Machine Learning Tools and Platforms in Clinical Trial Outputs to Support Evidence-Based Health Informatics: A Rapid Review of the Literature

Abstract

Background: The application of machine learning (ML) tools (MLTs) to support clinical trials outputs in evidence-based health informatics can be an effective, useful, feasible, and acceptable way to advance medical research and provide precision medicine. Methods: In this study, the author used the rapid review approach and snowballing methods. The review was conducted in the following databases: PubMed, Scopus, COCHRANE LIBRARY, clinicaltrials.gov, Semantic Scholar, and the first six pages of Google Scholar from the 10 July–15 August 2022 period. Results: Here, 49 articles met the required criteria and were included in this review. Accordingly, 32 MLTs and platforms were identified in this study that applied the automatic extraction of knowledge from clinical trial outputs. Specifically, the initial use of automated tools resulted in modest to satisfactory time savings compared with the manual management. In addition, the evaluation of performance, functionality, usability, user interface, and system requirements also yielded positive results. Moreover, the evaluation of some tools in terms of acceptance, feasibility, precision, accuracy, efficiency, efficacy, and reliability was also positive. Conclusions: In summary, design based on the application of clinical trial results in ML is a promising approach to apply more reliable solutions. Future studies are needed to propose common standards for the assessment of MLTs and to clinically validate the performance in specific healthcare and technical domains.

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