Z Cui: Researcher — Prolific Citations Library

Explore 2 peer-reviewed studies by Z Cui in Trust in AI and AI-assisted diagnosis (2024–2025). Discover research powered by Prolific's participant panel.

This page lists 2 peer-reviewed papers authored or co-authored by Z Cui in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (2 of 2)

Impact of AI-Assisted Diagnosis on American Patients' Trust in and Intention to Seek Help From Health Care Professionals: Randomized, Web-Based Survey ...

Authors: C Chen, Z Cui

Year: 2025

Published in: Journal of Medical Internet Research, 2025 - jmir.org

Institution: Medical College of Wisconsin

Research Area: Trust in AI, AI-assisted diagnosis, Health communication, Healthcare human-AI interaction

Discipline: Digital Health, Human-Computer Interaction (HCI), Behavioral Science

Patients trust and are more likely to seek help from doctors explicitly avoiding AI-assisted diagnosis rather than those using extensive or moderate AI, highlighting a strong aversion to AI in healthcare settings.

Methods: A randomized, web-based 4-group survey experiment was conducted with controls for sociodemographic factors and analysis using regression, mediation, and moderation techniques.

Key Findings: Trust in and intention to seek medical help from health care professionals using AI-assisted diagnosis versus those avoiding AI, and the influence of demographic, social, and experiential factors.

DOI: https://doi.org/10.2196/66083

Citations: 4

Sample Size: 1762
Can AI replace human subjects? a large-scale replication of psychological experiments with LLMs

Authors: Z Cui, N Li, H Zhou

Year: 2024

Published in: A Large-Scale Replication of Psychological ..., 2024 - papers.ssrn.com

Institution: Harbin Institute of Technology at Weihai

Research Area: LLM replication of psychological experiments, Social Science Research Methods, Artificial Intelligence, Psychology

Discipline: Psychological Science

Large Language Models (LLMs) like GPT-4 successfully replicate 76% of main effects and 47% of interaction effects from 154 psychological experiments, but exhibit overestimation and potential false positives, highlighting their complementary role rather than full replacement of human subjects.

Methods: Replication of 154 psychological experiments from top social science journals using GPT-4 as a simulated participant to measure main effects and interaction effects.

Key Findings: The ability of GPT-4 to replicate human responses in psychological experiments and the extent to which it produces similar results in terms of effect direction, significance, and confidence intervals.

Citations: 29

Sample Size: 154