Faust Oliver, Salvi Massimo, Barua Prabal Datta, Chakraborty Subrata, Molinari Filippo, Acharya U Rajendra
School of Computing and Information Science, Anglia Ruskin University, Cambridge Campus, Cambridge CB1 1PT, UK.
PoliToBIOMed Lab, Biolab, Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca Degli Abruzzi 24, 10129 Turin, Italy.
Sensors (Basel). 2025 Jan 2;25(1):205. doi: 10.3390/s25010205.
In this paper, we explore the correlation between performance reporting and the development of inclusive AI solutions for biomedical problems. Our study examines the critical aspects of bias and noise in the context of medical decision support, aiming to provide actionable solutions. Contributions: A key contribution of our work is the recognition that measurement processes introduce noise and bias arising from human data interpretation and selection. We introduce the concept of "noise-bias cascade" to explain their interconnected nature. While current AI models handle noise well, bias remains a significant obstacle in achieving practical performance in these models. Our analysis spans the entire AI development lifecycle, from data collection to model deployment.
To effectively mitigate bias, we assert the need to implement additional measures such as rigorous study design; appropriate statistical analysis; transparent reporting; and diverse research representation. Furthermore, we strongly recommend the integration of uncertainty measures during model deployment to ensure the utmost fairness and inclusivity. These comprehensive recommendations aim to minimize both bias and noise, thereby improving the performance of future medical decision support systems.
在本文中，我们探讨性能报告与用于生物医学问题的包容性人工智能解决方案开发之间的相关性。我们的研究在医疗决策支持的背景下考察偏差和噪声的关键方面，旨在提供可操作的解决方案。贡献：我们工作的一个关键贡献是认识到测量过程会引入因人类数据解释和选择而产生的噪声和偏差。我们引入“噪声-偏差级联”的概念来解释它们的相互关联性质。虽然当前的人工智能模型能很好地处理噪声，但偏差仍是这些模型实现实际性能的重大障碍。我们的分析涵盖从数据收集到模型部署的整个人工智能开发生命周期。
为有效减轻偏差，我们主张需要实施额外措施，如严谨的研究设计；适当的统计分析；透明的报告；以及多样化的研究代表性。此外，我们强烈建议在模型部署期间整合不确定性度量，以确保最大程度的公平性和包容性。这些全面的建议旨在将偏差和噪声降至最低，从而提高未来医疗决策支持系统的性能。
