Artificial intelligence (AI) is transforming health care by enhancing diagnostics, improving patient outcomes, and reducing administrative burdens through advanced algorithms, with applications in medical imaging, virtual care, and automated data analysis. However, its role in palliative and hospice care remains underexplored.
Aim:
This review synthesizes research on AI applications in palliative and hospice care, examining its technological and clinical contributions to inform future research and guide clinical implementation.
Design:
An integrative literature review, guided by Whittemore and Knafl’s framework, analyzed qualitative, quantitative, and mixed-method studies. Registered with PROSPERO.
Data Sources:
A comprehensive search across 11 databases: Academic Search Complete, CINAHL, Cochrane Library, PubMed, Medline, Web of Science, Scopus, PsycINFO, ProQuest Dissertations & Theses Global, ACM Digital Library, and IEEE Xplore, identified English-language studies published from 2010 to 2024. Studies on AI applications in clinical settings, model validation, and key findings were included, with quality assessed using the Mixed Methods Appraisal Tool.
Results:
Seventy studies (2018–2024) were included, primarily quantitative analyses of retrospective clinical and administrative data. AI applications supported mortality prediction, symptom monitoring, patient needs identification, communication facilitation, care planning, and resource allocation. Early tools included rule-based and structured-data models, while more recent approaches integrate unstructured clinical notes, wearable devices, and multimodal data for individualized prognostication and timely interventions. Key barriers included reliance on retrospective or single-center datasets, limited generalizability, ethical and equity concerns, and challenges in integrating AI into clinical workflows.
Conclusions:
AI holds potential in enhancing timely, patient-centered palliative and hospice care, supporting prognostication, symptom management, and decision-making. Successful integration requires attention to clinician trust, workflow alignment, equity, and ethical considerations. To maximize its impact on underutilization, future research should focus on multicenter validation, representative datasets, ethical deployment, and seamless integration into clinical practice.
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