2023
Montenegro, L.; Gomes, L. M.; Machado, J. M.
AI-Based Medical Scribe to Support Clinical Consultations: A Proposed System Architecture Proceedings Article
Em: N., Vale Z. Moniz N. Moniz (Ed.): pp. 274-285, Springer Science and Business Media Deutschland GmbH, 2023, ISSN: 03029743, (cited By 0; Conference of 22nd EPIA Conference on Artificial Intelligence, EPIA 2023 ; Conference Date: 5 September 2023 Through 8 September 2023; Conference Code:305499).
Resumo | Links | BibTeX | Etiquetas: AI applications; Automatic speech recognition; Digital transformation; Language processing; Natural language processing; Natural languages; Patient care; Smart healthcare; Smart hospital; Systems architecture, Deep learning; Health care; Natural language processing systems; Population statistics; Speech recognition, Hospitals
@inproceedings{Montenegro2023274,
title = {AI-Based Medical Scribe to Support Clinical Consultations: A Proposed System Architecture},
author = {L. Montenegro and L. M. Gomes and J. M. Machado},
editor = {Vale Z. Moniz N. Moniz N.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85180626879&doi=10.1007%2f978-3-031-49011-8_22&partnerID=40&md5=a02dc441c31b5e5b04b0b741a3aaa2bd},
doi = {10.1007/978-3-031-49011-8_22},
issn = {03029743},
year = {2023},
date = {2023-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {14116 LNAI},
pages = {274-285},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {AI applications in hospital frameworks can improve patient-care quality and efficient workflows and assist in digital transformation. By designing Smart Hospital infrastructures, creating an efficient framework enables patient information exchange between hospitals, point of care, and remote patient monitoring. Deep learning (DL) solutions play important roles in these infrastructures’ digital transformation process and architectural design. Literature review shows that DL solutions based on Automatic Speech Recognition (ASR) and Natural Language Processing (NLP) are rising concerning clinical data digitalisation, population health management, and improving patient care. Nevertheless, one of the literature’s shortcomings highlights the limited research using these solutions in real-world medical environments. As part of smart hospitals, smart medical scribes have been presented in several studies as a promising solution. However, just a few studies have tested it in real settings. Moreover, it was limited to non-existent studies on non-English systems, even yet to be found similar studies for European Portuguese. The proposed study evaluates NLP-based solutions in real-life Portuguese clinical settings focused on patient care for Smart Healthcare applications. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
note = {cited By 0; Conference of 22nd EPIA Conference on Artificial Intelligence, EPIA 2023 ; Conference Date: 5 September 2023 Through 8 September 2023; Conference Code:305499},
keywords = {AI applications; Automatic speech recognition; Digital transformation; Language processing; Natural language processing; Natural languages; Patient care; Smart healthcare; Smart hospital; Systems architecture, Deep learning; Health care; Natural language processing systems; Population statistics; Speech recognition, Hospitals},
pubstate = {published},
tppubtype = {inproceedings}
}
AI applications in hospital frameworks can improve patient-care quality and efficient workflows and assist in digital transformation. By designing Smart Hospital infrastructures, creating an efficient framework enables patient information exchange between hospitals, point of care, and remote patient monitoring. Deep learning (DL) solutions play important roles in these infrastructures’ digital transformation process and architectural design. Literature review shows that DL solutions based on Automatic Speech Recognition (ASR) and Natural Language Processing (NLP) are rising concerning clinical data digitalisation, population health management, and improving patient care. Nevertheless, one of the literature’s shortcomings highlights the limited research using these solutions in real-world medical environments. As part of smart hospitals, smart medical scribes have been presented in several studies as a promising solution. However, just a few studies have tested it in real settings. Moreover, it was limited to non-existent studies on non-English systems, even yet to be found similar studies for European Portuguese. The proposed study evaluates NLP-based solutions in real-life Portuguese clinical settings focused on patient care for Smart Healthcare applications. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.