HEAR4Health: A blueprint for making computer audition a staple of modern healthcare
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

TL;DR
This paper proposes a comprehensive framework for integrating computer audition into modern healthcare, emphasizing technological, efficiency, personalization, and ethical considerations to transform traditional auditory medical tools like the stethoscope.
Contribution
It introduces a structured blueprint outlining key technological and ethical pillars necessary for advancing computer audition in healthcare applications.
Findings
Identifies four key pillars: Hear, Earlier, Attentively, Responsibly.
Highlights challenges and opportunities in AI-driven auditory healthcare.
Provides a strategic roadmap for future research and development.
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…
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Taxonomy
TopicsMachine Learning in Healthcare
