Evidence map›Paper›PMID 38034843›Full record

ArticleContemporary clinical trials communications2023

Transforming cancer clinical trials: The integral role of artificial intelligence in electronic health records for efficient patient recruitment.

Abdulqadir J Nashwan, Salam Bani Hani

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Contemporary clinical trials communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Article
  2. Article
  3. Scoping review of oncology clinical trials nursing: Roles, competencies, and evolving practice in cancer research.Canadian oncology nursing journal = Revue canadienne de nursing oncologique · 2026
    Article
  4. Canadian oncology nursing journal = Revue canadienne de nursing oncologique · 2026
    Article
  5. Review
  6. Review
  7. Review
  8. Review
  9. Review
  10. Article
  11. Article
  12. Decentralized Clinical Trials at Home: Commentary on Caregivers' Burden.Cancer control : journal of the Moffitt Cancer Center
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Abdulqadir J NashwanDirector of Nursing for Education & Practice Development, Hamad Medical Corporation, Doha, Qatar.
Salam Bani HaniFaculty of Nursing, Nursing Deparment, Irbid National University, Irbid, Jordan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Healthcare is one of the sectors where artificial intelligence (AI) is currently viewed as a crucial driving factor. Patient care, medical research, and clinical trial enrollment could all significantly improve due to AI's incorporation into electronic health records (EHRs). This short communication highlights how AI may improve the recruitment process regarding speed, accuracy, and overall cancer clinical trial efficiency. AI can automate this procedure by utilizing machine learning (ML) algorithms, identifying potential trial participants quickly and precisely. Many challenges could be addressed due to this integration, including data privacy and security that can be resolved through cutting-edge encryption techniques and differential privacy algorithms that ensure data anonymization. Another significant obstacle is the lack of common EHR formats and interoperability that can be addressed by creating a standardized structured layout. Automating and improving recruitment processes with AI may speed up research, increase the effectiveness of clinical trials, and open the door to more specialized cancer treatments.

Indexed as

Artificial intelligenceCancerClinical trialsElectronic health recordsSubject recruitment

Identifiers

PMID38034843
PMCPMC10682526

What OpenQuestion holds

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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.