Artikel ; Online: Développement d’une solution d’intelligence artificielle pour améliorer le screening en recherche clinique.
2024 Band 111, Heft 5, Seite(n) 473–482
Abstract: Introduction: The recruitment step of all clinical trials is time consuming, harsh and generate extra costs. Artificial intelligence tools could improve recruitment in order to shorten inclusion phase. The objective was to assess the performance of an ... ...
| Titelübersetzung | Development of an artificial intelligence system to improve cancer clinical trial eligibility screening. | |||||
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| Abstract | Introduction: The recruitment step of all clinical trials is time consuming, harsh and generate extra costs. Artificial intelligence tools could improve recruitment in order to shorten inclusion phase. The objective was to assess the performance of an artificial intelligence driven tool (text mining, machine learning, classification…) for the screening and detection of patients, potentially eligible for recruitment in one of the clinical trials open at the "Institut de Cancérologie de Lorraine". Methods: Computerized clinical data during the first medical consultation among patients managed in an anticancer center over the 2019-2023 period were used to study the performances of an artificial intelligence tool (SAS® Viya). Recall, precision and F1-score were used to determine the artificial intelligence algorithm effectiveness. Time saved on screening was determined by the difference between the time taken using the artificial intelligence-assisted method and that taken using the standard method in clinical trial participant screening. Results: Out of 9876 patients included in the study, the artificial intelligence algorithm obtained the following scores: precision of 96 %, recall of 94 % and a 0.95 F1-score to detect patients with breast cancer (n=2039) and potentially eligible for inclusion in a clinical trial. The screening of 258 potentially eligible patient's files took 20s per file vs. 5min and 6s with standard method. Discussion: This study suggests that artificial intelligence could yield sizable improvements over standard practices in several aspects of the patient screening process, as well as in approaches to feasibility, site selection, and trial selection. |
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| Mesh-Begriff(e) | Humans ; Artificial Intelligence ; Clinical Trials as Topic ; Patient Selection ; Female ; Algorithms ; Breast Neoplasms/diagnosis ; Data Mining/methods ; Middle Aged ; Eligibility Determination/methods ; Machine Learning ; Aged ; Male ; Time Factors ; Neoplasms/diagnosis | |||||
| Sprache | Französisch | |||||
| Erscheinungsdatum | 2024-03-19 | |||||
| Erscheinungsland | France | |||||
| Dokumenttyp | English Abstract ; Journal Article | |||||
| ZDB-ID | 213270-9 | |||||
| ISSN | 1769-6917 ; 0007-4551 | |||||
| ISSN (online) | 1769-6917 | |||||
| ISSN | 0007-4551 | |||||
| DOI | 10.1016/j.bulcan.2024.01.010 | |||||
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| Datenquelle | MEDical Literature Analysis and Retrieval System OnLINE |
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