ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2018
A systematic comparison of contemporary automatic speech recognition engines for conversational clinical speech.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it, 30 citations in OpenAlex.
- Evaluating the performance of artificial intelligence-based speech recognition for clinical documentation: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- For the record: a narrative review of ambient voice technology in clinical documentation for dentistry and wider healthcare.British dental journal · 2026Review
- AI-Augmented Teach-Back in Dentistry: From Patient Education to Verified Clinical Understanding.Cureus · 2026Review
- Dictionary-Augmented Large Language Model Postprocessing for Bilingual Code-Switched Medical Speech Recognition: Development and Evaluation Study.Journal of medical Internet research · 2026Article
- Assessing the Reliability, Accuracy, and Relevance of Artificial Intelligence Speech Recognition for Clinical Documentation: A Scoping Review.Journal of evaluation in clinical practice · 2026Article
- Does Recording Hardware Matter for Clinical Speech Recognition? Evaluating ASR Performance Across Consumer Devices.medRxiv : the preprint server for health sciences · 2026Article
- Assessing the Effectiveness of Automatic Speech Recognition Technology in Emergency Medicine Settings: a Comparative Study of Four AI-Powered Engines.Journal of healthcare informatics research · 2025Article
- Association of machine-learning-rated supportive counseling skills with psychotherapy outcome.Journal of consulting and clinical psychology · 2025Article
- Feedback From Automatic Speech Recognition to Elicit Clear Speech in Healthy Speakers.American journal of speech-language pathology · 2023Article
- "Mm-hm," "Uh-uh": are non-lexical conversational sounds deal breakers for the ambient clinical documentation technology?Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Expectations for Artificial Intelligence (AI) in Psychiatry.Current psychiatry reports · 2022Review
- Automatic Assessment of Intelligibility in Noise in Parkinson Disease: Validation Study.Journal of medical Internet research · 2022Article
- An Automated Quality Evaluation Framework of Psychotherapy Conversations with Local Quality Estimates.Computer speech & language · 2022Article
- A dataset of simulated patient-physician medical interviews with a focus on respiratory cases.Scientific data · 2022Article
- Automated evaluation of psychotherapy skills using speech and language technologies.Behavior research methods · 2022Article
- Automatic speech recognition performance for digital scribes: a performance comparison between general-purpose and specialized models tuned for patient-clinician conversations.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
- Complete and Resilient Documentation for Operational Medical Environments Leveraging Mobile Hands-free Technology in a Systems Approach: Experimental Study.JMIR mHealth and uHealth · 2021Article
- Automatic Classification of Screen Gaze and Dialogue in Doctor-Patient-Computer Interactions: Computational Ethnography Algorithm Development and Validation.Journal of medical Internet research · 2021Article
- The digital scribe in clinical practice: a scoping review and research agenda.NPJ digital medicine · 2021Article
- Cognitive Computing-Based CDSS in Medical Practice.Health data science · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Conversations especially between a clinician and a patient are important sources of data to support clinical care. To date, clinicians act as the sensor to capture these data and record them in the medical record. Automatic speech recognition (ASR) engines have advanced to support continuous speech, to work independently of speaker and deliver continuously improving performance. Near human levels of performance have been reported for several ASR engines. We undertook a systematic comparison of selected ASRs for clinical conversational speech. Using audio recorded from unscripted clinical scenarios using two microphones, we evaluated eight ASR engines using word error rate (WER) and the precision, recall and F1 scores for concept extraction. We found a wide range of word errors across the ASR engines, with values ranging from 65% to 34%, all falling short of the rates achieved for other conversational speech. Recall for health concepts also ranged from 22% to 74%. Concept recall rates match or exceed expectations given measured word error rates suggesting that vocabulary is not the dominant issue.
Indexed as
Identifiers
30815110PMC6371385W2941185438What OpenQuestion holds
Registered trials
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.