Despite the rapid development of AI models in medical image analysis, their validation in real-world clinical settings remains limited. To address this, we introduce a generic framework designed for deploying image-based AI models in such settings. Using this framework, we deployed a trained model for fetal ultrasound standard plane detection, and evaluated it in real-time sessions with both novice and expert users. Feedback from these sessions revealed that while the model offers potential benefits to medical practitioners, the need for navigational guidance was identified as a key area for improvement. These findings underscore the importance of early deployment of AI models in real-world settings, leading to insights that can guide the refinement of the model and system based on actual user feedback.

翻译：尽管人工智能模型在医学影像分析领域发展迅速，但其在真实世界临床环境中的验证仍然有限。为此，我们引入了一个通用框架，旨在将基于影像的人工智能模型部署于此类环境中。利用该框架，我们部署了一个用于胎儿超声标准切面检测的已训练模型，并在与新手和专家用户的实时会话中对其进行了评估。这些会话的反馈表明，虽然该模型为医疗从业者提供了潜在的益处，但导航引导的需求被确定为需要改进的关键领域。这些发现强调了在真实世界环境中早期部署人工智能模型的重要性，从而获得基于实际用户反馈来指导模型和系统优化的见解。