[preprint]
medRxiv — Health Informatics[preprint]By Brian, D., Pereira Hernandez, X. I., Yin, H., Koury, J., Rust, R.
Large language models detect methodological errors that human peer review misses in clinical trials
Why it matters
Journals could add an LLM screening pass to peer review: in this test, models caught most planted trial-report errors that human reviewers largely missed.
The brief
5 points
- Fifteen LLMs detected 53% to 96% of planted methodological errors in clinical trial reports; untrained human reviewers averaged 27%.
- A repeat test on three trials published in 2026, designed to limit memorization, produced scores of 51% to 99%.
- Opus 5.5 scored highest at 99%; GPT-6.1 Sol and GPT-6 Luna reached 92% and 87% as cost-effective alternatives.
- Models flagged 2% to 29% of error types in error-free control reports, mostly unsupported conclusions and missing enrollment reasons.
- Mean review times across 12 models ranged from 1.4 to 16.3 minutes.