Artificial Intelligence in Supportive Oncology and Symptom Management Opportunities

Artificial intelligence (AI) is rapidly transforming medical care, including in oncology, offering promising avenues for enhancing supportive care and symptom management. This review synthesizes current research on AI applications in this critical domain, exploring its potential to personalize interventions and improve patient-reported outcomes in oncology supportive care. We examine AI-driven tools for symptom monitoring, predictive analytics for adverse events, and personalized supportive care recommendations. Emphasis is placed on the integration of machine learning algorithms for real-time data analysis, enabling proactive interventions and timely symptom relief. We highlight challenges in translating AI-based solutions into clinical practice, including data privacy, algorithm bias, applicability for all patients, and the need for rigorous validation studies. Ultimately, the integration of AI in supportive oncology holds the potential to revolutionize patient-centered care, optimizing symptom control and improving the quality of life for individuals facing cancer.

Authors

Institutions

Publication Details

Journal
The Cancer Journal
Published
2025-11-01
DOI
https://doi.org/10.1097/ppo.0000000000000800
Citations
1
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.65
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Artificial Intelligence in Supportive Oncology and Symptom Management Opportunities

E. Kim, Krisstina Gowin, Finly Zachariah, Anne Reb et al.
1 citations
The Cancer Journal
Artificial Intelligence in Healthcare and Education
0.65
article

Artificial Intelligence in Supportive Oncology and Symptom Management Opportunities

E. Kim, Krisstina Gowin, Finly Zachariah, Anne Reb, Richard T. Lee, D P S SANDHU, Erica Veguilla
article en
1 citations

Abstract

Artificial intelligence (AI) is rapidly transforming medical care, including in oncology, offering promising avenues for enhancing supportive care and symptom management. This review synthesizes current research on AI applications in this critical domain, exploring its potential to personalize interventions and improve patient-reported outcomes in oncology supportive care. We examine AI-driven tools for symptom monitoring, predictive analytics for adverse events, and personalized supportive care recommendations. Emphasis is placed on the integration of machine learning algorithms for real-time data analysis, enabling proactive interventions and timely symptom relief. We highlight challenges in translating AI-based solutions into clinical practice, including data privacy, algorithm bias, applicability for all patients, and the need for rigorous validation studies. Ultimately, the integration of AI in supportive oncology holds the potential to revolutionize patient-centered care, optimizing symptom control and improving the quality of life for individuals facing cancer.

The Cancer JournalVol. 31(6)
City Of Hope National Medical Center (US), University School (US), Case Western Reserve University (US)
Openalex Percentile: Top 20%
Artificial Intelligence in Healthcare and Education
0.65
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Artificial Intelligence in Supportive Oncology and Symptom Management Opportunities — E. Kim, Krisstina Gowin, et al. · The Cancer Journal (2025) | TGRS Research Map | TGRS