AI辅助生成报告可提速30%且不影响诊断准确率。
The Impact of AI Assistance on Radiology Reporting: A Pilot Study Using Simulated AI Draft Reports
- 用GPT-4模拟AI报告草稿，对比标准模板与AI辅助流程。
- 平均报告时间从573秒缩短至435秒，降幅达24%。
- 适合面临报告压力的放射科医生，尤其关注效率提升者。
放射科医生面临影像数量增长带来的工作压力，存在职业倦怠和报告延迟风险。尽管基于人工智能的自动化报告生成有望优化工作流程，但其对临床准确性与效率的真实影响证据仍有限。本研究通过三名读者的多病例对照实验，比较了标准报告流程与AI辅助流程的效果。在两种流程中，放射科医生均需审阅病例并修改报告：标准流程使用模板，而AI辅助流程则修改由GPT-4生成的模拟AI草稿。为控制评估，我们在一半案例中故意引入1-3处错误，以模拟真实AI系统的性能。结果显示，AI辅助流程将平均报告时间从573秒显著降低至435秒（p=0.003），且两组间临床显著错误差异无统计学意义。结果表明，AI生成的报告草稿可在不牺牲诊断准确性的情况下显著提升报告效率，为应对临床实践中的工作量挑战提供可行方案。
原文摘要 · Abstract (English)
Radiologists face increasing workload pressures amid growing imaging volumes, creating risks of burnout and delayed reporting times. While artificial intelligence (AI) based automated radiology report generation shows promise for reporting workflow optimization, evidence of its real-world impact on clinical accuracy and efficiency remains limited. This study evaluated the effect of draft reports on radiology reporting workflows by conducting a three reader multi-case study comparing standard versus AI-assisted reporting workflows. In both workflows, radiologists reviewed the cases and modified either a standard template (standard workflow) or an AI-generated draft report (AI-assisted workflow) to create the final report. For controlled evaluation, we used GPT-4 to generate simulated AI drafts and deliberately introduced 1-3 errors in half the cases to mimic real AI system performance. The AI-assisted workflow significantly reduced average reporting time from 573 to 435 seconds (p=0.003), without a statistically significant difference in clinically significant errors between workflows. These findings suggest that AI-generated drafts can meaningfully accelerate radiology reporting while maintaining diagnostic accuracy, offering a practical solution to address mounting workload challenges in clinical practice.
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