What Is the Role of AI in Palliative Care Decisions?

By Rasit Dinc

Palliative care, an interdisciplinary field focused on improving the quality of life for patients with serious illnesses, is increasingly intersecting with the world of artificial intelligence (AI). For healthcare professionals, understanding the potential and pitfalls of AI in this sensitive area is crucial. AI is not a replacement for human compassion and clinical judgment, but a powerful tool that can augment decision-making, personalize care, and optimize resource allocation. This article explores the multifaceted role of AI in palliative care decisions, examining its applications, benefits, and the critical ethical considerations that must be addressed.

One of the most significant contributions of AI in palliative care is its ability to enhance prognostication and identify patients who could benefit from early intervention. Machine learning (ML) models can analyze vast electronic health records (EHRs) to identify complex patterns that may be imperceptible to human clinicians [1]. These algorithms can predict mortality risk with increasing accuracy, enabling care teams to initiate timely conversations about end-of-life preferences and goals of care [2]. By flagging patients who are at high risk for clinical deterioration, AI-driven tools can facilitate earlier referrals to palliative care services, ensuring that support is provided not just in the final days of life, but as an integrated part of managing a serious illness.

Beyond prognostication, AI offers sophisticated tools for optimizing symptom management and personalizing treatment plans. Natural Language Processing (NLP), a subset of AI, can scan through clinical notes and patient communications to detect signs of psychological distress, pain, or other symptoms that might otherwise be overlooked [3]. This allows for more proactive and responsive symptom control. Furthermore, AI can help tailor care plans by analyzing a patient's unique clinical data, preferences, and social determinants of health to recommend the most effective interventions. This data-driven approach moves beyond a one-size-fits-all model, promoting a more individualized and effective standard of care that aligns with the patient's specific needs and values.

However, the integration of AI into palliative care is not without significant ethical challenges. One of the primary concerns is algorithmic bias. If the data used to train AI models is not representative of diverse populations, the resulting predictions can perpetuate and even amplify existing health disparities [3]. There are also concerns regarding patient autonomy and the "black box" nature of some complex algorithms, where it is difficult to understand the exact reasoning behind a specific prediction [4]. This lack of transparency can erode trust between clinicians and technology, and more importantly, between clinicians and patients. Ensuring data privacy and maintaining the humanistic core of palliative care in an increasingly automated environment are paramount.

In conclusion, artificial intelligence holds immense promise for transforming palliative care by providing powerful decision support tools, enhancing prognostic accuracy, and enabling highly personalized patient care. It can empower healthcare professionals to make more informed, timely, and effective decisions. However, the adoption of these technologies must be approached with caution and a strong ethical framework. It is essential for the healthcare community to engage in multidisciplinary collaboration to develop transparent, equitable, and validated AI tools that augment, rather than replace, the compassionate, patient-centered communication that defines high-quality palliative care. The future of AI in this field depends on our ability to harness its power responsibly, always prioritizing the dignity and well-being of the patient.

References

[1] Pan, M., Xiong, Y., Liu, H., Li, N., Peng, H., Liang, Y., Gu, W., & Liu, H. (2025). Application of artificial intelligence in palliative care: a bibliometric analysis of research hotspots and trends. Frontiers in Medicine, 12. https://doi.org/10.3389/fmed.2025.1597195

[2] Wilson, P. M., Ramar, P., Philpot, L. M., Soleimani, J., et al. (2023). Effect of an artificial intelligence decision support tool on palliative care referral in hospitalized patients: a randomized clinical trial. Journal of Pain and Symptom Management, 66(3), 227-235.e1. https://doi.org/10.1016/j.jpainsymman.2023.05.013

[3] Oh, O., Ulrich, C. M., & Demiris, G. (2025). The ethical dimensions of utilizing Artificial Intelligence in palliative care. Nursing Ethics. https://penntoday.upenn.edu/news/nursing-oonjee-oh-palliative-care-artificial-intelligence

[4] Nikoloudi, M., & Mystakidou, K. (2025). Artificial Intelligence in Palliative Care: A Scoping Review of Current Applications, Challenges, and Future Directions. American Journal of Hospice and Palliative Medicine®, 10499091251358379. https://doi.org/10.1177/10499091251358379