Evidence map›Paper›PMID 40917542›Full record

ArticleJournal of multidisciplinary healthcare2025

Ethical and Legal Governance of Generative AI in Chinese Healthcare.

Jinrun Jia, Shiqiao Zhao

Abstract read
In one paragraph

Article in Journal of multidisciplinary healthcare, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Artificial Intelligence Across the Drug Development Lifecycle.Medical sciences (Basel, Switzerland) · 2026
    Review
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Jinrun JiaSchool of Law, Xi'an Jiaotong University, Xi'an, Shaanxi Province, People's Republic of China.
Shiqiao ZhaoSchool of Law, Xi'an Jiaotong University, Xi'an, Shaanxi Province, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The application of generative artificial intelligence (AI) technology in the healthcare sector can significantly enhance the efficiency of China's healthcare services. However, risks persist in terms of accuracy, transparency, data privacy, ethics, and bias. These risks are manifested in three key areas: first, the potential erosion of human agency; second, issues of fairness and justice; and third, questions of liability and responsibility. This study reviews and analyzes the legal and regulatory frameworks established in China for the application of generative AI in healthcare, as well as relevant academic literature. Our research findings indicate that while China is actively constructing an ethical and legal governance framework in this field, the regulatory system remains inadequate and faces numerous challenges. These challenges include lagging regulatory rules; an unclear legal status of AI in laws such as the Civil Code; immature standards and regulatory schemes for medical AI training data; and the lack of a coordinated regulatory mechanism among different government departments. In response, this study attempts to establish a governance framework for generative AI in the medical field in China from both legal and ethical perspectives, yielding relevant research findings. Given the latest developments in generative AI in China, it is necessary to address the challenges of its application in the medical field from both ethical and legal perspectives. This includes enhancing algorithm transparency, standardizing medical data management, and promoting AI legislation. As AI technology continues to evolve, more diverse technical models will emerge in the future. This study also proposes that to address potential risks associated with medical AI, efforts should be made to establish a global AI ethics review committee to promote the formation of internationally unified ethical and legal review mechanisms.

Indexed as

ethicsgenerative artificial intelligencehealthcarelawpolicy

Identifiers

PMID40917542
PMCPMC12412760

What Socratic holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.