Discover 4 peer-reviewed studies in Healthcare (2024–2025). Explore research findings powered by Prolific's diverse participant panel.
This page lists 4 peer-reviewed papers in the research area of Healthcare in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.
-
Authors: S Shekar, P Pataranutaporn, C Sarabu, GA Cecchi
Year: 2025
Published in: NEJM AI, 2025 - ai.nejm.org
Institution: MIT Media Lab, IBM Research, Stanford University, Massachusetts Institute of Technology
Research Area: AI Ethics, Healthcare, Patient Trust, Medical Misinformation
Discipline: Artificial Intelligence, Human-Computer Interaction (HCI), AI Ethics
This paper discusses a study by MIT researchers detailing patient trust in AI-generated medical advice, even when that advice is incorrect, raising concerns about misinformation in healthcare.
Citations: 19
-
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
-
Authors: Mukund Telukunta, Venkata Sriram Siddhardh Nadendla, Morgan Stuart, Casey Canfield
Year: 2025
Published in: ArXiv
Institution: Missouri University of Science and Technology, United Network for Organ Sharing
Research Area: Algorithmic Fairness, Healthcare AI, Decision-Making
Discipline: Artificial Intelligence
The study investigates fairness in regression-based predictive models for kidney transplantation, introducing three group fairness notions and identifying social preferences for fairness criteria, revealing biases against age groups but fairness towards gender and race groups.
Methods: Three novel fairness notions (independence, separation, sufficiency) were introduced alongside crowd feedback analysis through a Mixed-Logit discrete choice model.
Key Findings: Fairness in regression-based predictive analytics regarding group fairness criteria across social dimensions such as age, gender, and race.
Sample Size: 85
-
Authors: M Reis, F Reis, W Kunde
Year: 2024
Published in: Nature Medicine, 2024 - nature.com
Institution: University of Cambridge, Julius Maximilians Universität
Research Area: AI in Healthcare, Medical Ethics, Cognitive Psychology, Human-Computer Interaction (HCI) in Medicine
Discipline: AI in Healthcare, Medical Ethics, Cognitive Psychology
The study found that medical advice labeled as being sourced from AI (or AI supervised by humans) is perceived as less reliable and empathetic compared to advice labeled as originating solely from a human physician, resulting in reduced willingness to follow such advice.
Methods: Two preregistered studies were conducted where participants were presented with identical medical advice scenarios but with manipulated labels for the advice source ('AI', 'human physician', 'human+AI').
Key Findings: Participants' perceptions of reliability, empathy, and willingness to follow medical advice based on the perceived source.
Citations: 78
Sample Size: 2280