ArticleJournal of Korean medical science2022
Accuracy of Cloud-Based Speech Recognition Open Application Programming Interface for Medical Terms of Korean.
Article in Journal of Korean medical science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Assessing the Reliability, Accuracy, and Relevance of Artificial Intelligence Speech Recognition for Clinical Documentation: A Scoping Review.Journal of evaluation in clinical practice · 2026Article
- Automatic Speech Recognition and Large Language Models for Multilingual Pathology Report Generation: Proof-of-Concept Study.JMIR formative research · 2026Article
- Technical requirements and optimization strategies for home-based teleradiology workstations: a review article.Insights into imaging · 2025Review
- Evaluation Framework of Large Language Models in Medical Documentation: Development and Usability Study.Journal of medical Internet research · 2024Article
- Automated Information Extraction from Thyroid Operation Narrative: A Comparative Study of GPT-4 and Fine-tuned KoELECTRA.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024Article
- Intelligent speech technologies for transcription, disease diagnosis, and medical equipment interactive control in smart hospitals: A review.Computers in biology and medicine · 2023Review
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Authors and funding
5 authors.
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Abstract
backgroundThere are limited data on the accuracy of cloud-based speech recognition (SR) open application programming interfaces (APIs) for medical terminology. This study aimed to evaluate the medical term recognition accuracy of current available cloud-based SR open APIs in Korean.
methodsWe analyzed the SR accuracy of currently available cloud-based SR open APIs using real doctor-patient conversation recordings collected from an outpatient clinic at a large tertiary medical center in Korea. For each original and SR transcription, we analyzed the accuracy rate of each cloud-based SR open API (i.e., the number of medical terms in the SR transcription per number of medical terms in the original transcription).
resultsA total of 112 doctor-patient conversation recordings were converted with three cloud-based SR open APIs (Naver Clova SR from Naver Corporation; Google Speech-to-Text from Alphabet Inc.; and Amazon Transcribe from Amazon), and each transcription was compared. Naver Clova SR (75.1%) showed the highest accuracy with the recognition of medical terms compared to the other open APIs (Google Speech-to-Text, 50.9%,
conclusionAmong three current cloud-based SR open APIs, Naver Clova SR which manufactured by Korean company showed highest accuracy of medical terms in Korean, compared to Google Speech-to-Text and Amazon Transcribe. Although limitations are existing in the recognition of medical terminology, there is a lot of rooms for improvement of this promising technology by combining strengths of each SR engines.
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