Exploring the application of Artificial Intelligence in palliative care and its practical, technical and ethical considerations: a scoping review

Background Palliative care improves the quality of life of patients with life-limiting conditions and their families; however, global access remains constrained by workforce shortages and late referrals. Artificial Intelligence (AI) has been proposed as a scalable solution for optimising the identification of needs, supporting clinical decision-making, and enhancing care delivery. However, real-world evidence of the application of AI in palliative care remains sparse, particularly regarding its impact on quality of life, quality of care, and associated practical, technical and ethical challenges. Methods A scoping review was conducted following the Joanna Briggs Institute methodology and the PRISMA-ScR checklist. Five databases (the ACM Digital Library, CINAHL, Cochrane Central, PubMed and Web of Science) were searched. Studies reporting the use of AI to facilitate or enhance palliative care delivery in adults were eligible. Four reviewers independently screened the records, and two reviewers extracted the data using Covidence software. A narrative synthesis was then performed. Results Fifteen studies, published between 2021 and 2025, were included. Fourteen originated from Global North settings (USA 5, Germany 2, Japan 2, Taiwan 2, UK 1, Spain 1 and Cyprus 1) and one from Iran. Conceptually, AI applications fall into three domains: (1) early identification of palliative care needs, (2) symptom assessment and management and (3) clinical decision support for care conversations. Fifteen studies (100%) reported or discussed quality of care outcomes, most commonly prognostic performance, usability and referral/conversation rates, and only two (13.3%) directly addressed quality of life. Effectiveness was consistently positive, with four randomised controlled trials demonstrating superiority over usual care in referrals, advance care planning, pain control and quality of life domains. Practical barriers were centred on workflow integration and resource demands, while technical limitations include data quality, generalisability, and interpretability. Ethical discourse is underdeveloped, with major gaps in the principles of AI governance. Conclusions AI shows potential to improve prognostic accuracy, trigger earlier involvement of palliative care specialists and support symptom management. However, this evidence is geographically skewed, methodologically immature and ethically underdeveloped. Future research must prioritise diverse global settings, patient-reported quality of life outcomes, participatory co-design and systematic ethical governance to ensure equitable implementation.