Ethical challenges and countermeasures of generative artificial intelligence in medical informed consent： a case study of Chat Generative Pre-trained Transformer
10.12026/j.issn.1001-8565.2026.03.04
- VernacularTitle:生成式人工智能用于医学知情同意中的伦理挑战及对策——以ChatGPT为例
- Author:
Yongqi REN
1
;
Mengyuan LI
2
;
Xing LIU
3
;
Xiaomin WANG
1
Author Information
1. Research Center for Clinical Trials， the Third Xiangya Hospital of Central South University， Changsha 410000， China
2. School of Humanities， Central South University， Changsha 410012， China
3. Medical Ethics Committee， Xiangya Hospital of Central South University， Changsha 410008， China
- Publication Type:Journal Article
- Keywords:
generative artificial intelligence;
Chat Generative Pre-trained Transformer， informed consent;
ethical challenge
- From:
Chinese Medical Ethics
2026;39(3):307-313
- CountryChina
- Language:Chinese
-
Abstract:
Informed consent constitutes a fundamental ethical principle in medical practice. With the in-depth integration of generative artificial intelligence （AI） represented by Chat Generative Pre-trained Transformer （ChatGPT） with medicine， it has brought revolutionary development to traditional informed consent while also introducing new ethical challenges. ChatGPT offers features such as improving the readability of informed consent content， enhancing its comprehensiveness and accuracy， and increasing the convenience of obtaining informed consent. However， as the application of ChatGPT in informed consent is still in the exploratory stage， it is imperative to proactively and fully consider the accompanying ethical issues， such as information security， liability determination， transparency， and fairness. This paper conducted an ethical analysis on the challenges faced by generative AI， represented by ChatGPT， in the application of informed consent and proposed countermeasures， such as upholding free and fully informed consent， strengthening the balance of rights and obligations in informed consent， and establishing a transparent and fair supervision mechanism. The aim was to promote the ethically compliant， orderly， and controllable development of generative AI in the field of medical informed consent.
