Evidence map›Paper›PMID 40692654›Full record

ArticleFrontiers in digital health2025

Ethical dilemmas and the reconstruction of subjectivity in digital mourning in the age of AI: an empirical study on the acceptance intentions of bereaved family members of cancer patients.

Kun Fu, Chenxi Ye, Zeyu Wang, Miaohui Wu, Zhen Liu, Yuan Yuan

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Article in Frontiers in digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
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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

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

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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

6 authors.

Kun Fu *School of Culture and Creativity, Beijing Normal-Hong Kong Baptist University, Zhuhai, Guangdong, China.
Chenxi Ye *School of Culture and Creativity, Beijing Normal-Hong Kong Baptist University, Zhuhai, Guangdong, China.
Zeyu Wang *School of Culture and Creativity, Beijing Normal-Hong Kong Baptist University, Zhuhai, Guangdong, China.
Miaohui WuFaculty of Philosophy, Zhengzhou University, Zhengzhou, Henan, China.
Zhen LiuSchool of Digital Art and Design, Chengdu Neusoft University, Chengdu, Sichuan, China.
Yuan YuanSchool of Culture and Creativity, Beijing Normal-Hong Kong Baptist University, Zhuhai, Guangdong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: With the rapid advancement of AI replication, virtual memorials, and affective computing technologies, digital mourning has emerged as a prevalent mode of psychological reconstruction for families coping with the loss of terminally ill patients. For family members of cancer patients, who often shoulder prolonged caregiving and complex ethical decisions, this process entails not only emotional trauma but also profound ethical dilemmas. Methods: This study adopts the Unified Theory of Acceptance and Use of Technology (UTAUT) as its analytical framework, further integrating Foucauldian subjectivation theory and emotional-cognitive models. A structural path model was constructed to examine how ethical identification and grief perception influence the acceptance of AI-based digital mourning technologies. A total of 129 valid survey responses were collected and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Results: The findings indicate that performance expectancy, effort expectancy, social influence, and ethical concern significantly predict users' intention to adopt digital mourning technologies. Additionally, grief perception not only influences adoption intention but also directly affects actual usage behavior. Discussion: This study highlights that the acceptance of AI-based digital mourning technologies extends beyond instrumental rationality. It is shaped by the interplay of emotional vulnerability and moral tension. The results contribute to a deeper understanding of the ethical and psychological dimensions of posthumous AI applications and provide valuable insights for future human-AI interaction design, digital commemoration systems, and the governance of end-of-life technologies.

Indexed as

AI digital mourningemprical studyethical concerns in AIperception of griefUTAUT model

Identifiers

PMID40692654
PMCPMC12278142

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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 OpenQuestion graph.