A primer on artificial intelligence for palliative care educators

Area of Science:

• Health Professions Education
• Artificial Intelligence in Medicine
• Palliative Care

Background:

• Artificial intelligence (AI) adoption is increasing in health professions education (HPE).
• Existing AI primers lack specificity for palliative care (PC), overlooking its unique sensitivities.
• There is a need for practical guidance tailored to PC educators.

Purpose of the Study:

• To provide a narrative primer for PC educators on the responsible use of AI.
• To bridge the gap in AI education specific to the nuances of palliative care.
• To equip educators with practical guidance for integrating AI into PC curricula.

Main Methods:

• Literature review to inform the primer's content.
• Introduction to foundational AI concepts: machine learning (ML), large language models (LLMs), generative AI (GenAI), and agentic AI.
• Exploration of general HPE and PC-specific AI applications.

Main Results:

• AI applications range from general study support to PC-specific uses like curriculum design and serious illness communication training.
• Key risks include bias, communication integrity issues (hallucination), data privacy, and over-reliance on AI.
• Ethical principles of equity, ethics, and patient-centeredness are crucial for AI implementation in PC.

Conclusions:

• Educators need to build AI literacy and establish governance policies for responsible AI use.
• Verification of AI-generated content against trusted sources is essential.
• An educator-AI partnership can enhance compassionate, patient-centered decision-making and care in PC.