Ethical Challenges in the Integration of Artificial Intelligence in Palliative Care
Bibliographic record
Abstract
The integration of artificial intelligence (AI) into palliative care offers the possibility of improved patient outcomes through enhanced decision-making, personalized care, and reduced healthcare provider burden. However, the use of AI in this sensitive area presents significant ethical challenges which require serious consideration to ensure that technology serves the best interests of patients without compromising their rights or well-being. This narrative review explores the key ethical issues associated with AI in palliative care, with a focus on low-resource settings where these challenges are often intensified. The review examines essential ethical principles such as autonomy, beneficence , non-maleficence, and justice, and identifies critical concerns including data privacy, informed consent , algorithmic bias, and the risk of depersonalizing care. It also highlights the unique difficulties faced in low-resource environments, where the lack of infrastructure and regulatory frameworks can exacerbate these ethical risks. To address these challenges, the review offers actionable recommendations, such as developing context-specific guidelines, promoting transparency and accountability through explainable AI (XAI), and conducting regular ethical audits. Interdisciplinary collaboration is emphasized to ensure that AI systems are ethically designed and implemented, respecting cultural contexts and upholding patient dignity. This study contributes to the ongoing discourse on ethical AI integration in healthcare, indicating the need for careful consideration of ethical principles to ensure that AI enhances rather than undermines the compassionate care at the heart of palliative care. These findings serve as a foundation for future research and policy development in this emerging field.
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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 | 0.117 | 0.165 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.033 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".