In spite of the strong performance of machine learning (ML) models in radiology, they have not been widely accepted by radiologists, limiting clinical integration. A key reason is the lack of explainability, which ensures that model predictions are understandable and verifiable by clinicians. Several methods and tools have been proposed to improve explainability, but most reflect developers' perspectives and lack systematic clinical validation. In this work, we gathered insights from radiologists with varying experience and specialties into explainable ML requirements through a structured questionnaire. They also highlighted key clinical tasks where ML could be most beneficial and how it might be deployed. Based on their input, we propose guidelines for designing and developing explainable ML models in radiology. These guidelines can help researchers develop clinically useful models, facilitating integration into radiology practice as a supportive tool.

翻译：尽管机器学习模型在放射学领域表现出强劲性能，但其尚未被放射科医生广泛接受，从而限制了临床整合。其中一个关键原因在于缺乏可解释性——即确保模型预测能被临床医生理解与验证。目前已有多种提升可解释性的方法与工具被提出，但多数反映的是开发者的视角，缺乏系统性临床验证。本研究通过结构化问卷，收集了不同经验层次和专业方向的放射科医生对可解释机器学习需求的认识。他们同时指出了机器学习最具应用价值的核心临床任务及其潜在部署方式。基于其反馈，我们提出了放射学领域可解释机器学习模型的设计与开发指南。这些指南可帮助研究人员开发更具临床实用性的模型，从而促进其作为辅助工具融入放射诊疗实践。