical Informatics Association : JAMIA (Anderson et al.)Published Mar 1, 2026Checked Oct 8, 2026

   “Early data suggest that ADS utilization may reduce documentation burden and improve provider efficiency; however, the ethical implications of this largely unregulated technology remain relatively unexamined.FindingsIn this article, we identify and explore 4 key ethical issues surrounding ADS technology-safety, bias, data ownership, and justice-from a range of stakeholder perspectives. We provide an overview of current international regulatory policies, highlighting the need for standardized evaluation and reporting guidelines.RecommendationsDrawing on established ethical frameworks, we propose actionable recommendations for safe and equitable ADS implementation, including standardized evaluation metrics, regulatory oversight, and safeguards at institutional and end-user levels.ConclusionEnsuring the ethical implementation of ADS technology is essential for actualizing its potential benefits while upholding foundational principles of safety, equity, and transparency in clinical practice.”

3. 3

   Performance Evaluation of Offline Speech Recognition on Edge Devices

   Electronics (Gondi & Pratap)Published Nov 4, 2021Checked Oct 8, 2026

   “The major disadvantage of server-based speech recognition is the lack of privacy and security for user speech data. Additionally, because of network dependency, this server-based architecture cannot always be reliable, performant and available. Nevertheless, offline speech recognition on client devices overcomes these issues. However, resource constraints on smaller edge devices may pose challenges for achieving state-of-the-art speech recognition results. In this paper, we evaluate the performance and efficiency of transformer-based speech recognition systems on edge devices. We evaluate inference performance on two popular edge devices, Raspberry Pi and Nvidia Jetson Nano, running on CPU and GPU, respectively. We conclude that with PyTorch mobile optimization and quantization, the models can achieve real-time inference on the Raspberry Pi CPU with a small degradation to word error rate. On the Jetson Nano GPU, the inference latency is three to five times better, compared to Raspberry Pi. The word error rate on the edge is still higher, but it is not too far behind, compared to that on the server inference.”

4. 4

   Offline Speech Translation Systems: A Review of Opportunities, Technologies, and Future Prospects

   International Journal For Multidisciplinary Research (Vij)Published Dec 20, 2025Checked Oct 8, 2026

   “This paper reviews the development of speech translation technologies, tracing the shift from early cloud-dependent architectures to modern, on-device neural systems integrating automatic speech recognition (ASR), machine translation (MT), and text-to-speech (TTS). Drawing from recent research, field applications, and industry innovations, the review examines the technical foundations enabling offline translation, including model compression, quantization, and hardware acceleration, as well as emerging approaches such as semantic caching and vector-database-assisted adaptation. Application contexts across healthcare, disaster response, education, security, and travel highlight the advantages of offline systems in reliability, privacy protection, and low-latency communication. The review concludes by outlining ongoing challenges, such as limited language coverage, accuracy gaps, and hardware constraints, and identifies future directions for building secure, efficient, and inclusive offline speech translation technologies.”

5. 5

   Development and evaluation of a novel voice-enabled prototype to support consistent application of surgical safety checklists: a proof-of-concept study.

   Patient safety in surgery (Medroa et al.)Published Dec 24, 2025Checked Oct 8, 2026

   “The system operates offline, incorporating Rhasspy for intent recognition and Whisper for speech-to-text transcription. Twelve surgical team members participated in a field evaluation, completing structured tasks alongside routine workflows. Usability was assessed using the System Usability Scale (SUS) and a custom questionnaire. Technical evaluations tested Whisper V2 and V3 under quiet and noisy conditions, and Rhasspy's intent and wake word recognition across 1'125 commands in two environments (noisy, quiet).ResultsParticipants rated usability positively (SUS median score 76.04). Whisper V3 achieved 90.7-97.3% transcription accuracy, outperforming V2 in noisy settings. Rhasspy recognized intents with 92.8-94.8% accuracy and maintained a low false-positive rate. VoiceCheck functioned reliably offline and was preferred with a conference microphone for multi-user interaction.ConclusionVoiceCheck demonstrates feasibility for voice-assisted checklist execution in surgical settings. It was well accepted by users and performed reliably under realistic conditions. Further research should explore clinical integration, workflow impact, and multilingual capabilities.”