AI-based assessment of abstract quality in otorhinolaryngology journals

Objectives: This study aims to analyze 2024 otorhinolaryngology journal abstracts indexed in the Web of Science (WoS) using an artificial intelligence (AI)-based structured rubric (ChatGPT) to assess quality and explore associations with journal metrics. Materials and Methods: A methodological analysis was conducted on 515 comparative-study abstracts from 66 WoS-indexed journals. Each abstract was evaluated by an AI language model (ChatGPT-5, OpenAI) using a 10-item rubric derived from international reporting standards, scoring 0-100 across originality, aim, design, methods, statistics, results, interpretation, flow, and impact. Journal metrics (SCI/ESCI, quartile, Journal Citation Indicator [JCI]) were retrieved from the WoS database. Results: The mean total quality score was 75.3±7.6 (range, 50 to 94). Highest scores were for clarity of aim and results (91.0±5.6%), while lowest were for study design and sample size. Abstracts in SCI journals (76.0±7.6) scored higher than ESCI (70.2±5.1, p1 (p