ArticleBMC medical ethics2025
Trust transfer from medical AI to doctors and hospitals: Integrating digital, AI, and scientific literacy in a cross-sectional framework.
Article in BMC medical ethics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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.
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.
Who cites it
8 citing papers in PubMed.
- AI-Augmented Teach-Back in Dentistry: From Patient Education to Verified Clinical Understanding.Cureus · 2026Review
- Ethical Considerations in Personal Health Large Language Models.Journal of medical Internet research · 2026Article
- AI Chatbots vs. Traditional Sources: Dental Health Literacy and Confidence Among Dental Patients-A Cross-Sectional Study.International dental journal · 2026Article
- Determinants of the Public's Behavioral Intention to Adopt AI-Assisted Lung Cancer Screening: An Extended UTAUT Model Integrating Trust and Risk.Healthcare (Basel, Switzerland) · 2026Article
- Patient perspectives on the use of artificial intelligence to support treatment decision making in renal cancer: findings from the KATY project.BMC medical informatics and decision making · 2026Article
- A double-edged impact of AI literacy on consumers' willingness to use AI-powered products: a parallel mediation model of perceived AI risk and perceived social good.Frontiers in psychology · 2026Article
- The double-edged sword of generative AI in dermatology: a multi-component cross-sectional study on physician burnout, patient satisfaction, and communication quality.Frontiers in medicine · 2026Article
- Digital self-service in hospitals and patient treatment barriers: The role of organizational trust via dual mechanisms.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
This study investigates how different forms of literacy shape trust in medical AI and its transfer in healthcare contexts. Based on a survey of 1,250 participants, three findings emerge. First, digital literacy and AI literacy exert opposite influences on medical AI trust: while digital literacy enhances trust, higher AI literacy unexpectedly reduces it. This paradox highlights a theoretical puzzle in technology acceptance, suggesting that deeper knowledge can generate informed skepticism rather than blind confidence. Second, trust in medical AI transfers hierarchically, flowing to hospitals only through physician trust as a critical intermediary, underscoring the role of interpersonal trust in institutional trust building. Third, scientific literacy moderates this process, with higher literacy dampening trust transfer, reflecting the impact of cognitive processing differences. These results extend theories of trust and technology acceptance by integrating multiple literacies and uncovering divergent cognitive pathways. Practically, they call for communication strategies and policy designs that calibrate trust-strengthening physicians' role as trust brokers, balancing education about AI's capacities and risks, and leveraging explainable AI tools to sustain appropriate confidence in medical AI.
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Registered trials
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.