[Automation of evidence synthesis exemplified by a review on harm endpoints in ear canal cleaning].

Abstract

Objective: Automation approaches have the potential to significantly reduce the effort required for systematic literature reviews. These approaches include the use of AI-based automation software. This article presents a rapid review approach using AI-based automation tools for evidence synthesis of harm endpoints, using the example of the risks and complications associated with ear canal cleaning for cerumen removal.

Material and methods: Based on comparative studies of rapid review methods, the standard steps of a systematic review were adapted and the most suitable supporting AI and software tools for implementation were selected, taking into account the current scientific evidence.

Results: A total of 1,521 titles and abstracts and 52 full texts were screened for eligibility. Three reviewers were involved in different stages of the process over a period of six weeks. A web-based platform supported the screening of titles and abstracts, while a large language model web service was used for data extraction.

Conclusions: While systematic reviews require an average of five reviewers and over a year to complete, the combination of rapid review approaches and AI/automation tools for the steps of literature research, study selection, data extraction, data synthesis and visualisation significantly reduced the time and personnel required.