MedTalkAI: Assisted Anamnesis Creation With Automatic Speech Recognition

Conventional approaches to documenting patient medical histories are often time-consuming and require significant healthcare professional involvement. This paper introduces MedTalkAI, which integrates ASR models, including Whisper and Wav2Vec 2.0, to transcribe audio recordings of patient histories in Brazilian Portuguese efficiently. MedTalkAI validates, corrects, and evaluates transcriptions, facilitating the creation of a unique medical audio-text database. Additionally, MedTalkAI enhances ASR models for medical applications using language models. This approach aims to improve medical history transcription and analysis, contributing to the development of more reliable ASR models and automating the documentation process.

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Publication Details

Published
2024-10-14
DOI
https://doi.org/10.5753/sbbd_estendido.2024.243214
Primary Topic
Speech Recognition and Synthesis
Type
article
Field-Weighted Citation Impact
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article

MedTalkAI: Assisted Anamnesis Creation With Automatic Speech Recognition

L. Silva, Ticiana L. Coelho da Silva, João Victor Barbosa Alves, José Antônio Fernandes de Macêdo et al.
Speech Recognition and Synthesis
article

MedTalkAI: Assisted Anamnesis Creation With Automatic Speech Recognition

L. Silva, Ticiana L. Coelho da Silva, João Victor Barbosa Alves, José Antônio Fernandes de Macêdo, Yanna Torres Gonçalves, Breno Alef Dourado Sá
article en

Abstract

Conventional approaches to documenting patient medical histories are often time-consuming and require significant healthcare professional involvement. This paper introduces MedTalkAI, which integrates ASR models, including Whisper and Wav2Vec 2.0, to transcribe audio recordings of patient histories in Brazilian Portuguese efficiently. MedTalkAI validates, corrects, and evaluates transcriptions, facilitating the creation of a unique medical audio-text database. Additionally, MedTalkAI enhances ASR models for medical applications using language models. This approach aims to improve medical history transcription and analysis, contributing to the development of more reliable ASR models and automating the documentation process.

Universidade Federal do Ceará (BR)
Quality Education
Openalex Percentile: Top 84%
Speech Recognition and Synthesis
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