SPECTRUM SRA: An R Shiny Application to Automate Systematic Reviews Using Artificial Intelligence and Large Language Models

Purpose : This study aims to develop an R Shiny application named SPECTRUM SRA (Systematic Review Automation) and to assess its feasibility. The application encompasses eight modules: Search, PRISMA flow diagram generation, dEduplication, sCreening, daTa extraction, Reporting, qUery, and suMmarization. It provides an end‑to‑end workflow to automate and semi‑automate multiple steps of systematic reviews, addressing growing time and labor constraints and accelerating evidence‑to‑practice translation. Design/methodology/approach : SPECTRUM SRA comprises eight interconnected modules and offers an interface for automated article search and retrieval from PubMed, Scopus, and Web of Science. Search functionality is implemented via API integration for PubMed and Scopus, with manual search and import for Web of Science results. Deduplication employs two strategies: DOI‑based matching and fuzzy string deduplication. The application also supports automated title/abstract and full‑text screening using large language model prompting. Automated reporting for both search and screening is incorporated through AI‑driven scripting. Additionally, SPECTRUM SRA automates PRISMA flow diagram generation. Summarization and data extraction of the final set of eligible studies are performed via LLM prompting. The feasibility of the application was assessed through a systematic review of randomized controlled trials comparing virtual reality interventions to non‑virtual‑reality approaches in dentistry. Findings : Keyword identification was completed in 12 seconds; search query generation took less than three seconds; and retrieval of 703 records from the databases required 4 minutes and 30 seconds. Deduplication removed 238 records in 3 seconds, leaving 465 records for screening. ChatGPT o4‑mini‑high and DeepSeek V3 R1 were selected as the first and second reviewers, respectively. Title/abstract screening took 84.5 minutes for Reviewer 1 and 131 minutes for Reviewer 2 (κ = 0.852). Full‑text screening of 43 articles required 43 minutes for Reviewer 1 and 90 minutes for Reviewer 2 (κ = 0.903). Automated summarization and data extraction of the 25 final eligible studies achieved 91% accuracy using ChatGPT in 25 minutes. Originality : To the best of our knowledge, SPECTRUM SRA is the first end‑to‑end Shiny application that supports multiple tasks in systematic reviews. It can reduce the time, labor, and energy required to conduct systematic reviews, thereby accelerating health policy and decision‑making.