The study shows that artificial intelligence (AI) has become highly important in contemporary computing because of its capacity to efficiently tackle intricate jobs that were typically carried out by people. The authors provide scientific literature that analyzes and shows how to utilize AI to represent and infer information, quickly modify texts, and learn from large datasets. One crucial responsibility in this field is performing systematic literature reviews (SLRs), which include developing a strategy, searching and evaluating material, and presenting results. Manual SLR preparation is laborious and prone to errors, particularly when handling large quantities of documents. AI can streamline repetitious activities, enhancing SLR process efficiency.
Torre-López et al. outline the AI techniques developed in the past 15 years to aid scholars in performing systematic evaluations of scientific publications. The strategies examined focus on activities associated with SLRs, including screening pertinent papers, extracting essential information, and summarizing discoveries. The paper goes on to investigate the algorithms utilized in this setting, such as natural language processing (NLP), machine learning, and information retrieval, as well as the tools (suggested in 34 primary papers) that help academics automate SLR processes.
The paper discusses many aspects related to automation, data synthesis and extraction, and timeliness:
AI uses NLP algorithms to search through large databases and find pertinent studies for systematic reviews. The algorithms can effectively handle substantial amounts of text, identify keywords, and classify papers according to their topic.
AI techniques can streamline the search and screening process by eliminating unnecessary papers. This greatly decreases the amount of manual work needed for selecting literature.
Researchers are teaching machine learning algorithms to automatically extract important discoveries from scholarly journals. These models extract patterns from existing literature to recognize crucial information including study outcomes, statistical findings, and trends.
AI approaches help extract important data points such as study design, sample size, and statistical measurements. Automating this process allows academics to concentrate on more advanced analysis.
AI can help detect biases in the literature. Researchers have the ability to create algorithms that can identify potential biases associated with study design, funding sources, or author connections. This contributes to preserving the integrity of systematic reviews.
AI tools can track new publications and notify researchers when pertinent papers are released. This guarantees that systematic reviews are kept current and include the most recent research discoveries.
The survey offers insights into the development of AI in sign language recognition systems, and emphasizes the involvement of humans in a process that is becoming more automated. Although AI can simplify some processes, human experience is essential for interpreting results and maintaining the quality of systematic reviews.
Although AI may provide solutions, obstacles remain. Researchers need to find a balance between automation and human judgment. Furthermore, ethical considerations, bias detection, and openness are crucial when implementing AI in scientific literature analysis. In summary, this paper discusses how AI might improve research efficiency in SLRs while also recognizing the need for human participation.
