(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 15, No. 1, 2024 Transformative Automation: AI in Scientific Literature Reviews Kirtirajsinh Zala 1 , Biswaranjan Acharya 2 , Madhav Mashru 3 , Damodharan Palaniappan 4 , Vassilis C. Gerogiannis 5 , Andreas Kanavos 6 , Ioannis Karamitsos 7 Department of Information Technology, Marwadi University, Rajkot, Gujarat 360003, India 1, 4 Department of Computer Engineering -AI & BDA, Marwadi University, Rajkot, Gujarat 360003, India 2 Faculty of Engineering, Marwadi Education Foundation’s Group of Institutions, Rajkot, Gujarat 360003, India 3 Department of Digital Systems, University of Thessaly, Larissa, Greece 5 Department of Informatics, Ionian University, Corfu, Greece 6 Research and Graduate Department, Rochester Institute of Technology, Dubai, UAE 7 Abstract—This paper investigates the integration of Artificial Intelligence (AI) into systematic literature reviews (SLRs), aiming to address the challenges associated with the manual review process. SLRs, a crucial aspect of scholarly research, often prove time-consuming and prone to errors. In response, this work explores the application of AI techniques, including Natural Language Processing (NLP), machine learning, data mining, and text analytics, to automate various stages of the SLR process. Specifically, we focus on paper identification, information extrac- tion, and data synthesis. The study delves into the roles of NLP and machine learning algorithms in automating the identification of relevant papers based on defined criteria. Researchers now have access to a diverse set of AI-based tools and platforms designed to streamline SLRs, offering automated search, retrieval, text mining, and analysis of relevant publications. The dynamic field of AI-driven SLR automation continues to evolve, with ongoing exploration of new techniques and enhancements to existing algorithms. This shift from manual efforts to automation not only enhances the efficiency and effectiveness of SLRs but also marks a significant advancement in the broader research process. Keywords—Artificial intelligence; systematic literature review; scholarly data analysis; machine learning algorithms; natural language processing; scientific publication automation I. I NTRODUCTION Artificial intelligence (AI) has emerged to alleviate humans from repetitive tasks that demand specific human skills. Like any other field, scientific endeavors benefit from powerful algorithms to expedite and enhance outcomes. Initiating a new research project typically involves a thorough investigation of relevant scholarly publications to comprehend the landscape and identify activities significant for addressing similar or related issues. The process of gathering documents, when performed without prior training or well-defined parameters, may lead to the omission of significant contributions [29]. A comprehensive approach to searching and analyzing literature can help reduce the likelihood of bias and inaccuracy in research [24], [28]. A systematic literature review (SLR) is a secondary investi- gation that assesses existing research, employing a widely rec- ognized procedure to identify related articles, extract pertinent details, and present their main findings in an organized manner [33]. It is anticipated that a published literature review will deliver a comprehensive summary of a corresponding research subject, often providing a historical perspective that facilitates the identification of research trends and unresolved issues. Literature reviews are now a fundamental component of many scientific fields, including medicine (with 13,510 published reviews) and computer science (with 6,342) [47]. Conducting a literature review is known to be time- consuming, especially when addressing a vast research subject. In recent years, various systematic literature review (SLR)- related tools have been developed for diverse purposes [47]. These tools can automate digital database searches, designate relevant outcomes based on inclusion criteria, and provide visual support for analyzing information from works’ authors and their citations, among other capabilities. Particularly, the automation of the SLR process is gaining attention in the field of computer science research, offering strategies to construct search phrases and retrieve publications semi-automatically or manually from relevant scientific databases [76]. The utiliza- tion of automated methods has proven to save time and costs in selecting relevant articles [11], or providing a summary of the findings [71]. However, some authors argue that the usefulness of these automated tools is limited by their steep learning curve and the lack of research analyzing the advantages they offer [74]. This paper focuses on the computerized and automated operation of SLR tasks, replacing manual labor with ML as the primary driver. The goal is to enhance the capability of automated review processes and technologies with some ad- ditional understanding and suggestions. The initial application of AI methods to automate SLR tasks occurred in 2006 [12], where it was suggested that neural networks could be used to automate the selection of relevant articles. Initial resistance to this idea stemmed from concerns regarding the use of data gleaned from secondary sources through text mining [51]. Following this concept, previous works by other re- searchers have delved into powerful text mining techniques [52], [58], [65]. Recent innovations in the field include the integration of ML and natural language processing (NLP) techniques [27], [76]. Considering the repetitive tasks involved in a SLR methodology, the capabilities of AI for analyzing scientific literature are vast. However, it’s crucial not to devalue the role of human involvement in this process, as humans bring www.ijacsa.thesai.org 1246 | Page