Exploring trust factors in AI-healthcare integration: a rapid review
Bibliographic record
Abstract
This rapid review explores how artificial intelligence (AI) is integrated into healthcare and examines the factors influencing trust between users and AI systems. By systematically identifying trust-related determinants, this review provides actionable insights to support effective AI adoption in clinical settings. A comprehensive search of MEDLINE (Ovid), Embase (Ovid), and CINAHL (Ebsco) using keywords related to AI, healthcare, and trust yielded 872 unique citations, of which 40 studies met the inclusion criteria after screening. Three core themes were identified. AI literacy highlights the importance of user understanding of AI inputs, processes, and outputs in fostering trust among patients and clinicians. AI psychology reflects demographic and experiential influences on trust, such as age, gender, and prior AI exposure. AI utility emphasizes perceived usefulness, system efficiency, and integration within clinical workflows. Additional considerations include anthropomorphism, privacy and security concerns, and trust-repair mechanisms following system errors, particularly in high-risk clinical contexts. Overall, this review advances the understanding of trustworthy AI in healthcare and offers guidance for future implementation strategies and policy development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".