IEEE J Biomed Health Inform. 2020 Jul;24(7):1860-1863. doi: 10.1109/JBHI.2020.2970807. Epub 2020 Feb 10.
Medicine has entered the digital era, driven by data from new modalities, especially genomics and imaging, as well as new sources such as wearables and Internet of Things. As we gain a deeper understanding of the disease biology and how diseases affect an individual, we are developing targeted therapies to personalize treatments. There is a need for technologies like Artificial Intelligence (AI) to be able to support predictions for personalized treatments. In order to mainstream AI in healthcare we will need to address issues such as explainability, liability and privacy. Developing explainable algorithms and including AI training in medical education are many of the solutions that can help alleviate these concerns.
医学已经进入数字时代，新的模态产生的数据，尤其是基因组学和影像学，以及可穿戴设备和物联网等新来源，都推动了这一发展。随着我们对疾病生物学以及疾病如何影响个体的认识不断加深，我们正在开发针对个体治疗的靶向疗法。人工智能（AI）等技术需要能够支持个性化治疗的预测。为了使人工智能在医疗保健中得到普及，我们将需要解决可解释性、责任和隐私等问题。开发可解释的算法并将人工智能培训纳入医学教育是许多可以帮助缓解这些担忧的解决方案。
