Artificial intelligence in home-based serious illness care: a scoping review of applications supporting quality palliative care

Document Type

Article

Publication Date

5-26-2026

Publication Title

Annals of Palliative Medicine

Abstract

BACKGROUND: Artificial intelligence (AI) has emerged as a promising tool to address gaps in palliative care access and delivery for adults with serious illness and their family caregivers, particularly in home-based settings where access to specialty care is limited. AI-driven tools, including machine learning, natural language processing, and decision-support systems may enable proactive, personalized, and efficient approaches to addressing several domains of quality palliative care, as defined by the National Consensus Project (NCP), including continuity of care, symptom relief, emotional support, and family caregiver assistance. This scoping review aimed to systematically map the evidence on AI applications designed to assist home-based care for adults with serious illness and their family caregivers, with a focus on their potential role in enhancing palliative care delivery.

METHODS: Six databases were searched from inception to August 2025 using terms related to AI, serious illness, home care, self-care, and caregiving. Eligible studies included peer-reviewed empirical research among adults (≥18 years) with serious illness and/or family caregivers, focusing on AI as a tool to support home-based self-care or caregiver contributions to self-care.

RESULTS: Of 1,791 articles screened, 24 met inclusion criteria. Qualitative content analysis identified six themes: (I) personalization and contextual adaptation; (II) multimodal and accessible interfaces; (III) emotional and relational dimensions; (IV) predictive and proactive care; (V) daily routines and care ecosystems; and (VI) equity and access. Personalization emerged as a critical feature, with culturally tailored AI tools improving trust and usability. Limitations of the evidence are that most studies emphasized feasibility, usability, and user experience, over clinical or psychosocial outcomes, limiting insight into AI's real-world impact on palliative care. Evidence was further constrained by heterogeneous designs, language restrictions, and the scarcity of research published in palliative care journals, highlighting the need for more rigorous, context-specific studies.

CONCLUSIONS: Findings underscore AI's capacity to address core components of palliative care, including predicting and managing symptoms and addressing psychosocial needs. However, the evidence base remains early-stage. Future research should prioritize rigorous evaluation of clinical and psychosocial outcomes, along with co-design with patients, caregivers, and clinicians to ensure alignment between AI innovation and core principles of palliative care.

First Page

48

PubMed ID

42273820

Volume

15

Issue

3

Rights

© The Author(s) 2026. Published by Oxford University Press on behalf of Infectious Diseases Society of America. All rights reserved.

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