Ethical issues of Artificial Intelligence in Healthcare in Developing Countries: A Systematic Review of Empirical Studies
Bibliographic record
Abstract
Significant improvements in diagnosis, treatment, and patient outcomes are possible with the use of artificial intelligence (AI) in healthcare. However, there is still a lack of research on ethical issues, especially in developing nations. This systematic review, conducted following PRISMA 2020 guidelines, identified 22 studies from a comprehensive search of 2977 records published between January 2019 and May 2024. Ethical themes were categorised using Jobin et al.'s framework and the European Commission's Ethics Guidelines for Trustworthy AI (EGTAI), while studies were evaluated using Kitchenham and Charters' quality checklist. Nine main ethical issues were identified by thematic analysis; the most often discussed issues were data privacy and justice, followed by patient safety, autonomy, and cyber-security. Benevolence received the least attention, while notable ethical conundrums included bias, fairness, discrimination, algorithmic transparency, and data protection. This systematic review highlights the need for stronger regulatory frameworks, ethical guidelines, and governance structures to ensure responsible AI integration in healthcare, particularly in developing countries, and calls for further research to address existing gaps.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.038 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.016 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".