Implications of Large Language Models for Clinical Practice: Ethical Analysis Through the Principlism Framework

Area of Science:

• Medical Ethics
• Artificial Intelligence in Healthcare
• Clinical Informatics

Background:

• Generative artificial intelligence (AI), specifically large language models (LLMs), are increasingly explored for medical and healthcare applications.
• Practitioners and researchers are investigating the potential of LLMs to transform healthcare delivery.

Purpose of the Study:

• To examine the ethical implications of using LLMs in clinical practice.
• To evaluate LLM integration within the ethical framework of principlism.

Main Methods:

• Analysis of ethical principles: beneficence, non-maleficence, autonomy, and justice.
• Consideration of LLM impact on patient care, practitioner responsibilities, and health equity.

Main Results:

• LLMs can enhance patient outcomes via administrative support and direct clinical insights, aligning with beneficence.
• Potential patient harm necessitates risk mitigation for non-maleficence; informed patient consent and alternatives are crucial for autonomy.
• LLMs can promote care standardization, mitigate bias, and address health disparities, supporting justice.

Conclusions:

• A strong ethical case exists for integrating LLMs into clinical practice.
• Mitigating risks of patient harm may make LLM incorporation ethically imperative under principlism.