Empowering Patient Autonomy: The Role of Large Language Models (LLMs) in Scaffolding Informed Consent in Medical...

Area of Science:

• Bioethics
• Artificial Intelligence
• Medical Informatics

Background:

• Traditional patient autonomy emphasizes independent decision-making.
• Recent philosophy highlights the dependence of autonomous decisions on social and epistemic supports.
• The "scaffolded model" illustrates reliance on distributed cognition and external aids.

Purpose of the Study:

• To explore how Large Language Models (LLMs) can operationalize scaffolded autonomy in medical informed consent.
• To argue that LLMs can enhance, rather than undermine, patient autonomy.
• To examine LLMs as cognitive scaffolds for information processing, value clarification, and culturally sensitive decision-making.

Main Methods:

• Conceptual analysis of scaffolded autonomy and LLM capabilities.
• Application of the scaffolded autonomy model to medical informed consent.
• Examination of clinical practice examples for LLM integration.
• Analysis of implementation challenges through the lens of scaffolded autonomy.

Main Results:

• LLMs can serve as cognitive scaffolds to enhance information accessibility, comprehension, and value clarification in medical consent.
• Appropriately designed LLM systems can support personalized and culturally sensitive decision-making.
• Implementation necessitates careful consideration of epistemic responsibility, choice authenticity, and human oversight.

Conclusions:

• LLMs offer a promising avenue for enhancing patient autonomy in medical informed consent through scaffolded support.
• Successful implementation requires a shift from mere information provision to supporting genuine autonomous decision-making.
• Further research and practical guidelines are needed for ethically integrating LLMs into technologically mediated healthcare environments.