ArticleBMC medical ethics2025
Evaluating trustworthiness in AI-Based diabetic retinopathy screening: addressing transparency, consent, and privacy challenges.
Article in BMC medical ethics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled 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.
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, 1 synthesis or guideline pooled it.
- Current landscape and research gaps in artificial intelligence for ophthalmic nursing: a scoping review.Frontiers in medicine · 2026Pooled it
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI) offers significant potential to drive advancements in healthcare; however, the development and implementation of AI models present complex ethical, legal, social, and technical challenges, as data practices often undermine regulatory frameworks in various regions worldwide. This study explores stakeholder perspectives on the development and deployment of AI algorithms for diabetic retinopathy (DR) screening, with a focus on ethical risks, data practices, governance, and emerging shortcomings in the Global South AI discourse.
methodsFifteen semi-structured interviews were conducted with ophthalmologists, program officers, AI developers, bioethics experts, and legal professionals. Thematic analysis was guided by OECD principles for responsible AI stewardship. Interviews were analyzed using MAXQDA software to identify themes related to AI trustworthiness and ethical governance.
resultsSix key themes emerged regarding the perceived trustworthiness of AI: algorithmic effectiveness, responsible data collection, ethical approval processes, explainability, implementation challenges, and accountability. Participants reported critical shortcomings in AI companies' data collection practices, including a lack of transparency, inadequate consent processes, and limited patient awareness about data ownership. These findings highlight how unchecked data collection and curation practices may reinforce data colonialism in low and middle-income healthcare systems.
conclusionEnsuring trustworthy AI requires transparent and accountable data practices, robust patient consent mechanisms, and regulatory frameworks aligned with ethical and privacy standards. Addressing these issues is vital to safeguarding patient rights, preventing data misuse, and fostering responsible AI ecosystems in the Global South.
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.