Title: Datasheets for Healthcare AI: A Framework for Transparency and Bias Mitigation Show Chinese title

Title: 医疗AI的数据表：一种透明度和偏差缓解的框架

Abstract: The use of AI in healthcare has the potential to improve patient care, optimize clinical workflows, and enhance decision-making. However, bias, data incompleteness, and inaccuracies in training datasets can lead to unfair outcomes and amplify existing disparities. This research investigates the current state of dataset documentation practices, focusing on their ability to address these challenges and support ethical AI development. We identify shortcomings in existing documentation methods, which limit the recognition and mitigation of bias, incompleteness, and other issues in datasets. We propose the 'Healthcare AI Datasheet' to address these gaps, a dataset documentation framework that promotes transparency and ensures alignment with regulatory requirements. Additionally, we demonstrate how it can be expressed in a machine-readable format, facilitating its integration with datasets and enabling automated risk assessments. The findings emphasise the importance of dataset documentation in fostering responsible AI development. Show Chinese abstract

Abstract: 人工智能在医疗保健中的应用有可能改善患者护理、优化临床工作流程并提高决策能力。 然而，训练数据集中的偏见、数据不完整性和不准确性可能导致不公平的结果，并加剧现有的不平等。 本研究调查了当前的数据集文档实践状况，重点关注其应对这些挑战和支持伦理人工智能发展的能力。 我们发现现有文档方法存在不足，这限制了对数据集中偏见、不完整性和其他问题的识别和缓解。 我们提出了“医疗人工智能数据表”，以弥补这些差距，这是一种促进透明度并确保符合监管要求的数据集文档框架。 此外，我们展示了如何将其表示为机器可读格式，促进与数据集的集成，并实现自动风险评估。 研究结果强调了数据集文档在促进负责任的人工智能发展中的重要性。