Abstract：
Uncertainty estimation in healthcare involves quantifying and understanding the inherent uncertainty or variability associated with medical predictions, diagnoses, and treatment outcomes. In this era of Artificial Intelligence (AI) models, uncertainty estimation becomes vital to ensure safe decision-making in the medical field. Therefore, this review focuses on the application of uncertainty techniques to machine and deep learning models in healthcare. A systematic literature review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Our analysis revealed that Bayesian methods were the predominant technique for uncertainty quantification in machine learning models, with Fuzzy systems being the second most used approach. Regarding deep learning models, Bayesian methods emerged as the most prevalent approach, finding application in nearly all aspects of medical imaging. Most of the studies reported in this paper focused on medical images, highlighting the prevalent application of uncertainty quantification techniques using deep learning models compared to machine learning models. Interestingly, we observed a scarcity of studies applying uncertainty quantification to physiological signals. Thus, future research on uncertainty quantification should prioritize investigating the application of these techniques to physiological signals. Overall, our review highlights the significance of integrating uncertainty techniques in healthcare applications of machine learning and deep learning models. This can provide valuable insights and practical solutions to manage uncertainty in real-world medical data, ultimately improving the accuracy and reliability of medical diagnoses and treatment recommendations.
摘要：
医疗保健中的不确定性估计涉及量化和理解与医疗预测相关的固有不确定性或变异性，诊断,和治疗结果。在这个人工智能（AI）模型的时代，不确定性估计对于确保医疗领域的安全决策至关重要。因此,这篇综述的重点是不确定性技术在医疗保健中机器和深度学习模型中的应用。使用系统审查和荟萃分析的首选报告项目（PRISMA）指南进行了系统的文献审查。我们的分析表明，贝叶斯方法是机器学习模型中不确定性量化的主要技术，模糊系统是第二常用的方法。关于深度学习模型，贝叶斯方法成为最普遍的方法，发现在医学成像的几乎所有方面的应用。本文报道的大多数研究都集中在医学图像上，与机器学习模型相比，突出了使用深度学习模型的不确定性量化技术的普遍应用。有趣的是,我们观察到缺乏将不确定性量化应用于生理信号的研究。因此，未来的不确定性量化研究应优先研究这些技术在生理信号中的应用。总的来说,我们的综述强调了在机器学习和深度学习模型的医疗保健应用中整合不确定性技术的重要性.这可以提供有价值的见解和实用的解决方案，以管理现实世界医疗数据中的不确定性，最终提高医疗诊断和治疗建议的准确性和可靠性。
