The AI-educated patient

Area of Science:

• Medical Informatics
• Artificial Intelligence in Healthcare
• Patient Education

Background:

• Patients increasingly use generative artificial intelligence (GenAI) tools for health information.
• This marks a shift from previous online health searches, with large language models (LLMs) synthesizing information.
• LLMs provide guideline-framed narratives, potentially increasing patient baseline knowledge before clinical encounters.

Purpose of the Study:

• To introduce the concept of the "AI-educated patient."
• To propose a conceptual framework of "soft accountability" arising from informed patients.
• To explore potential impacts of AI-mediated patient education on clinician behavior.

Main Methods:

• Conceptual analysis and framework development.
• Literature review on AI in healthcare and patient engagement.
• Hypothesis generation regarding AI's influence on clinical interactions.

Main Results:

• The rise of the "AI-educated patient" is identified as a significant trend.
• "Soft accountability" is proposed as a novel concept describing patient-driven expectations.
• Potential mechanisms for AI's influence on clinical dynamics are hypothesized.

Conclusions:

• GenAI tools are transforming patient engagement with healthcare information.
• The "AI-educated patient" and "soft accountability" frameworks offer new perspectives on the clinician-patient relationship.
• Addressing risks like AI bias, privacy, and equitable access is crucial for harnessing AI's potential in healthcare.