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Abstract：
The rapid development of biosensing technologies together with the advent of deep learning has marked an era in healthcare and biomedical research where widespread devices like smartphones, smartwatches, and health-specific technologies have the potential to facilitate remote and accessible diagnosis, monitoring, and adaptive therapy in a naturalistic environment. This systematic review focuses on the impact of combining multiple biosensing techniques with deep learning algorithms and the application of these models to healthcare. We explore the key areas that researchers and engineers must consider when developing a deep learning model for biosensing: the data modality, the model architecture, and the real-world use case for the model. We also discuss key ongoing challenges and potential future directions for research in this field. We aim to provide useful insights for researchers who seek to use intelligent biosensing to advance precision healthcare.
摘要：
生物传感技术的快速发展以及深度学习的出现标志着医疗保健和生物医学研究的时代，智能手机等广泛使用的设备，智能手表,特定于健康的技术有可能促进远程和可访问的诊断，监测,和自然主义环境中的适应性治疗。本系统综述侧重于将多种生物传感技术与深度学习算法相结合的影响以及这些模型在医疗保健中的应用。我们探索了研究人员和工程师在开发生物传感深度学习模型时必须考虑的关键领域：数据模态。模型架构，和模型的实际用例。我们还讨论了该领域研究的主要挑战和潜在的未来方向。我们的目标是为寻求使用智能生物传感来推进精准医疗的研究人员提供有用的见解。
