Comprehensive Summary
Wang et. al evaluated the diagnostic performance of large language models (LLMs) in interpreting urodynamic studies (UDS). UDS is a core diagnostic tool in urology that often requires specialized expertise. Data from 320 urodynamic studies from patients with lower urinary tract conditions were interpreted using two LLMs, Deepseek-R1 and GPT-4. Their results were then compared with junior and senior urologists. Diagnostic performance was assessed through ROC analysis, AUC scores, accuracy, and the QUEST framework. Deepseek-R1 achieved the highest diagnostic accuracy of 92.5%, while GPT-4 achieved 85.9%. Junior urologists had the lowest percentage of 83.8%, while senior urologists reached 95.9%. ROC analysis and QUEST framework showed similar results, depicting Deepseek-R1’s superiority compared to GPT-4.
Outcomes and Implications
The findings depict LLMs’ rapid development in their ability to interpret complex urodynamic data. More specifically, Deepseek-R1’s strong performance suggests that AI tools could soon play a valuable role in clinical settings, especially in patient education and quality assurance. By improving diagnostic consistency, AI models could help standardize UDS interpretation and make expert-level analysis more accessible in community or training settings. However, integration into clinical workflows will require physician oversight and strict safety standards to ensure responsible use in patient care.