Evidence map›Paper›PMID 42238471›Full record

ArticlemedRxiv : the preprint server for health sciences2026

Does Recording Hardware Matter for Clinical Speech Recognition? Evaluating ASR Performance Across Consumer Devices.

Brian D Tran, Di Hu, Seungjun Kim, Yawen Guo, Ramya Mangu, Tera L Reynolds, Jennifer Elston Lafata, Ming Tai-Seale, Kai Zheng

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Brian D TranUniversity of California, Irvine, Irvine, CA, USA.
Di HuUniversity of California, Irvine, Irvine, CA, USA.
Seungjun KimUniversity of California, Irvine, Irvine, CA, USA.
Yawen GuoUniversity of California, Irvine, Irvine, CA, USA.
Ramya ManguUniversity of California, Irvine, Irvine, CA, USA.
Tera L ReynoldsUniversity of Maryland, Baltimore County, Baltimore, MD, USA.
Jennifer Elston LafataUniversity of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Ming Tai-SealeUniversity of California, San Diego, San Diego, CA, USA.
Kai ZhengUniversity of California, Irvine, Irvine, CA, USA.

Funding

University of California Health Participation in the National COVID Cohort Collaborative (N3C)UL1TR001414 · NCATS · UNIVERSITY OF CALIFORNIA-IRVINE · PI COOPER, DAN M, VILAIN, ERIC J. · 2015 to 2023
$35.1M
MEDICAL SCIENTIST TRAINING PROGRAMT32GM008620 · NIGMS · UNIVERSITY OF CALIFORNIA-IRVINE · PI GOLDIN, ALAN L · 1999 to 2023
$8.1M
PHYSICIAN RECOMMENDATION AND COLORECTAL CANCER SCREENINGR01CA112379 · NCI · HENRY FORD HEALTH SYSTEM · PI ELSTON LAFATA, JENNIFER M · 2006 to 2009
$1.7M
Mental Health Communication in Elderly Primary Care Visits and Economic OutcomesR01MH081098 · NIMH · TEXAS A&M UNIVERSITY HEALTH SCIENCE CTR · PI TAI-SEALE, MING · 2009 to 2010
$1.0M
NCATS NIH HHS UL1 TR001414NCI NIH HHS R01 CA112379NIGMS NIH HHS T32 GM008620NIMH NIH HHS R01 MH081098
6 · The paper itself

Abstract

Ambient clinical intelligence (ACI) systems use automatic speech recognition (ASR) to capture patient-provider conversations for downstream clinical documentation. However, many ASR evaluations are conducted under controlled conditions using specialized hardware. We evaluated how recording devices influence transcription performance of contemporary ASR engines applied to clinical dialogue. Thirty-five primary care encounters were re-enacted from transcribed conversations and recorded using five devices simultaneously: smartphone, laptop microphone, portable recorder, clip-on microphone, and a desktop microphone. Six ASR engines were evaluated using word error rate (WER), clinical concept extraction precision and recall, and sentence-level semantic similarity. Median WER ranged from 16.7% to 20.7% across engines. Engine choice produced larger variation in transcription performance than recording device, although device-related differences were statistically significant. Overall, contemporary ASR engines demonstrated relative robustness to consumer-grade recording hardware, suggesting that model selection may have greater impact on transcription performance than recording device configuration in real-world ACI deployments.

Identifiers

PMID42238471
PMCPMC13228836

What OpenQuestion holds

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Registered trials

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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.