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.

翻译：放射科医师面临日益增长的影像检查量带来的工作压力增加，这导致了职业倦怠和报告延迟的风险。虽然基于人工智能（AI）的自动化放射学报告生成在优化报告工作流程方面显示出潜力，但其对临床准确性和效率的实际影响证据仍然有限。本研究通过开展一项包含三名阅片医师的多病例研究，比较了标准工作流程与AI辅助报告工作流程，评估了草稿报告对放射学报告工作流程的影响。在两种工作流程中，放射科医师均审阅病例并修改标准模板（标准工作流程）或AI生成的草稿报告（AI辅助工作流程）以生成最终报告。为了进行受控评估，我们使用GPT-4生成模拟的AI草稿，并故意在一半的病例中引入1-3个错误，以模拟真实AI系统的性能。AI辅助工作流程将平均报告时间从573秒显著减少至435秒（p=0.003），而两种工作流程之间在具有临床意义的错误方面没有统计学上的显著差异。这些发现表明，AI生成的草稿可以在保持诊断准确性的同时，有意义地加速放射学报告流程，为应对临床实践中日益增长的工作负荷挑战提供了一个实用的解决方案。