Ethical Challenges and Opportunities of AI in End-of-Life Palliative Care: Integrative Review

被引:0
作者
Abejas, Abel Garcia [1 ,2 ]
Santos, David Geraldes [3 ]
Costa, Fabio Leite [1 ]
Botejara, Aida Cordero [1 ]
Mota-Filipe, Helder [4 ]
Verges, Angels Salvador [5 ]
机构
[1] Univ Beira Interior, Fac Hlth Sci, Lisbon, Portugal
[2] Hosp Lusiadas Lisboa, Palliat Care, Lisbon, Portugal
[3] Univ Beira Interior, Dept Commun Philosophy & Polit, Lisbon, Portugal
[4] Univ Lisbon, Fac Pharm, Pharmacol & Hlth Technol, Lisbon, Portugal
[5] Innohealth Acad, Magi Casanovas 11 Sitges, Barcelona 08870, Spain
关键词
palliative medicine; artificial intelligence (AI); ethical implications; end-of-life care; patient-centred care; INTELLIGENCE/MACHINE LEARNING-MODEL; ARTIFICIAL-INTELLIGENCE;
D O I
10.2196/73517
中图分类号
R-3 [医学研究方法]; R3 [基础医学];
学科分类号
1001 ;
摘要
Background: Artificial intelligence (AI) is increasingly integrated into palliative medicine, offering opportunities to improve quality, efficiency, and patient-centeredness in end-of-life care. However, its use raises complex ethical issues, including privacy, equity, dehumanization, and decision-making dilemmas. Objective: We aim to critically analyze the main ethical implications of AI in end-of-life palliative care and examine the benefits and risks. We propose strategies for ethical and responsible implementation. Methods: We conducted an integrative review of studies published from 2020 to 2025 in English, Portuguese, and Spanish, identified through systematic searches in PubMed, Scopus, and Google Scholar. Inclusion criteria were studies addressing AI in palliative medicinefocusing on ethical implications or patient experience. Two reviewers independently performed study selection and data extraction, resolving discrepancies by consensus. The quality of the papers was assessed using the Critical Appraisal Skills Programmechecklistand the Hawker etal tool. Results: Six key themes emerged: (1) practical applications of AI, (2) communication and AI tools, (3) patient experience and humanization, (4) ethical implications, (5) quality of life perspectives, and (6) challenges and limitations. WhileAI shows promise for improving efficiency and personalization, consolidated real-world examples of efficiency and equity remain scarce. Key risks includealgorithmic bias, cultural insensitivity, and the potential for reduced patientautonomy. Conclusions: AI can transform palliative care, but implementation must be patient-centered and ethically grounded. Robust policies are needed to ensure equity, privacy, and humanization. Future research should address data diversity, social determinants, and culturally sensitive approaches. (InteractJ MedRes2025;14:e73517) doi: 10.2196/73517
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页数:14
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