How Does AI Support Palliative Care in Oncology?

By Rasit Dinc

Palliative care in oncology is dedicated to improving the quality of life for patients and their families facing the challenges of a cancer diagnosis. Its primary goal is to prevent and relieve suffering through the early identification, assessment, and treatment of pain and other physical, psychosocial, and spiritual problems [1]. In recent years, the integration of Artificial Intelligence (AI) has started to revolutionize this field, offering unprecedented opportunities to enhance patient care, support clinical decision-making, and streamline complex healthcare processes. For health professionals, understanding the capabilities and potential of AI is becoming crucial for delivering forward-thinking, patient-centered care.

AI, particularly through its subfields of machine learning (ML) and natural language processing (NLP), excels at analyzing vast and complex datasets. In oncology, this includes electronic health records (EHRs), genomic data, imaging results, and patient-reported outcomes. By identifying patterns and making predictions that are often beyond human capability, AI provides powerful tools to support the core tenets of palliative care.

Enhancing Prognostication and Early Identification

One of the most significant contributions of AI in palliative oncology is in prognostication—predicting a patient's disease trajectory and survival time. Accurate prognostication is essential for timely goals-of-care conversations and ensuring that palliative interventions are introduced when they can be most beneficial. Machine learning models can analyze thousands of variables within a patient's record to identify individuals at high risk of mortality or clinical deterioration within a specific timeframe, such as 6 to 12 months [2].

For instance, algorithm-based approaches have been shown to improve access to palliative care by automatically flagging patients who meet certain criteria for a specialist consultation [3]. This data-driven method helps overcome referral biases and ensures that patients who could benefit from palliative support are not overlooked, facilitating earlier and more effective interventions.

Personalized Symptom Management

Symptom control is a cornerstone of palliative care. AI algorithms can help create more personalized symptom management plans by predicting the likelihood of issues like pain, nausea, or fatigue based on a patient's clinical data. ML models have been developed to predict which patients are most likely to experience severe symptoms, allowing clinicians to intervene proactively [4].

Furthermore, NLP can analyze clinical notes and patient communications to detect subtle expressions of distress or undocumented symptoms, providing a more holistic view of the patient's experience. This enables healthcare teams to tailor treatments more precisely and respond to patient needs in real-time, significantly improving their quality of life.

Supporting Clinical Decision-Making and Reducing Workload

For clinicians, the administrative burden of documentation and data review can be overwhelming. AI tools are being developed to automate routine tasks, such as summarizing patient histories or extracting relevant information from lengthy EHRs. This frees up valuable time for clinicians to focus on direct patient interaction and complex decision-making [5].

AI-powered systems can also act as decision-support tools, providing evidence-based recommendations for treatment pathways or palliative care strategies. By synthesizing the latest research and clinical trial data, these tools can help clinicians stay abreast of best practices and make more informed choices that align with the patient's goals and preferences [6].

Challenges and the Path Forward

Despite its immense potential, the application of AI in palliative care is still in its early stages and faces several challenges. These include the need for high-quality, unbiased data to train the models, the ethical implications of AI-driven prognostication, and the importance of maintaining the human element in end-of-life care discussions. The integration of AI should augment, not replace, the compassionate, human-centered care that is the hallmark of the palliative field [7].

Looking ahead, the synergy between AI and palliative oncology holds the promise of a more predictive, personalized, and proactive approach to care. As these technologies mature and become more integrated into clinical workflows, they will undoubtedly become indispensable tools for health professionals dedicated to enhancing the quality of life for patients with cancer.

References

[1] World Health Organization. (2020). Palliative Care. https://www.who.int/news-room/fact-sheets/detail/palliative-care [2] Hassan, A. (n.d.). Advances in Precision Supportive and Palliative Care. British Journal of Cancer Research. https://britishjournalofcancerresearch.com/advances-in-precision-supportive-and-palliative-care-integrating-biomarkers-digital-health-and-ai-to-personalize-symptom-management-in-cancer-patients [3] Emory Winship Cancer Institute. (2025). Algorithm-based approach improves access to palliative care. https://winshipcancer.emory.edu/newsroom/articles/2025/algorithm-based-approach-improves-access-to-palliative-care-for-patients-with-cancer.php [4] Guo, J., et al. (2025). Machine learning model for prediction of palliative care phases in patients with advanced cancer: a retrospective study. BMC Palliative Care. https://pmc.ncbi.nlm.nih.gov/articles/PMC12102986/ [5] Reddy, V., et al. (2023). Recent advances in artificial intelligence applications for supportive and palliative care in cancer patients. Current Opinion in Supportive and Palliative Care. https://pubmed.ncbi.nlm.nih.gov/37039590/ [6] Gajra, A., et al. (2022). Impact of augmented intelligence on utilization of palliative care services in a real-world oncology setting. JCO Oncology Practice. https://ascopubs.org/doi/abs/10.1200/op.21.00179 [7] Bozkurt, S., et al. (2025). AI in Palliative Care: A Scoping Review of Foundational and Translational Research. Journal of Pain and Symptom Management. https://www.jpsmjournal.com/article/S0885-3924(25)00783-3/abstract