Evidence map›Paper›PMID 39259922›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2024

Artificial intelligence for optimizing recruitment and retention in clinical trials: a scoping review.

Xiaoran Lu, Chen Yang, Lu Liang, Guanyu Hu, Ziyi Zhong, Zihao Jiang

Erratum issuedAbstract readScoping Review
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

28 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Review
  5. Review
  6. Review
  7. AI for clinical trials in oncology.ESMO real world data and digital oncology · 2026
    Review
  8. The potential of artificial intelligence in clinical trials.European journal of clinical investigation · 2026
    Review
  9. Article
  10. Observational
  11. Review
  12. Review
  13. Article
  14. Review
  15. Article
  16. Article
  17. Article
  18. Article
  19. Review
  20. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Xiaoran LuDepartment of Philosophy, School of the Art, University of Liverpool, Liverpool L69 3BX, United Kingdom.ORCID 0009-0008-5835-1192
Chen YangDepartment of Philosophy, School of Humanities, Central South University, Changsha, Hunan 410075, P.R. China.
Lu LiangDepartment of Philosophy, School of Humanities, Central South University, Changsha, Hunan 410075, P.R. China.
Guanyu HuSchool of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, Shanxi 710049, P.R. China.
Ziyi ZhongInstitute of Life Course and Medical Sciences, University of Liverpool, Liverpool L69 3BX, United Kingdom.
Zihao JiangSchool of Marxism, Shenzhen Polytechnic University, Shenzhen, Guangdong 518055, P.R. China.

Funding

Central South University Graduate Independent Innovation 2022ZZTS0333Central South University Graduate Independent Innovation Project 2022ZZTS0333
6 · The paper itself

Abstract

objectiveThe objective of our research is to conduct a comprehensive review that aims to systematically map, describe, and summarize the current utilization of artificial intelligence (AI) in the recruitment and retention of participants in clinical trials. MATERIALS AND

methodsA comprehensive electronic search was conducted using the search strategy developed by the authors. The search encompassed research published in English, without any time limitations, which utilizes AI in the recruitment process of clinical trials. Data extraction was performed using a data charting table, which included publication details, study design, and specific outcomes/results.

resultsThe search yielded 5731 articles, of which 51 were included. All the studies were designed specifically for optimizing recruitment in clinical trials and were published between 2004 and 2023. Oncology was the most covered clinical area. Applying AI to recruitment in clinical trials has demonstrated several positive outcomes, such as increasing efficiency, cost savings, improving recruitment, accuracy, patient satisfaction, and creating user-friendly interfaces. It also raises various technical and ethical issues, such as limited quantity and quality of sample size, privacy, data security, transparency, discrimination, and selection bias. DISCUSSION AND

conclusionWhile AI holds promise for optimizing recruitment in clinical trials, its effectiveness requires further validation. Future research should focus on using valid and standardized outcome measures, methodologically improving the rigor of the research carried out.

Indexed as

Artificial IntelligenceClinical Trials as TopicPatient SelectionHumansartificial intelligencebenefits and drawbacksclinical trialsrecruitment and retentionscoping review

Identifiers

PMID39259922
PMCPMC11491624

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.