ArticleEthics & human research
Natural Language Processing in Clinical Research Recruitment: A Scoping Review Enriched with Stakeholder Insights.
Article in Ethics & human research. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- From Invisible to Enrolled: How Artificial Intelligence Is Reshaping Clinical Trial Recruitment.Pharmaceutical medicine · 2026Article
- Embracing the Digital Revolution: How Artificial Intelligence is Transforming Clinical Trials in Older Participants.Drugs & aging · 2026Review
- Natural Language Processing in Clinical Research Recruitment: A Scoping Review Enriched with Stakeholder Insights.Ethics & human researchArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We conducted a scoping review to characterize natural language processing (NLP) applications in clinical trials recruitment and conducted semistructured interviews to obtain stakeholders' perspectives on these technologies, with a focus on ethical considerations. The scoping review focused on English-language original articles published from January 2021 to June 2024, sourced from Ovid Medline. Data extracted included the characteristics of NLP systems, their evaluations, and ethical considerations regarding patient autonomy and equity. Additionally, semistructured interviews with experts from various specialties were conducted, and the data were analyzed using thematic analysis. Most of the 47 eligible articles focused on NLP models for electronic health records screening. The literature predominantly emphasized the models' accuracy and efficiency, while ethical considerations received little attention. Interview findings underscored the need for more ethical reflection and real-world implementation analysis, revealing differing opinions on anonymization, consent, and the impact of NLP tools on fair opportunities. NLP applications for participant recruitment in clinical research are in early stages, with a gap between ethical discourse and reporting in current literature. Practical guidelines are needed for implementing and reporting ethical aspects throughout the lifecycle of NLP applications, along with empirical research to assess their ethical impact.
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What OpenQuestion holds
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.