Clinical applications of artificial intelligence in symptom management and decision making in oncologic palliative care: a systematic review

Abstract

Abstract Introduction: Artificial intelligence (AI) is increasingly being integrated into healthcare, offering innovative tools to improve symptom management and support clinical decisionmaking in patients with advanced cancer receiving palliative care (PC). The study aimed to systematically evaluate recent evidence (2021–2024) on the clinical use of AI-based tools for symptom management, prognosis prediction, and clinical decision support in adult oncology patients in PC settings. Methods: A systematic review was conducted following the PRISMA-P 2015 guidelines. Databases searched included PubMed, Scopus, Cochrane Library, BVS, Scielo, and ScienceDirect, using MeSH terms related to AI, cancer, pain, and palliative care. Studies were included if they involved adult oncology patients using AI tools in PC and reported outcomes related to symptom control, clinical decisions, or mortality estimation. Two independent reviewers conducted the selection and methodological quality assessment using STROBE, PRISMA, and CONSORT guidelines. Only studies rated as medium or high quality were included. Results: From an initial pool of 3,018 records, 20 studies were selected. AI applications were grouped into prognosis and mortality prediction (n = 9), symptom identification and monitoring (n = 5), clinical decision support (n = 4), and communication tools (n = 2). Models included neural networks, eXtreme Gradient Boosting (XGBoost), decision trees, natural language processing (NLP), and chatbots. Most studies demonstrated high accuracy in retrospective or real-world clinical settings. Conclusions: AI has shown potential in the early identification of palliative needs, symptom control, and care planning. Prospective validation and implementation studies are needed to ensure ethical and safe integration into palliative care.