ReviewSeminars in hearing2021
The Collaboration between Hearing Aid Users and Artificial Intelligence to Optimize Sound.
Review in Seminars in hearing, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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.
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.
Who cites it
11 citing papers in PubMed, 22 citations in OpenAlex.
- Artificial intelligence and hearing health: a global evidence review of biases and equity implications for Africa.Global health action · 2026Review
- Acoustic Scene-Aware Processing and Auditory Model-Based Compensation Strategies.Journal of the Association for Research in Otolaryngology : JARO · 2026Review
- Artificial Intelligence and Machine Learning in Audiology and Hearing Disorders: A Scoping Review with Bibliometric and Thematic Mapping (1995-2025).Audiology research · 2026Review
- AI-Driven Innovations in Hearing Health: A Review of Artificial Intelligence Applications in Audiology and Hearing Technologies.Current aging science · 2026Review
- An Ontology-Based Expert System Approach for Hearing Aid Fitting in a Chaotic Environment.Audiology research · 2025Article
- Artificial Intelligence in Audiology: A Scoping Review of Current Applications and Future Directions.Sensors (Basel, Switzerland) · 2024Article
- Digital Therapeutics in Hearing Healthcare: Evidence-Based Review.Journal of audiology & otology · 2024Article
- A Review of Machine Learning Approaches for the Personalization of Amplification in Hearing Aids.Sensors (Basel, Switzerland) · 2024Review
- Mechanical effect of reconstructed shapes of autologous ossicles on middle ear acoustic transmission.Frontiers in bioengineering and biotechnology · 2023Article
- Translational Applications of Machine Learning in Auditory Electrophysiology.Seminars in hearing · 2022Review
- Ecological Momentary Assessment to Obtain Signal Processing Technology Preference in Cochlear Implant Users.Journal of clinical medicine · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hearing aid gain and signal processing are based on assumptions about the average user in the average listening environment, but problems may arise when the individual hearing aid user differs from these assumptions in general or specific ways. This article describes how an artificial intelligence (AI) mechanism that operates continuously on input from the user may alleviate such problems by using a type of machine learning known as Bayesian optimization. The basic AI mechanism is described, and studies showing its effects both in the laboratory and in the field are summarized. A crucial fact about the use of this AI is that it generates large amounts of user data that serve as input for scientific understanding as well as for the development of hearing aids and hearing care. Analyses of users' listening environments based on these data show the distribution of activities and intentions in situations where hearing is challenging. Finally, this article demonstrates how further AI-based analyses of the data can drive development.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.