The Technologies, Applicability, and Trade-Offs of AI in Palliative Care for Older Adults: Scoping Review

Vollständiger Abstract

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Abstract Background The rapid advancement of AI has introduced new opportunities for palliative care. However, its context-specific applicability and the trade-offs related to it use for older adults with multimorbidity, functional decline, and complex care needs remain unclear. Objective This review aimed to characterize the applicability of AI in palliative care for older adults and synthesize its potential benefits and limitations. Methods A scoping review was conducted following the framework of Arksey and O’Malley. Literature searches were performed in PubMed, Web of Science, CINAHL, Embase, and Scopus. Eligible studies were screened, and data were synthesized as a narrative synthesis incorporating thematic analysis. Results Eleven studies were included, primarily comprising retrospective predictive model development and validation studies, as well as AI-based clinical information extraction studies. Applications were examined in hospital and community settings and drew on diverse routinely collected and population-based data sources, including electronic health records, clinical databases, administrative claims and health insurance databases, and population-based longitudinal survey datasets. Traditional machine learning, deep learning, and natural language processing approaches were applied. AI applications encompassed prediction and identification, monitoring and data integration, clinical decision support, and health care system optimization. Reported potential roles occurred across the data management, application performance, and clinical practice levels and included multisource information integration, more efficient data use, identification of potential palliative care beneficiaries and health risks, prognostic prediction, and quantitative support for clinical decision-making. Potential cost savings were suggested but not directly evaluated. Reported limitations relevant to real-world implementation included insufficient data reliability and availability, weak model generalizability, and restricted applicability. Human-centered limitations were infrequently examined and included difficulty recognizing patients’ emotions and the continuing need for human intervention. Conclusions AI has the potential to support palliative care for older adults, but its clinical effectiveness and implementation effects remain uncertain. In this complex care context, the potential benefits of AI should be recognized while its inherent limitations are carefully considered. Future research should prioritize external model validation, real-world implementation studies, interoperable data systems, and the integration of patient-centered and contextual information. In clinical palliative care, AI should be positioned as an assistive tool, complementing rather than replacing clinical judgment and humanistic care.