ArticleBMC medical ethics2025
Public trust of AI in healthcare in South Africa: results of a survey.
Article in BMC medical ethics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 2 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
2 citing papers in PubMed.
- Artificial intelligence for Africa: bridge or barrier for infectious disease control?IJID regions · 2026Article
- Correction: Public trust of AI in healthcare in South Africa: results of a survey.BMC medical ethics · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
2 authors.
Funding
Abstract
backgroundThe integration of Artificial Intelligence (AI) in healthcare promises significant advancements in patient care, yet its success heavily relies on public trust and acceptance, particularly in diverse socio-economic contexts like South Africa. This study investigates South African residents' willingness to trust AI in healthcare decisions, exploring the impact of socio-demographic factors such as age and religion on their preferences.
methodsUtilizing a cross-sectional online survey distributed via Facebook, we gathered data from 341 respondents across South Africa. The survey assessed participants' preference for human versus AI doctors in serious health scenarios, alongside demographic information including age, gender, educational attainment, religion, and home language. Weighting adjustments were applied to align the sample with South Africa's demographic proportions for home language and gender. Data were analysed to explore correlations between these demographics and preferences for AI in healthcare.
resultsA significant majority (73.7% weighted) expressed a preference for a human doctor over an AI doctor. Notably, the importance of religion (p < .001) and specific age groups (p = .025) significantly influenced preferences. A significant proportion of respondents for whom religion was "not too important," as well as those in the 40-49 age group, preferred an AI doctor.
conclusionsThis study underscores the need for innovative governance models tailored to resource-constrained settings, where traditional human-in-the-loop requirements may not always be feasible. Future research should explore how socio-cultural factors and trust dynamics influence public attitudes toward AI in healthcare and investigate models that ensure safety and accountability while addressing practical limitations in healthcare delivery systems.
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.