Palliative care helps improve the quality of life for patients with serious illnesses. It deals with pain, symptoms, and emotional needs. AI can help by looking at lots of medical data fast and assisting in making decisions. It can create treatment plans just for each patient and predict how symptoms may change. AI can also handle some routine tasks so healthcare workers have more time to talk with patients, which is very important in end-of-life care.
A study in the Journal of Medicine, Surgery, and Public Health by Abiodun Adegbesan and others shows that AI can reduce the workload while making care more personal. In the U.S., where many kinds of patients need different care, AI systems that help with tasks like answering phones are becoming useful. These tools help make communication easier and help patients get care when they need it.
Doctors and healthcare workers agree on some key ethical rules to guide AI use in palliative care. These rules protect patients’ rights and keep care human:
These rules help build policies to use AI responsibly. The Journal of Medicine, Surgery, and Public Health says it is important to have regular ethics checks and clear AI designs that follow these principles.
Even with clear rules, some problems still come up. The main challenges include:
One idea researchers suggest, including Abiodun Adegbesan’s team, is Explainable AI (XAI). XAI shows clearly how AI makes decisions to doctors and patients. This helps build trust and makes sure people understand why AI suggests certain actions.
For example, if AI recommends changing a pain medicine dose, XAI can explain the reasons behind that suggestion. This helps patients and doctors decide with confidence.
Transparency helps meet ethical standards by:
Healthcare leaders should pick AI systems with XAI and train their teams to understand AI results well.
Using AI in palliative care ethically needs teamwork from many experts. These include doctors, ethics specialists, IT staff, lawyers, and patient advocates. This helps make sure AI systems are:
Working together like this helps build and review AI tools that meet the needs of palliative care in the U.S.
AI has challenges but also can improve important work tasks in palliative care. Automated workflows can help in several ways:
AI improves efficiency and care quality while respecting patient independence by keeping clinicians in charge.
Fair access is very important when adding AI to palliative care. The U.S. has many different kinds of patients with various incomes, races, languages, and healthcare access. AI tools should not increase existing gaps. Ways to promote fairness include:
Healthcare leaders should work with tech companies and policymakers to use these strategies.
Protecting patient privacy is very important, especially in end-of-life care where data is sensitive. HIPAA sets basic privacy rules in the U.S., but AI brings new challenges. Healthcare managers and IT staff should focus on:
Being careful with privacy helps keep patient trust and meet legal requirements.
Regular ethical audits are needed to find and fix new risks from AI in palliative care. These audits look at:
Frequent reviews also help improve AI as technology and care needs change. Healthcare administrators should think about setting up teams from different fields to do these audits regularly.
Respect for patient dignity is at the core of ethical AI use in palliative care. AI should never treat patients just as data or algorithms. Instead, AI must support doctors and nurses in giving care that respects patient values, choices, and cultures.
Clear communication, explainable AI, good consent processes, and cultural awareness are all needed to make sure AI helps meet the full needs of patients in the U.S. Healthcare leaders have an important job choosing and managing AI tools that follow these goals.
AI use in palliative care brings both chances and duties for healthcare groups in the U.S. By basing AI use on ethical rules like autonomy, beneficence, non-maleficence, and justice, and by working across fields with ongoing review, medical administrators and IT leaders can help AI improve care without harming patient dignity or trust. Tools for automating workflows, such as those from Simbo AI, show how technology can make operations smoother and communication better, helping patients, caregivers, and the healthcare system overall.
The key ethical principles include autonomy, beneficence, non-maleficence, and justice. These principles guide ensuring patients’ rights are respected, care is beneficial and non-harmful, and access to AI technology is fair and equitable.
Major challenges include ensuring informed consent, protecting data privacy, avoiding algorithmic bias, and preventing depersonalization of care, which may reduce the human touch essential in palliative settings.
AI can reduce healthcare provider burden by supporting decision-making and personalizing patient care, allowing providers to focus more on compassionate aspects while AI handles data-heavy tasks.
Low-resource settings face intensified ethical challenges, including limited infrastructure, lack of regulatory frameworks, and inequitable access to necessary AI technologies, increasing risks related to bias, privacy, and quality of care.
Recommendations include promoting transparency with explainable AI (XAI), conducting regular ethical audits, developing culturally sensitive and context-specific guidelines, and fostering interdisciplinary collaboration for ethical AI system design.
XAI increases transparency and accountability by making AI decision processes understandable to clinicians and patients, helping maintain trust and ensuring decisions align with ethical standards and patient dignity.
Patient dignity must remain central, ensuring AI supports compassionate care without reducing patients to data points, thus preserving respect, empathy, and individualized attention throughout palliative care.
Algorithmic bias can lead to unfair treatment recommendations or resource allocation, disadvantaging certain patient groups and worsening healthcare disparities, especially in sensitive end-of-life care scenarios.
Interdisciplinary collaboration ensures AI systems respect diverse cultural contexts, medical ethics, and technological standards, fostering balanced development that aligns with patient needs and healthcare provider expertise.
Future efforts should prioritize ethical frameworks, equitable access, culturally sensitive guidelines, transparency measures, and robust privacy protections to ensure AI enhances rather than undermines compassionate end-of-life care.
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