HEAR4Health: A blueprint for making computer audition a staple of modern
healthcare
- URL: http://arxiv.org/abs/2301.10477v1
- Date: Wed, 25 Jan 2023 09:25:08 GMT
- Title: HEAR4Health: A blueprint for making computer audition a staple of modern
healthcare
- Authors: Andreas Triantafyllopoulos, Alexander Kathan, Alice Baird, Lukas
Christ, Alexander Gebhard, Maurice Gerczuk, Vincent Karas, Tobias H\"ubner,
Xin Jing, Shuo Liu, Adria Mallol-Ragolta, Manuel Milling, Sandra Ottl,
Anastasia Semertzidou, Srividya Tirunellai Rajamani, Tianhao Yan, Zijiang
Yang, Judith Dineley, Shahin Amiriparian, Katrin D. Bartl-Pokorny, Anton
Batliner, Florian B. Pokorny, Bj\"orn W. Schuller
- Abstract summary: Recent years have seen a rapid increase in digital medicine research in an attempt to transform traditional healthcare systems.
Computer audition can be seen to be lagging behind, at least in terms of commercial interest.
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.
- Score: 89.8799665638295
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: 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.
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