What Are the Ethical, Social, and Psychological Effects of Artificial Intelligence on End-of-Life Care and Perceptions of Death?

24 June 2025 4 5K Report

This question invites exploration into how AI technologies—such as predictive analytics in healthcare, robotic caregivers, and virtual memorials—are influencing the experience of death and dying. It encourages interdisciplinary dialogue on the benefits, challenges, and moral considerations surrounding AI’s role in end-of-life decisions, grief processing, and the broader cultural understanding of mortality.

Imran Zafar

AI in end-of-life care offers clinical support but raises ethical concerns around autonomy, privacy, and responsibility. Socially, it may alter cultural norms and deepen care disparities. Psychologically, it can impact emotional well-being, perceptions of death, and the grieving process.

Budoor Musameh

Artificial intelligence affects end-of-life care on multiple levels. First Socially, it may reduce human interaction and weaken emotional bonds between patients and their families due to reliance on robotic caregivers. Second Ethically, it raises questions about decision-making authority in critical moments and the fairness of algorithms predicting death or recommending treatment withdrawal. other than that, Psychologically, AI interventions may disrupt the grieving process or create unhealthy attachments to virtual entities simulating deceased individuals, complicating emotional acceptance and closure.

Deepa Shukla

The integration of Artificial Intelligence (AI) into end-of-life (EoL) care introduces profound ethical, social, and psychological challenges. AI's role in predictive diagnostics, decision support systems, palliative care recommendations, and even companionship alters how individuals, families, and healthcare providers navigate death, dying, and dignity.

🔍 Ethical Effects:

Autonomy and Decision-Making: AI-driven prognosis models may predict survival time or disease progression, influencing decisions about life-sustaining treatments. Raises concerns about patient autonomy — whether individuals make informed decisions or defer excessively to AI recommendations.

Accuracy vs. Moral Responsibility: Errors in AI predictions can lead to either overtreatment or premature withdrawal of care. The question emerges: Who is morally accountable — the clinician, the AI developer, or the healthcare institution?

Bias and Fairness: AI models trained on biased datasets may exacerbate disparities in EoL care, disproportionately affecting marginalized groups.

Consent and Transparency: Complexity of AI models challenges informed consent, as patients may not fully understand how AI influences their care.

Dignity and Human Touch: Over-reliance on AI could compromise compassionate, human-centered care, reducing patients to data points rather than persons deserving empathy.

🌍 Social Effects:

Changing Family Dynamics: Families may experience confusion or distress when AI outputs contradict their beliefs or the physician’s intuitive judgments.

Trust in Healthcare: Dependence on opaque AI algorithms may erode or alternatively reinforce trust in medical institutions, depending on the perceived reliability and transparency.

Access and Equity: AI-based EoL tools may be available only in technologically advanced settings, widening the digital divide in palliative care.

Cultural Perceptions of Death: AI may inadvertently medicalize death further, reducing culturally embedded rituals, spiritual practices, or traditional approaches to dying.

🧠 Psychological Effects:

Patient Psychological Burden: Knowing AI-generated life expectancy predictions may cause existential anxiety, anticipatory grief, or emotional distress. Alternatively, it may offer psychological relief for patients seeking clarity.

Impact on Clinicians: Moral injury or psychological distress may arise when clinicians feel constrained between AI recommendations and their personal, ethical judgment.

Grief Processing: AI companion systems (e.g., chatbots for terminal patients) may affect how patients process mortality, but also risk emotional detachment or depersonalization.

Perception of Death: AI’s role in quantifying death (survival time predictions, risk scores) may shift death from a spiritual or existential event to a statistical probability, potentially desensitizing or over-rationalizing the dying process.

⚖️ Balancing AI with Ethical EoL Care:

Ethical AI deployment requires: Transparent algorithms and explainability. Human-in-the-loop systems, ensuring clinicians remain the final decision-makers. Robust bias audits and fairness testing. Continuous psychosocial support integrated with AI-driven recommendations.

📚 Key References:

Coeckelbergh, M. (2020). AI Ethics. MIT Press.
Walsh, S., et al. (2021). Artificial intelligence in healthcare: Insights on the ethical and social impact in end-of-life care. Palliative Medicine.
Cave, S., et al. (2021). AI and the meaning of life: Ethical challenges in end-of-life AI applications. Nature Machine Intelligence.

Maryam Andalib

Hello and regards

I think your question could be a good title for a research paper in the field of artificial intelligence and religions.

Why do I say this?

As we know, the quality of artificial intelligence's answers improves with its users, becoming smarter day by day and providing more complete and higher-quality answers.

So, this is a very important issue: when users ask about the perception of death and the end of life, it's essential to consider the region and the religious beliefs they come from, because the perception of death varies across different religions. That means, for instance, if someone wants to research this subject with the help of AI in a secular or Laicus country, their results will be entirely different from someone conducting the same research in a Muslim country. Your question asks about the effects in great detail, and answering it properly requires thorough and precise research.

Wishing the beautiful wishes of the people of the world

M.A

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