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HEAR4Health: a blueprint for making computer audition a staple of modern healthcare
Recent years have seen a rapid increase in digital medicine research in an attempt to transform traditional healthcare systems to their modern, intelligent, and versatile equivalents that are adequately equipped to tackle contemporary challenges. This has led to a wave of applications that utilise A...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10520966/ https://www.ncbi.nlm.nih.gov/pubmed/37767523 http://dx.doi.org/10.3389/fdgth.2023.1196079 |
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author | Triantafyllopoulos, Andreas Kathan, Alexander Baird, Alice Christ, Lukas Gebhard, Alexander Gerczuk, Maurice Karas, Vincent Hübner, Tobias Jing, Xin Liu, Shuo Mallol-Ragolta, Adria Milling, Manuel Ottl, Sandra Semertzidou, Anastasia Rajamani, Srividya Tirunellai Yan, Tianhao Yang, Zijiang Dineley, Judith Amiriparian, Shahin Bartl-Pokorny, Katrin D. Batliner, Anton Pokorny, Florian B. Schuller, Björn W. |
author_facet | Triantafyllopoulos, Andreas Kathan, Alexander Baird, Alice Christ, Lukas Gebhard, Alexander Gerczuk, Maurice Karas, Vincent Hübner, Tobias Jing, Xin Liu, Shuo Mallol-Ragolta, Adria Milling, Manuel Ottl, Sandra Semertzidou, Anastasia Rajamani, Srividya Tirunellai Yan, Tianhao Yang, Zijiang Dineley, Judith Amiriparian, Shahin Bartl-Pokorny, Katrin D. Batliner, Anton Pokorny, Florian B. Schuller, Björn W. |
author_sort | Triantafyllopoulos, Andreas |
collection | PubMed |
description | Recent years have seen a rapid increase in digital medicine research in an attempt to transform traditional healthcare systems to their modern, intelligent, and versatile equivalents that are adequately equipped to tackle contemporary challenges. This has led to a wave of applications that utilise AI technologies; first and foremost in the fields of medical imaging, but also in the use of wearables and other intelligent sensors. In comparison, computer audition can be seen to be lagging behind, at least in terms of commercial interest. Yet, audition has long been a staple assistant for medical practitioners, with the stethoscope being the quintessential sign of doctors around the world. Transforming this traditional technology with the use of AI entails a set of unique challenges. We categorise the advances needed in four key pillars: Hear, corresponding to the cornerstone technologies needed to analyse auditory signals in real-life conditions; Earlier, for the advances needed in computational and data efficiency; Attentively, for accounting to individual differences and handling the longitudinal nature of medical data; and, finally, Responsibly, for ensuring compliance to the ethical standards accorded to the field of medicine. Thus, we provide an overview and perspective of HEAR4Health: the sketch of a modern, ubiquitous sensing system that can bring computer audition on par with other AI technologies in the strive for improved healthcare systems. |
format | Online Article Text |
id | pubmed-10520966 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-105209662023-09-27 HEAR4Health: a blueprint for making computer audition a staple of modern healthcare Triantafyllopoulos, Andreas Kathan, Alexander Baird, Alice Christ, Lukas Gebhard, Alexander Gerczuk, Maurice Karas, Vincent Hübner, Tobias Jing, Xin Liu, Shuo Mallol-Ragolta, Adria Milling, Manuel Ottl, Sandra Semertzidou, Anastasia Rajamani, Srividya Tirunellai Yan, Tianhao Yang, Zijiang Dineley, Judith Amiriparian, Shahin Bartl-Pokorny, Katrin D. Batliner, Anton Pokorny, Florian B. Schuller, Björn W. Front Digit Health Digital Health Recent years have seen a rapid increase in digital medicine research in an attempt to transform traditional healthcare systems to their modern, intelligent, and versatile equivalents that are adequately equipped to tackle contemporary challenges. This has led to a wave of applications that utilise AI technologies; first and foremost in the fields of medical imaging, but also in the use of wearables and other intelligent sensors. In comparison, computer audition can be seen to be lagging behind, at least in terms of commercial interest. Yet, audition has long been a staple assistant for medical practitioners, with the stethoscope being the quintessential sign of doctors around the world. Transforming this traditional technology with the use of AI entails a set of unique challenges. We categorise the advances needed in four key pillars: Hear, corresponding to the cornerstone technologies needed to analyse auditory signals in real-life conditions; Earlier, for the advances needed in computational and data efficiency; Attentively, for accounting to individual differences and handling the longitudinal nature of medical data; and, finally, Responsibly, for ensuring compliance to the ethical standards accorded to the field of medicine. Thus, we provide an overview and perspective of HEAR4Health: the sketch of a modern, ubiquitous sensing system that can bring computer audition on par with other AI technologies in the strive for improved healthcare systems. Frontiers Media S.A. 2023-09-12 /pmc/articles/PMC10520966/ /pubmed/37767523 http://dx.doi.org/10.3389/fdgth.2023.1196079 Text en © 2023 Triantafyllopoulos, Kathan, Baird, Christ, Gebhard, Gerczuk, Karas, Hübner, Jing, Liu, Mallol-Ragolta, Milling, Ottl, Semertzidou, Rajamani, Yan, Yang, Dineley, Amiriparian, Bartl-Pokorny, Batliner, Pokorny and Schuller. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (https://creativecommons.org/licenses/by/4.0/) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Digital Health Triantafyllopoulos, Andreas Kathan, Alexander Baird, Alice Christ, Lukas Gebhard, Alexander Gerczuk, Maurice Karas, Vincent Hübner, Tobias Jing, Xin Liu, Shuo Mallol-Ragolta, Adria Milling, Manuel Ottl, Sandra Semertzidou, Anastasia Rajamani, Srividya Tirunellai Yan, Tianhao Yang, Zijiang Dineley, Judith Amiriparian, Shahin Bartl-Pokorny, Katrin D. Batliner, Anton Pokorny, Florian B. Schuller, Björn W. HEAR4Health: a blueprint for making computer audition a staple of modern healthcare |
title | HEAR4Health: a blueprint for making computer audition a staple of modern healthcare |
title_full | HEAR4Health: a blueprint for making computer audition a staple of modern healthcare |
title_fullStr | HEAR4Health: a blueprint for making computer audition a staple of modern healthcare |
title_full_unstemmed | HEAR4Health: a blueprint for making computer audition a staple of modern healthcare |
title_short | HEAR4Health: a blueprint for making computer audition a staple of modern healthcare |
title_sort | hear4health: a blueprint for making computer audition a staple of modern healthcare |
topic | Digital Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10520966/ https://www.ncbi.nlm.nih.gov/pubmed/37767523 http://dx.doi.org/10.3389/fdgth.2023.1196079 |
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