Jorg Tobias, Halfmann Moritz C, Stoehr Fabian, Arnhold Gordon, Theobald Annabell, Mildenberger Peter, Müller Lukas
Department of Diagnostic and Interventional Radiology, University Medical Centerof the, Johannes Gutenberg-University Mainz , Langenbeckst. 1, 55131, Mainz, Germany.
Insights Imaging. 2024 Mar 19;15(1):80. doi: 10.1186/s13244-024-01660-5.
Artificial intelligence (AI) has tremendous potential to help radiologists in daily clinical routine. However, a seamless, standardized, and time-efficient way of integrating AI into the radiology workflow is often lacking. This constrains the full potential of this technology. To address this, we developed a new reporting pipeline that enables automated pre-population of structured reports with results provided by AI tools.
Findings from a commercially available AI tool for chest X-ray pathology detection were sent to an IHE-MRRT-compliant structured reporting (SR) platform as DICOM SR elements and used to automatically pre-populate a chest X-ray SR template. Pre-populated AI results could be validated, altered, or deleted by radiologists accessing the SR template. We assessed the performance of this newly developed AI to SR pipeline by comparing reporting times and subjective report quality to reports created as free-text and conventional structured reports.
Chest X-ray reports with the new pipeline could be created in significantly less time than free-text reports and conventional structured reports (mean reporting times: 66.8 s vs. 85.6 s and 85.8 s, respectively; both p < 0.001). Reports created with the pipeline were rated significantly higher quality on a 5-point Likert scale than free-text reports (p < 0.001).
The AI to SR pipeline offers a standardized, time-efficient way to integrate AI-generated findings into the reporting workflow as parts of structured reports and has the potential to improve clinical AI integration and further increase synergy between AI and SR in the future.
With the AI-to-structured reporting pipeline, chest X-ray reports can be created in a standardized, time-efficient, and high-quality manner. The pipeline has the potential to improve AI integration into daily clinical routine, which may facilitate utilization of the benefits of AI to the fullest.
• A pipeline was developed for automated transfer of AI results into structured reports. • Pipeline chest X-ray reporting is faster than free-text or conventional structured reports. • Report quality was also rated higher for reports created with the pipeline. • The pipeline offers efficient, standardized AI integration into the clinical workflow.
人工智能（AI）在日常临床工作中具有巨大潜力，可协助放射科医生。然而，将AI无缝、标准化且高效地整合到放射学工作流程的方法往往缺失，这限制了该技术的全部潜力。为解决此问题，我们开发了一种新的报告流程，可利用AI工具提供的结果自动预填充结构化报告。
将一种用于胸部X线病理检测的商用AI工具的结果作为DICOM SR元素发送到符合IHE-MRRT的结构化报告（SR）平台，并用于自动预填充胸部X线SR模板。放射科医生访问SR模板时可对预填充的AI结果进行验证、修改或删除。我们通过比较报告时间和主观报告质量，将这种新开发的AI到SR流程的性能与以自由文本和传统结构化报告形式创建的报告进行了比较。
使用新流程创建胸部X线报告的时间明显少于自由文本报告和传统结构化报告（平均报告时间分别为66.8秒、85.6秒和85.8秒；p均<0.001）。在5点李克特量表上，使用该流程创建的报告质量评级明显高于自由文本报告（p<0.001）。
AI到SR流程提供了一种标准化、高效的方式，可将AI生成结果作为结构化报告的一部分整合到报告工作流程中，并且有潜力在未来改善临床AI整合，进一步增强AI与SR之间的协同作用。
借助AI到结构化报告流程，可标准化、高效且高质量地创建胸部X线报告。该流程有潜力改善AI在日常临床工作中的整合，从而可能最大程度地利用AI的益处。
• 开发了一种将AI结果自动传输到结构化报告的流程。• 流程胸部X线报告比自由文本或传统结构化报告更快。• 使用该流程创建的报告质量评级也更高。• 该流程为临床工作流程提供了高效、标准化的AI整合。
