ReviewGlobal health action2026
Artificial intelligence and hearing health: a global evidence review of biases and equity implications for Africa.
Review in Global health action, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) is increasingly integrated into audiology and hearing health, yet evidence from across the health sciences shows that AI systems routinely embed structural biases that can exacerbate inequities, particularly for African and other low- and middle-income country (LMIC) populations. This review identified and analysed bias types in AI applications relevant to audiology and examined their ethical, cultural, and linguistic implications for LMIC settings. A narrative review design was adopted to accommodate the heterogeneity of available evidence, where thematic saturation was more appropriate than effect-size aggregation. Peer-reviewed articles published between 2015 and 2025 were retrieved from PubMed, Scopus, Web of Science, and IEEE Xplore, with inclusion requiring explicit engagement with AI and bias or equity. Rigour was assessed using a six-domain quality rubric, and data were extracted into structured evidence tables for thematic synthesis. Thirty-three studies met inclusion criteria: six were audiology-specific empirical studies (all small scale), and the remainder were reviews or conceptual analyses. No study presented empirical African audiogram, auditory brainstem response (ABR), or speech data. Six recurrent bias types were identified; representation, measurement, algorithmic, evaluation, deployment, and intersectional, with representation bias most frequent, exemplified by English-only corpora that underperform on tonal or indigenous languages. These biases manifest as misclassified hearing loss, reduced ABR accuracy, inequitable hearing-aid personalisation, and poor cochlear-implant algorithm transferability. Advancing equitable AI in audiology requires multilingual, paediatric-inclusive, locally governed datasets; fairness-aware model design with stratified reporting; and African-led governance and capacity-building to support future validation and implementation research.
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What Socratic 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 Socratic graph.