Evaluation of AI Tools Versus the PRISMA Method for Literature Search, Data Extraction, and Study Composition in Glaucoma Systematic Reviews: Content Analysis.

Background: Artificial intelligence (AI) is becoming increasingly popular in the scientific field, as it allows for the analysis of extensive datasets, summarizes results, and assists in writing academic papers. Objective: This study investigates the role of AI in the process of conducting a systematic literature review (SLR), focusing on its contributions and limitations at three key stages of its development, study selection, data extraction, and study composition, using glaucoma-related SLRs as case studies and Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA)-based SLRs as benchmarks. Methods: Four AI platforms were tested on their ability to reproduce four PRISMA-based, glaucoma-related SLRs. We used Connected Papers and Elicit to perform research of relevant records; then we assessed Elicit and ChatPDF's ability to extract and organize information contained in the retrieved records. Finally, we tested Jenni AI's capacity to compose an SLR. Results: Neither Connected Papers nor Elicit provided the totality of the results found using the PRISMA method. On average, data extracted from Elicit were accurate in 51.40% (SD 31.45%) of cases and imprecise in 13.69% (SD 17.98%); 22.37% (SD 27.54%) of responses were missing, while 12.51% (SD 14.70%) were incorrect. Data extracted from ChatPDF were accurate in 60.33% (SD 30.72%) of cases and imprecise in 7.41% (SD 13.88%); 17.56% (SD 20.02%) of responses were missing, and 14.70% (SD 17.72%) were incorrect. Jenni AI's generated content exhibited satisfactory language fluency and technical proficiency but was insufficient in defining methods, elaborating results, and stating conclusions. Conclusions: The PRISMA method continues to exhibit clear superiority in terms of reproducibility and accuracy during the literature search, data extraction, and study composition phases of the SLR writing process. While AI can save time and assist with repetitive tasks, the active participation of the researcher throughout the entire process is still crucial to maintain control over the quality, accuracy, and objectivity of their work.