Iterative Dual-AI Consultation for Error Detection in Clinical Medicine: A Case Study Demonstrating Convergent Validity Through Cross-Validation of Large Language Models.

Key Points

• Error detection was accurately supported using artificial intelligence, enhancing clinical decision-making processes.
• Convergent validity was demonstrated through systematic cross-validation of large language models with neuroimaging data.
• The approach utilized advanced automated volumetry to assess performance and reliability in error detection tasks.
• Findings suggest that AI can significantly improve clinical error identification, emphasizing its growing importance in medical settings.

Abstract