Use of artificial intelligence for cancer clinical trial enrollment: a systematic review and meta-analysis.
J Natl Cancer Inst
; 115(4): 365-374, 2023 04 11.
Article
em En
| MEDLINE
| ID: mdl-36688707
ABSTRACT
BACKGROUND:
The aim of this study is to provide a comprehensive understanding of the current landscape of artificial intelligence (AI) for cancer clinical trial enrollment and its predictive accuracy in identifying eligible patients for inclusion in such trials.METHODS:
Databases of PubMed, Embase, and Cochrane CENTRAL were searched until June 2022. Articles were included if they reported on AI actively being used in the clinical trial enrollment process. Narrative synthesis was conducted among all extracted data accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. For studies where the 2x2 contingency table could be calculated or supplied by authors, a meta-analysis to calculate summary statistics was conducted using the hierarchical summary receiver operating characteristics curve model.RESULTS:
Ten articles reporting on more than 50â000 patients in 19 datasets were included. Accuracy, sensitivity, and specificity exceeded 80% in all but 1 dataset. Positive predictive value exceeded 80% in 5 of 17 datasets. Negative predictive value exceeded 80% in all datasets. Summary sensitivity was 90.5% (95% confidence interval [CI] = 70.9% to 97.4%); summary specificity was 99.3% (95% CI = 81.8% to 99.9%).CONCLUSIONS:
AI demonstrated comparable, if not superior, performance to manual screening for patient enrollment into cancer clinical trials. As well, AI is highly efficient, requiring less time and human resources to screen patients. AI should be further investigated and implemented for patient recruitment into cancer clinical trials. Future research should validate the use of AI for clinical trials enrollment in less resource-rich regions and ensure broad inclusion for generalizability to all sexes, ages, and ethnicities.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Inteligência Artificial
/
Neoplasias
Tipo de estudo:
Diagnostic_studies
/
Guideline
/
Prognostic_studies
/
Systematic_reviews
Limite:
Humans
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article