AI-based automation tools have significantly enhanced the efficiency and accuracy of systematic searching, a critical component of systematic reviews. These tools leverage advanced algorithms to automate repetitive tasks, thereby reducing human error and saving time. AI tools can assist in various stages of systematic searching, from query formulation to data extraction and synthesis. However, while AI tools offer substantial benefits, they are best used in conjunction with human expertise to ensure methodological rigor and comprehensive outcomes.
AI tools can generate and refine search terms, expanding the scope of queries beyond human capabilities. This is particularly useful in developing complex Boolean queries essential for systematic reviews(Chen & Feng, 2024) (Li et al., 2020).
Tools like ChatGPT-4 and its variants can automate the repetitive aspects of search-term generation, though human input remains crucial for refining these terms(Chen & Feng, 2024).
AI tools such as Abstrackr, RobotAnalyst, and DistillerSR have demonstrated the ability to screen large volumes of literature efficiently, eliminating a significant percentage of irrelevant titles and abstracts without missing key citations(Yao et al., 2024).
These tools can also assist in extracting essential data from studies, streamlining the process of synthesizing research findings(Fabiano et al., 2024) (RamÃrez & Romero, 2023).
AI-based tools have shown varying levels of accuracy and efficiency, with some tools capable of reducing the workload by up to 88% in certain cases(Yao et al., 2024).
The integration of machine learning and natural language processing further enhances the ability of AI tools to automate systematic reviews, making them more efficient and less prone to human error(Ofori-Boateng et al., 2024) (Zala et al., n.d.).
Despite their advantages, AI tools face challenges such as limited access to proprietary databases and the need for sophisticated search strategies that may still require human oversight(Chen & Feng, 2024).
The current landscape of AI-driven systematic review automation is still evolving, with ongoing research needed to address existing gaps and improve tool efficacy(Ofori-Boateng et al., 2024).
While AI-based automation tools offer transformative potential in systematic searching, they are not yet a complete substitute for human expertise. The synergy between AI tools and human reviewers is crucial for achieving accurate and comprehensive research outcomes. As AI technology continues to advance, its role in systematic reviews is likely to expand, offering even greater efficiency and precision in the future.