Evidence map›Paper›PMID 37767523›Full record

ReviewFrontiers in digital health2023

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 Hübner, Xin Jing, Shuo Liu and 13 more

Open access · goldAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Frontiers in digital health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.8field-weighted citation impact, top 14% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 8 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

23 authors at 3 institutions in 3 countries.

Andreas TriantafyllopoulosEIHW - Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany.
Alexander KathanEIHW - Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany.
Alice BairdEIHW - Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany.
Lukas ChristEIHW - Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany.
Alexander GebhardEIHW - Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany.
Maurice GerczukEIHW - Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany.
Vincent KarasEIHW - Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany.
Tobias HübnerEIHW - Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany.
Xin JingEIHW - Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany.
Shuo LiuEIHW - Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany.
Adria Mallol-RagoltaEIHW - Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany.
Manuel MillingEIHW - Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany.
Sandra OttlEIHW - Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany.
Anastasia SemertzidouEIHW - Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany.
Srividya Tirunellai RajamaniEIHW - Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany.
Tianhao YanEIHW - Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany.
Zijiang YangEIHW - Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany.
Judith DineleyEIHW - Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany.
Shahin AmiriparianEIHW - Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany.
Katrin D Bartl-PokornyEIHW - Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany.
Anton BatlinerEIHW - Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany.
Florian B PokornyEIHW - Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany.
Björn W SchullerEIHW - Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany.
University of Augsburg · DEMedical University of Graz · ATImperial College London · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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. 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.

Indexed as

auscultationcomputer auditiondigital healthdigital medicinespeech and language disorders

Identifiers

PMID37767523
PMCPMC10520966
OpenAlexW4386634363

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.