Can artificial intelligence accurately assess systematic review quality? Benchmarking large language models for AMSTAR 2 appraisal in dental evidence synthesis.

Perplexity also achieved the best sensitivity (81.3%, 95% CI 76.5-85.4%) and specificity (82.7%, 95% CI 78.1-86.5%) for correctly identifying high-quality reviews.

Non-overlapping 95% confidence intervals suggest meaningful differences in performances among platforms, with Perplexity showing superior agreement across all metrics.

Across all platforms, agreement was generally higher for non-critical AMSTAR-2 domains involving clear and structured reporting, whereas performance was weaker for critical domains requiring interpretation of complex methodological details, risk-of-bias considerations, and evidence synthesis procedures.