Utilizing ChatGPT to select literature for meta-analysis shows workload reduction while maintaining a similar recall level as manual curation

Abstract

Background: Large language models (LLMs) like ChatGPT showed great potential in aiding medical research. A heavy workload in filtering records is needed during the research process of evidence-based medicine, especially meta-analysis. However, no study tried to use LLMs to help screen records in meta-analysis. In this research, we aimed to explore the possibility of incorporating ChatGPT to facilitate the screening step based on the title and abstract of records during meta-analysis. Methods: To assess our strategy, we selected three meta-analyses from the literature, together with a glioma meta-analysis embedded in the study, as additional validation. For the automatic selection of records from curated meta-analyses, a four-step strategy called LARS was developed, consisting of (1) criteria selection and single-prompt (prompt with one criterion) creation, (2) best combination identification, (3) combined-prompt (prompt with one or more criteria) creation, and (4) request sending and answer summary. We evaluated the robustness of the response from ChatGPT with repeated requests. Recall, workload reduction, precision, and F1 score were calculated to assess the performance of LARS. Findings: ChatGPT showed a stable response for repeated requests (robustness score: 0.747 - 0.996). A variable performance was found between different single-prompts with a mean recall of 0.841. Based on these single-prompts, we were able to find combinations with performance better than the pre-set threshold. Finally, with a best combination of criteria identified, LARS showed a 39.5% workload reduction on average with a recall greater than 0.9. In the glioma meta-analysis, we found no prognostic effect of CD8+ TIL on overall survival, progress-free survival, and survival time after immunotherapy. Interpretation: We show here the groundbreaking finding that automatic selection of literature for meta-analysis is possible with ChatGPT. We provide it here as a pipeline, LARS, which showed a great workload reduction while maintaining a pre-set recall. Funding: China Scholarship Council.

Addressing Data Scarcity in the Medical Domain: A GPT-Based Approach for Synthetic Data Generation and Feature Extraction

“…The paper illustrates how AI, specifically ChatGPT, can assist in various steps of the systematic review process, including evaluating methodologies and extracting data. The study in [13] focuses on the application of ChatGPT in streamlining the literature selection process for meta-analysis in medical research. It outlines a methodology for using ChatGPT to facilitate the screening of titles and abstracts during meta-analysis, aiming to reduce workload while maintaining recall efficiency.…”

“…It outlines a methodology for using ChatGPT to facilitate the screening of titles and abstracts during meta-analysis, aiming to reduce workload while maintaining recall efficiency. The study includes a glioma meta-analysis for validation and discusses the development of a pipeline called LARS (Literature Records Screener) to assess the performance of ChatGPT in this context [13]. It deals directly with improving the efficiency and effectiveness of literature selection and screening in the context of meta-analysis, a crucial step in systematic reviews and research synthesis [13].…”

“…The study includes a glioma meta-analysis for validation and discusses the development of a pipeline called LARS (Literature Records Screener) to assess the performance of ChatGPT in this context [13]. It deals directly with improving the efficiency and effectiveness of literature selection and screening in the context of meta-analysis, a crucial step in systematic reviews and research synthesis [13]. The research work in [14] discusses the potential public health risks posed by large language models like ChatGPT, specifically focusing on the spread of misinformation (infodemic).…”

“…Literature Review and Meta-Analysis: Studies in [11][12][13]18] illustrate how AI, specifically ChatGPT, can streamline literature reviews and meta-analyses, aiding in efficient data extraction and evaluation methodologies.…”

Potential Roles of Large Language Models in the Production of Systematic Reviews and Meta-Analyses (Preprint)

“…Subsequently, they can utilize ChatGPT to automatically screen records retrieved from databases, obtaining the filtered results . Previous studies suggested that utilizing ChatGPT in the literature selection process for meta-analysis substantially diminishes the workload while preserving a recall rate on par with manual curation [28,[44][45][46][47].…”