The Role of Artificial Intelligence in Palliative Care: A Scoping Review

Authors

  • Zahra Mansoor Samaei School of Nursing and Midwifery, Tehran University of Medical Sciences (TUMS), Tehran, Iran
  • Fatemeh Mansoor Samaei School of Nursing and Midwifery, Tehran University of Medical Sciences (TUMS), Tehran, Iran
  • Fathiyeh Bahramnejad Medical Informatics, Sirjan School of Medical Sciences, Sirjan, Kerman, Iran
  • Mahmoud Shiri Kahnouei School of Advanced Technologies in Medicine, Medical Nanotechnology and Tissue Engineering Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
  • Fatemeh Bahramnezhad Department of ICU Nursing, School of Nursing & Midwifery, Nursing and Midwifery Care Research Center, Tehran University of Medical Sciences, Tehran, Iran

DOI:

https://doi.org/10.18502/jimc.v9i4.22448

Keywords:

Artificial intelligence, Decision support, Machine learning, Palliative care, Symptom management, Scoping review

Abstract

Palliative care is a holistic, multidisciplinary approach aimed at improving the quality of life for patients with life-limiting illnesses and their families. Despite the global need for these services, many patients—especially in low-income countries—lack adequate access. Artificial Intelligence (AI) has emerged as a promising tool to enhance palliative care delivery by supporting early identification of patient needs, optimizing clinical decision-making, and improving patient outcomes. This scoping review examined the applications of AI in palliative care. A systematic search was conducted in PubMed, Scopus, and Web of Science up to June 2025, ultimately identifying 8 empirical studies that met the inclusion criteria. These studies were carried out across 5 countries and involved diverse patient populations, including advanced cancer, organ failure, dementia, and traumatic brain injury.

AI applications were categorized into five main areas: (1) evaluating chatbot responses (e.g., ChatGPT, Gemini, Copilot), which revealed inadequate readability and quality; (2) identifying communication silences in patient-provider conversations using machine learning; (3) supporting clinical documentation and decision-making to reduce clinician workload; (4) analyzing inequalities in access to palliative care through predictive algorithms; and (5) predicting disease progression and patient status using longitudinal symptom data. The findings indicate that AI holds significant potential to improve symptom management, support clinical decisions, and enable data-driven interventions in palliative care. However, several challenges must be addressed for sustainable and effective implementation, including poor readability of AI-generated content, ethical concerns, patient privacy issues, and disparities in access to technology. Future research should focus on broader and more diverse populations, integrate survival prediction models, and carefully consider the ethical, regulatory, and organizational dimensions of AI integration into palliative care practice.

Published

2026-09-01

Issue

Section

Articles