ArticleJAMIA open2020
Evaluation of an artificial intelligence clinical trial matching system in Australian lung cancer patients.
Article in JAMIA open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 2 of them syntheses that pooled it.
What it found
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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
41 citing papers in PubMed, 2 syntheses or guidelines pooled it, 82 citations in OpenAlex.
- Use of artificial intelligence for cancer clinical trial enrollment: a systematic review and meta-analysis.Journal of the National Cancer Institute · 2023Pooled it
- A systematic review on natural language processing systems for eligibility prescreening in clinical research.Journal of the American Medical Informatics Association : JAMIA · 2021Pooled it
- TrialTriage, a Semiautonomous Prescreening Workflow for Resolving Ambiguity in Phase I Oncology Trial Eligibility: Development and Proof-of-Concept Study Using Synthetic Cases.JMIR formative research · 2026Article
- Automated algorithm in catheter-associated urinary tract infection identification using digital health records: validity and barriers in implementation into clinical practice.BMC medical informatics and decision making · 2026Article
- Artificial intelligence for clinical trial design, conduct, and analysis: a narrative review.ESMO real world data and digital oncology · 2026Review
- AI-Driven Patient Screening for Clinical Trials in Pancreatic Cancer: The PANCR-AI Pilot Retrospective Comparative Study.JMIR cancer · 2026Article
- Artificial intelligence in clinical trial participant recruitment and retention: A scoping review and meta-analysis.Journal of clinical and translational science · 2026Review
- Computational Framework for Structuring and Analyzing Clinical Trial Criteria for AI-Guided Fine-grained Matching.Journal of medical systems · 2025Article
- Leveraging Artificial Intelligence for Clinical Study Matching: Key Threads for Interweaving Data Science and Implementation Science.JMIR formative research · 2025Article
- Large language models in clinical trials: applications, technical advances, and future directions.BMC medicine · 2025Review
- Finding trials for participants: an ethnographic study of successful recruitment strategies for clinical trials.Trials · 2025Article
- Development and implementation of cancer clinical trial patient screening using an electronic medical record-integrated trial matching system.BMJ health & care informatics · 2025Article
- Natural Language Processing in Support of Evidence-based Medicine: A Scoping Review.Findings of ACL. ACL · 2025Article
- A Thorough Review of the Clinical Applications of Artificial Intelligence in Lung Cancer.Cancers · 2025Review
- The Impact and Performance of the Molecular Tumor Board: Three-Year Activity in Precision Medicine for Treatment of Patients with Cancer from the Marche Region, in Italy.Oncology and therapy · 2025Article
- Hallmarks of artificial intelligence contributions to precision oncology.Nature cancer · 2025Review
- Computational Landscape in Drug Discovery: From AI/ML Models to Translational Application.Scientifica · 2025Review
- A Deployed Online Reinforcement Learning Algorithm In An Oral Health Clinical Trial.Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence · 2025Article
- Bias in machine learning applications to address non-communicable diseases at a population-level: a scoping review.BMC public health · 2024Article
- Artificial intelligence for optimizing recruitment and retention in clinical trials: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe objective of this technical study was to evaluate the performance of an artificial intelligence (AI)-based system for clinical trials matching for a cohort of lung cancer patients in an Australian cancer hospital.
methodsA lung cancer cohort was derived from clinical data from patients attending an Australian cancer hospital. Ten phases I-III clinical trials registered on clinicaltrials.gov and open to lung cancer patients at this institution were utilized for assessments. The trial matching system performance was compared to a gold standard established by clinician consensus for trial eligibility.
resultsThe study included 102 lung cancer patients. The trial matching system evaluated 7252 patient attributes (per patient median 74, range 53-100) against 11 467 individual trial eligibility criteria (per trial median 597, range 243-4132). Median time for the system to run a query and return results was 15.5 s (range 7.2-37.8). In establishing the gold standard, clinician interrater agreement was high (Cohen's kappa 0.70-1.00). On a per-patient basis, the performance of the trial matching system for eligibility was as follows: accuracy, 91.6%; recall (sensitivity), 83.3%; precision (positive predictive value), 76.5%; negative predictive value, 95.7%; and specificity, 93.8%. DISCUSSION AND
conclusionThe AI-based clinical trial matching system allows efficient and reliable screening of cancer patients for clinical trials with 95.7% accuracy for exclusion and 91.6% accuracy for overall eligibility assessment; however, clinician input and oversight are still required. The automated system demonstrates promise as a clinical decision support tool to prescreen a large patient cohort to identify subjects suitable for further assessment.
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Identifiers
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.