In this research, we take an HCI perspective on the opportunities provided by AI techniques in medical imaging, focusing on workflow efficiency and quality, preventing errors and variability of diagnosis in Breast Cancer. Starting from a holistic understanding of the clinical context, we developed BreastScreening to support Multimodality and integrate AI techniques (using a deep neural network to support automatic and reliable classification) in the medical diagnosis workflow. This was assessed by using a significant number of clinical settings and radiologists. Here we present: i) user study findings of 45 physicians comprising nine clinical institutions; ii) list of design recommendations for visualization to support breast screening radiomics; iii) evaluation results of a proof-of-concept BreastScreening prototype for two conditions Current (without AI assistant) and AI-Assisted; and iv) evidence from the impact of a Multimodality and AI-Assisted strategy in diagnosing and severity classification of lesions. The above strategies will allow us to conclude about the behaviour of clinicians when an AI module is present in a diagnostic system. This behaviour will have a direct impact in the clinicians workflow that is thoroughly addressed herein. Our results show a high level of acceptance of AI techniques from radiologists and point to a significant reduction of cognitive workload and improvement in diagnosis execution.

在这项研究中，我们从人机交互的角度探讨了AI技术在医学成像中提供的机会，重点是工作流程的效率和质量，防止乳腺癌诊断的错误和变异性。从对临床背景的全面了解开始，我们开发了BreastScreening，以支持多模式并整合AI技术（使用深度神经网络支持自动和可靠的分类）在医疗诊断工作流程中。通过使用大量的临床环境和放射线医师对此进行了评估。在这里，我们介绍：i）包括9个临床机构的45位医师的用户研究结果；ii）可视化设计建议清单，以支持乳房筛查放射学检查；iii）在当前（没有AI助手）和AI辅助的两个条件下的概念验证乳房筛查原型的评估结果；iv）来自多模式和AI辅助的影响的证据病变的诊断和严重性分类的策略。当策略模块中存在AI模块时，以上策略将使我们能够得出有关临床医生行为的结论。此行为将对临床医生工作流程产生直接影响，本文将对此进行详细介绍。我们的结果表明放射科医生对AI技术的接受程度很高，这表明认知工作量显着减少，诊断执行得到改善。