Chandra Jay, Lin Raymond, Kancherla Devin, Scott Sophia, Sul Daniel, Andrade Daniela, Marzouk Sammer, Iyer Jay M, Wasswa William, Villanueva Cleva, Celi Leo Anthony
Harvard Medical School, Harvard University, Boston, Massachusetts, United States of America.
Global Alliance for Medical Innovation, Cambridge, Massachusetts, United States of America.
PLOS Digit Health. 2024 Sep 19;3(9):e0000574. doi: 10.1371/journal.pdig.0000574. eCollection 2024 Sep.
In recent years, there has been substantial work in low-cost medical diagnostics based on the physical manifestations of disease. This is due to advancements in data analysis techniques and classification algorithms and the increased availability of computing power through smart devices. Smartphones and their ability to interface with simple sensors such as inertial measurement units (IMUs), microphones, piezoelectric sensors, etc., or with convenient attachments such as lenses have revolutionized the ability collect medically relevant data easily. Even if the data has relatively low resolution or signal to noise ratio, newer algorithms have made it possible to identify disease with this data. Many low-cost diagnostic tools have been created in medical fields spanning from neurology to dermatology to obstetrics. These tools are particularly useful in low-resource areas where access to expensive diagnostic equipment may not be possible. The ultimate goal would be the creation of a "diagnostic toolkit" consisting of a smartphone and a set of sensors and attachments that can be used to screen for a wide set of diseases in a community healthcare setting. However, there are a few concerns that still need to be overcome in low-cost diagnostics: lack of incentives to bring these devices to market, algorithmic bias, "black box" nature of the algorithms, and data storage/transfer concerns.
近年来，基于疾病的物理表现开展了大量关于低成本医学诊断的工作。这得益于数据分析技术和分类算法的进步，以及通过智能设备计算能力的可用性增加。智能手机及其与惯性测量单元（IMU）、麦克风、压电传感器等简单传感器或镜头等便捷附件连接的能力，彻底改变了轻松收集医学相关数据的能力。即使数据的分辨率或信噪比相对较低，更新的算法也使得利用这些数据识别疾病成为可能。在从神经学到皮肤病学再到妇产科的医学领域，已经创建了许多低成本诊断工具。这些工具在无法获得昂贵诊断设备的低资源地区特别有用。最终目标是创建一个“诊断工具包”，它由一部智能手机以及一组可用于在社区医疗环境中筛查多种疾病的传感器和附件组成。然而，低成本诊断仍有一些问题需要克服：将这些设备推向市场缺乏激励措施、算法偏差、算法的“黑箱”性质以及数据存储/传输问题。
