AI evidence extraction

Assessing the influence of generative artificial intelligence (GenAI) on awareness and behavior in medical research integrity: An online survey study

Xiaoting Peng, Yufeng Cai, Dehua Hu, Yi Guo, Haixia Liu, Xusheng Wu et al. · Accountability in Research · 2025

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

9/10

Relevance

1/4

Quality (LMQS)

Evidence

Citations

0.42

FWCI

Top 10%

Impact

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.1080/08989621.2025.2554696

Methodology & findings

Study design

Cross-sectional online survey of 718 valid responses from Chinese medical researchers using an extended Unified Theory of Acceptance and Use of Technology (UTAUT) model

Sample

N = 718, 1 group

Primary method

Extended Unified Theory of Acceptance and Use of Technology (UTAUT) model (specific statistical tests and software not detailed in abstract)

Main result

The findings reveal that "performance expectancy, effort expectancy, technical environment, trust in technology, and supporting conditions positively influence researchers' awareness of research integrity. Conversely, GenAI anxiety and perceived risks exert a significant negative impact. Furthermore, both supporting conditions and integrity awareness are positively associated with integrity behavior, while GenAI anxiety negatively affects such behavior."

Reports effect sizes.

Research paradigm

Positivist/Quantitative

Author conclusions

"The stakeholders in the medical research ecosystem should develop comprehensive guidelines for the responsible use of GenAI. Emphasis should be placed on optimizing the technical environment, enhancing trust and support structures, and embedding integrity safeguards, thereby promoting the synergistic development of technological innovation and ethical research practices."

Risk of bias

Selection bias: Participants are from China only, limiting generalizability to other geographic/cultural contexts; Respondent bias: Self-reported survey data on integrity awareness and behavior; Self-selection bias: Online survey may preferentially attract more digitally-literate or motivated researchers; Cross-sectional design: Cannot establish causality or temporal relationships; Self-reported survey data (social desirability bias); Cross-sectional design (cannot establish causality); Sample limited to Chinese medical researchers (generalizability concerns); Selection bias (online survey among Chinese medical researchers may not be representative of global medical research population); Self-report bias (survey-based methodology relies on self-reported awareness and behavior); Geographic limitation (Chinese medical researchers only, limiting generalizability)

Open questions raised

Not explicitly stated in abstract. The abstract mentions that "the lack of systematic guidelines for its ethical use underscores the need to investigate GenAI's impact on researchers' awareness and behavior concerning integrity" as background motivation.
The authors identify the need for systematic guidelines for ethical use of GenAI in medical research and emphasize the importance of investigating GenAI's impact on researchers' awareness and behavior concerning integrity.

Data: not_statedCode: not_statedExtracted from: pdfAgreement 72%

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