Intelligent speech technologies for transcription, disease diagnosis, and medical equipment interactive control in smart hospitals: A review

Zhang, Jun; Wu, Junjie; Qiu, Yiyi; Song, Aiguo; Liu, Weifeng; Li, Xin; Liu, Yecheng

doi:10.1016/j.compbiomed.2022.106517

Cited by 25 publications

(8 citation statements)

References 199 publications

(300 reference statements)

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“…The application of time-frequency and time-space features for LSTM learning was shown to be of high and better performance in classifying physiological signals than several other classification models [19]. In this study, the experimental results illustrated the superior performance of the LSTM that learned on time-frequency and time-space features of Medical voice analysis systems utilize hardware, software, and human-computer interaction to achieve smart hospital facilities [33]. Technical elaborations on this study can contribute to endeavors concerning intelligent technology for the diagnosis of pathology in human acoustics and its potential applications in smart healthcare.…”

Section: Discussionmentioning

confidence: 70%

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Diagnosis of Pathological Speech with Efficient and Effective Features for Long Short-Term Memory Learning

Pham,

Holmes,

Zou

et al. 2023

Preprint

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The majority of voice disorders stem from improper vocal usage. Alterations in voice quality can also serve as indicators for a broad spectrum of diseases. Particularly, the significant correlation between voice disorders and dental health underscores the need for precise diagnosis through acoustic data. This paper introduces effective and efficient features for deep learning with speech signals to distinguish between two groups: individuals with healthy voices and those with pathological voice conditions. Using a public voice database, the ten-fold test results obtained from long short-term memory networks trained on the combination of time-frequency and time-space features with a data balance strategy achieved the following metrics: accuracy = 90%, sensitivity = 93%, specificity = 87%, precision = 88%,F1score = 0.90, and area under the receiver operating characteristic curve = 0.96.

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Section: Discussionmentioning

confidence: 70%

“…Medical voice analysis systems utilize hardware, software, and human-computer interaction to achieve smart hospital facilities [33]. Technical elaborations on this study can contribute to endeavors concerning intelligent technology for the diagnosis of pathology in human acoustics and its potential applications in smart healthcare.…”

Section: Discussionmentioning

confidence: 99%

Diagnosis of Pathological Speech with Efficient and Effective Features for Long Short-Term Memory Learning

Pham,

Holmes,

Zou

et al. 2023

Preprint

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“…Using influencing factors revealed in this study, pilot testing of a developed VAT in a controlled setting, such as a simulation center, could be used to further test the benefits and drawbacks of this technology for dentistry. Building on this study’s findings, we could include the acceptance of other artificial AI systems by practitioners in future studies [ 17 , 60 , 61 ]. In addition, we plan to investigate the acceptance of both VAT and AI by patients and caregivers.…”

Section: Discussionmentioning

confidence: 99%

“…Dental speech applications are currently available for EHRs, however there are limitations [ 8 ]. Most of these software programs currently utilize two distinct types of voice input: command-driven and dictation [ 16 , 17 ]. The command-driven software allows users to navigate the EHR and enter information [ 12 ]; however, this mode of input requires memorization of specific words and prompts to navigate and enter information into the EHR, limiting its overall usefulness [ 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

Acceptance of voice assistant technology in dental practice: A cross sectional study with dentists and validation using structural equation modeling

Warren,

Claman,

Meyer

et al. 2024

PLOS Digit Health

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Voice assistant technologies (VAT) has been part of our daily lives, as a virtual assistant to complete requested tasks. The integration of VAT in dental offices has the potential to augment productivity and hygiene practices. Prior to the adoption of such innovations in dental settings, it is crucial to evaluate their applicability. This study aims to assess dentists’ perceptions and the factors influencing their intention to use VAT in a clinical setting. A survey and research model were designed based on an extended Unified Theory of Acceptance and Use of Technology (UTAUT). The survey was sent to 7,544 Ohio-licensed dentists through email. The data was analyzed and reported using descriptive statistics, model reliability testing, and partial least squares regression (PLSR) to explain dentists’ behavioral intention (BI) to use VAT. In total, 257 participants completed the survey. The model accounted for 74.2% of the variance in BI to use VAT. Performance expectancy and perceived enjoyment had significant positive influence on BI to use VAT. Perceived risk had significant negative influence on BI to use VAT. Self-efficacy had significantly influenced perceived enjoyment, accounting for 35.5% of the variance of perceived enjoyment. This investigation reveals that performance efficiency and user enjoyment are key determinants in dentists’ decision to adopt VAT. Concerns regarding the privacy of VAT also play a crucial role in its acceptance. This study represents the first documented inquiry into dentists’ reception of VAT, laying groundwork for future research and implementation strategies.

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“…В настоящее время исследователи отмечают ряд проблем, возникающих при внедрении технологий ГВ: стоимость на этапе первоначального внедрения; затраты времени, связанных с обучением персонала, стоимость оборудования [7]. Положительными аспектами внедрения ГВ в рутинной практике являются: положительная оценка медицинских работников и рост их удовлетворенности при работе с ЭМК; уменьшение времени, затрачиваемого на заполнение медицинской документации.…”

Section: перечень сокращений и обозначенийunclassified

Implementation of artificial intelligence in speech (voice input) in the conditions of a high-flow endoscopy

Shabunin,

Bedin,

Korzheva

et al. 2023

jour

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Artificial intelligence (AI) and digitalization are used in all spheres of life, including medicine: in making diagnoses, in working with patients, in creating and using an Electronic Medical Record (EMC) for fast and accessible document management. Attempts have been made repeatedly to facilitate work with medical protocols. Technologies related to speech recognition are a promising direction in this area. The purpose of the research work was to optimize the time of writing the protocol using voice input. The object of the study was a template protocol of endoscopic examination. This work was carried out on the basis of 2 large endoscopic centers in Moscow, created as part of the pilot project of the DM within the framework of the colorectal cancer screening program in the Russian Federation in 2021. The software of the Voice2Med Speech Technology Center (MDG) was used to fill out the research protocol. Using mathematical calculations, 4 metrics were created to evaluate the work of the software: the time of protocol formation; the proportion of protocol formation time from the study; the level of speech recognition accuracy; satisfaction of doctors when working with technology. Standardized protocol templates for various nosologies related to voice commands have been developed; medical thesaurus. In the course of the work, the accuracy of speech recognition was investigated and improved. Control measurements of the time of writing the protocol were carried out. The results of the evaluation of the use of the technology by endoscopists were obtained - the time of protocol formation decreased by 29%, the evaluation of the work by endoscopists was positive. Thus, the use of speech technologies related to the formation of a doctor’s protocol “from the voice” is a promising direction for the further development of the IT sphere in practical medicine.

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Intelligent speech technologies for transcription, disease diagnosis, and medical equipment interactive control in smart hospitals: A review

Cited by 25 publications

References 199 publications

Diagnosis of Pathological Speech with Efficient and Effective Features for Long Short-Term Memory Learning

Diagnosis of Pathological Speech with Efficient and Effective Features for Long Short-Term Memory Learning

Acceptance of voice assistant technology in dental practice: A cross sectional study with dentists and validation using structural equation modeling

Implementation of artificial intelligence in speech (voice input) in the conditions of a high-flow endoscopy

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