The three-stage dilemma and its legal response in realizing algorithm justice for medical artificial intelligence
10.12026/j.issn.1001-8565.2026.03.01
- VernacularTitle:医疗人工智能算法正义实现的三阶困境及其法治回应
- Author:
Jiaxin CHEN
1
;
Zhuoyu XU
1
;
Yuanlei YUE
1
Author Information
1. School of Medical Humanities， Hubei University of Chinese Medicine， Wuhan 430065， China
- Publication Type:Journal Article
- Keywords:
medical artificial intelligence;
algorithm justice;
informed consent;
fairness in health data
- From:
Chinese Medical Ethics
2026;39(3):279-286
- CountryChina
- Language:Chinese
-
Abstract:
In the period of digital transformation of the medical service model， maintaining algorithm justice for medical artificial intelligence possesses multiple value implications. However， practical dilemmas exist at different stages of achieving this value goal. At the initial stage of diagnosis and treatment decision-making， there is a risk of imbalance in patients’ rights and interests， such as weakened effectiveness of patients’ right to informed consent， as well as low synergy between rights and interests protection and technological innovation. In the process of diagnosis and treatment decision-making， the algorithmic model is constrained by the technological bottleneck in the underlying application， deviating from the legal track of technological neutrality. After the diagnosis and treatment decision-making is generated， it triggers a crisis in the remedy of patients’ rights and interests and breeds the phenomenon of an ambiguous definition of subject qualification and attribution principle. In this regard， it is essential to establish a preemptive safeguard system focusing on the protection of patients’ rights and interests， set technological fairness standards， and shape review norms of technological neutrality. The aim is to ensure the equalization of remedy channels for patients’ rights and interests， promote the fairness in the proportion of responsibilities among multiple subjects， and thereby safeguard the digital justice in the field of intelligent diagnosis and treatments.
