ArticleAI and ethics2026
Fostering an enabling environment for health AI innovation and scale: The need for tailored ethics training for innovators in low- and middle-income countries.
Article in AI and ethics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Research Ethics Committees (RECs) perspectives on large language models and AI ethics review: a South African case.BMC medical ethics · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors.
Funding
Abstract
Artificial intelligence (AI) is a rapidly evolving technology with transformative potential across many fields, including health. Interest in AI is growing globally, including in low- and middle-income countries (LMICs), but few studies have assessed the knowledge, experiences, and ethical practices of innovators in this space. This study aimed to assess the knowledge, experiences, challenges, and ethical practices of researchers and innovators, involved in AI-driven health research, to support the responsible integration of AI into health innovation. A cross-sectional, semi-quantitative online survey was conducted between February and May 2025 among health AI innovators in LMICs, primarily through the Grand Challenges network and its partners. Fifty respondents from 13 countries participated, the majority (92%) emphasized the importance of ethical principles, and 80% perceived AI-related ethical risks as greater than moderate. However, the majority of respondents (74%) were not sure about their competence in identifying and addressing the risks associated with AI practices and the same proportion reported having received no formal training in AI ethics. Respondents identified key challenges such as weak governance, limited ethics capacity, and risks of bias and data misuse. There was strong demand for training on ethics, bias mitigation, explainability, data governance, and privacy. The findings reveal an encouraging level of awareness but emphasize the need for formal training, clearer guidelines, and context-specific frameworks to ensure ethical AI development and deployment in health research.
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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.