ArticleJournal of healthcare informatics research2025
Assessing the Effectiveness of Automatic Speech Recognition Technology in Emergency Medicine Settings: a Comparative Study of Four AI-Powered Engines.
Article in Journal of healthcare informatics research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Dictionary-Augmented Large Language Model Postprocessing for Bilingual Code-Switched Medical Speech Recognition: Development and Evaluation Study.Journal of medical Internet research · 2026Article
- Development and validation of a natural language processing system to assess quality of physician communication in prostate cancer consultations.Prostate cancer and prostatic diseases · 2026Article
- Pseudocode Generation from Clinical Protocol Flowchart using Large Vision-Language Models.Proceedings. IEEE International Conference on Healthcare Informatics · 2026Article
- Does Recording Hardware Matter for Clinical Speech Recognition? Evaluating ASR Performance Across Consumer Devices.medRxiv : the preprint server for health sciences · 2026Article
- Automatic Speech Recognition and Large Language Models for Multilingual Pathology Report Generation: Proof-of-Concept Study.JMIR formative research · 2026Article
- Accuracy of large language model transcription of simulated physician-patient verbal interactions.BMC medical informatics and decision making · 2026Article
- Promise and Caution: Mapping Opportunities for AI Decision Support in Emergency Medical Services.Proceedings. IEEE International Conference on Healthcare Informatics · 2026Article
- Benchmarking speech-to-text robustness in noisy emergency medical dialogues: an evaluation of models under realistic acoustic conditions.JAMIA open · 2025Article
- Technical requirements and optimization strategies for home-based teleradiology workstations: a review article.Insights into imaging · 2025Review
- Unpacking Situational Awareness in Emergency Medical Services: An Eye-Tracking Study of Visual Attention.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
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4 authors.
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Abstract
This study investigates the potential of advanced automatic speech recognition (ASR) technology for transcribing and recognizing medical information during patient encounters, with the aim of enabling real-time clinical documentation to alleviate clinicians' workload. While ASR holds promise, its effectiveness in noisy and dynamic medical settings, such as emergency medical services (EMS), remains underexplored. To address this, four ASR engines-Google Speech-to-Text Clinical Conversation, OpenAI Speech-to-Text, Amazon Transcribe Medical, and Azure Speech-to-Text-were evaluated using 40 EMS simulation recordings. Transcriptions were analyzed for accuracy across 23 electronic health record (EHR) categories relevant to EMS. Google Speech-to-Text Clinical Conversation showed the best overall performance, excelling in categories such as "mental state" (F1 = 1.0), "allergies" (F1 = 0.912), and "electrolytes" (F1 = 1.0). However, all engines struggled with critical EMS categories like "airway" (F1 = 0.524) and "pupils" (F1 = 0.542). These findings highlight the limitations of current ASR technologies and the need for further advancements to improve accuracy and usability in time-sensitive and high-pressure medical environments. Supplementary Information: The online version contains supplementary material available at 10.1007/s41666-025-00193-w.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.