PDF(Pubmed)
Abstract：
OBJECTIVE: 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.
METHODS: 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.
RESULTS: 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).
CONCLUSIONS: 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.
UNASSIGNED: 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.
CONCLUSIONS: • 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.
METHODS: 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.
RESULTS: 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).
CONCLUSIONS: 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.
UNASSIGNED: 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.
CONCLUSIONS: • 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.
摘要：
目的：人工智能（AI）具有巨大的潜力，可以帮助放射科医生进行日常临床工作。然而,一个无缝的，标准化，并且通常缺乏将AI集成到放射学工作流程中的高效方法。这限制了该技术的全部潜力。为了解决这个问题，我们开发了一种新的报告管道，该管道可以使用AI工具提供的结果自动预先填充结构化报告。
方法：将用于胸部X线病理学检测的市售AI工具的结果作为DICOMSR元件发送到符合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。可以验证预先填充的AI结果，改变,或由放射科医师访问SR模板删除。我们通过将报告时间和主观报告质量与作为自由文本和传统结构化报告创建的报告进行比较，评估了这种新开发的AI到SR管道的性能。
结果：与自由文本报告和传统结构化报告相比，使用新管道的胸部X射线报告的创建时间要少得多（平均报告时间：66.8svs.85.6s和85.8s，分别；两者p<0.001)。在5点Likert量表上，使用管道创建的报告的质量显着高于自由文本报告（p<0.001）。
结论：AI到SR管道提供了一个标准化的，将AI生成的结果作为结构化报告的一部分集成到报告工作流程中的高效方法，并且有可能改善临床AI集成并在未来进一步增加AI和SR之间的协同作用。
■使用AI到结构化的报告管道，胸部X光报告可以创建一个标准化的，省时，和高质量的方式。该管道有可能改善AI与日常临床常规的整合，这可能有助于最大限度地利用人工智能的好处。
结论：•开发了一条管道，用于将AI结果自动传输到结构化报告中。•管道胸部X射线报告比自由文本或传统结构化报告更快。•使用管道创建的报告的报告质量也被评为较高。•Thepipelineoffersefficient,标准化的AI集成到临床工作流程中。
方法：将用于胸部X线病理学检测的市售AI工具的结果作为DICOMSR元件发送到符合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。可以验证预先填充的AI结果，改变,或由放射科医师访问SR模板删除。我们通过将报告时间和主观报告质量与作为自由文本和传统结构化报告创建的报告进行比较，评估了这种新开发的AI到SR管道的性能。
结果：与自由文本报告和传统结构化报告相比，使用新管道的胸部X射线报告的创建时间要少得多（平均报告时间：66.8svs.85.6s和85.8s，分别；两者p<0.001)。在5点Likert量表上，使用管道创建的报告的质量显着高于自由文本报告（p<0.001）。
结论：AI到SR管道提供了一个标准化的，将AI生成的结果作为结构化报告的一部分集成到报告工作流程中的高效方法，并且有可能改善临床AI集成并在未来进一步增加AI和SR之间的协同作用。
■使用AI到结构化的报告管道，胸部X光报告可以创建一个标准化的，省时，和高质量的方式。该管道有可能改善AI与日常临床常规的整合，这可能有助于最大限度地利用人工智能的好处。
结论：•开发了一条管道，用于将AI结果自动传输到结构化报告中。•管道胸部X射线报告比自由文本或传统结构化报告更快。•使用管道创建的报告的报告质量也被评为较高。•Thepipelineoffersefficient,标准化的AI集成到临床工作流程中。
