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展望尿动力学中人工智能的未来:对专家的探索性访谈

Envisioning the Future of AI in Urodynamics: Exploratory Interviews With Experts.

作者信息

van Leersum Catharina Margaretha, Gammie Andrew, Agrò Enrico Finazzi, Rademakers Kevin

机构信息

Faculty of Humanities, Open Universiteit, Heerlen, The Netherlands.

Bristol Urological Institute, Southmead Hospital, Bristol, UK.

出版信息

Neurourol Urodyn. 2025 Aug;44(6):1231-1237. doi: 10.1002/nau.70075. Epub 2025 Jun 15.

Abstract

BACKGROUND

Urodynamics monitors various parameters while the urinary bladder fills and empties, to diagnose functional or anatomic disorders of the lower urinary tract. It is an invasive and complex test with technical challenges, and it needs rigorous quality assessment and training of clinicians to avoid misdiagnosis. Applying AI to urodynamic pattern recognition and noisy data signals seems promising.

AIM

To understand desirable and appropriate applications, this study aims to explore the envisioned future of AI in urodynamics according to experts in AI and/or urodynamics.

METHOD

Ten semi-structured interviews were conducted to explore expectations, trust and possibilities of AI in urodynamics. Content analysis with an inductive approach was performed on all data.

RESULTS

The analysis resulted in seven overarching themes: difficulties with urodynamics, quality of urodynamics, AI will be supportive, development and training of AI systems, desirable outcomes, challenges, and envisioning the future of AI in urodynamics.

DISCUSSION AND CONCLUSION

In the vision of experts, urodynamics practices will change with the introduction of AI. In the beginning, clinicians will probably desire to check AI-made outcomes of UDS tests to gain trust the system. After experiencing the value of these systems, clinicians might let the system independently provide suggested UDS analyses, and they will use more of their time to spend on their patients.

摘要

背景

尿动力学在膀胱充盈和排空时监测各种参数,以诊断下尿路的功能或解剖紊乱。这是一项具有技术挑战的侵入性复杂检查,需要对临床医生进行严格的质量评估和培训以避免误诊。将人工智能应用于尿动力学模式识别和噪声数据信号似乎很有前景。

目的

为了解理想且合适的应用,本研究旨在根据人工智能和/或尿动力学领域的专家,探索人工智能在尿动力学中可预见的未来。

方法

进行了十次半结构化访谈,以探讨人工智能在尿动力学中的期望、信任和可能性。对所有数据采用归纳法进行内容分析。

结果

分析得出七个总体主题:尿动力学的困难、尿动力学的质量、人工智能将起到支持作用、人工智能系统的开发与培训、理想结果、挑战以及展望人工智能在尿动力学中的未来。

讨论与结论

在专家的设想中,随着人工智能的引入,尿动力学实践将会发生变化。一开始,临床医生可能希望检查人工智能生成的尿动力学检查结果以建立对该系统的信任。在体验到这些系统的价值后,临床医生可能会让系统独立提供尿动力学分析建议,并且他们将把更多时间用于照顾患者。

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