2022回望呓语

作者

自2019年底新冠疫情出现之后，媒体环境就让人非常不舒服，感觉在危机时刻，所有的痼疾都会暴露出来，猜忌、隐形暴力、情绪宣泄等原本就存在于世需要被克制的负面（像7宗罪一样），被洞悉人性的商业/政治逻辑广泛利用，无论是传统媒体的倒退还是自媒体的劣胜优汰，都让人非常失望。

思考这其中的原因，在大的社会背景下，有社会制度性的矛盾冲突，如商业社会的逻辑、国家利益的冲突，让人们很难合理应对“影响全人类命运”的重大危机；在微观层面，有个人思维方式、思维逻辑、科学素养、人文精神的亟待提升，我们离“公民”的要求还相距甚远。在面对普通危机时，人们往往更倾向于工具理性，舍弃价值理性，但在面临重大危机时，尤其是在应对得“稀烂”的情况下，应该会激发更多的反思从而回归价值理性。

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Artificial Intelligence for Computer Vision in Surgery: A Call for Developing Reporting Guidelines

作者

Advances in computing power and the availability of digital data have led to significant progress in artificial intelligence (AI) algorithms. As a result, novel and innovative applications of AI in healthcare continue to surface both in the scientific community and the lay press at a rapid pace. AI is the field of computer science that focuses on the development of algorithms that enable high-level and rational response, interaction, and advanced cognitive and perceptual functions by machines. One area of AI that has particularly bourgeoned over the last decade is computer vision (CV)— an interdisciplinary scientific field that deals with how computers can gain a high-level understanding of digital images or videos and the ability to perform functions, such as object identification and tracking and scene recognition1. Various fields in medicine have had significant success in the development of AI models capable of performing a variety of diagnostic functions using CV (e.g., identifying abnormalities in diagnostic radiology, identifying malignant skin lesions, and interpreting electrocardiograms), and there is potential for similar success in procedural specialties such as surgery. Clinicians and innovators alike have sought to develop AI algorithms capable of improving our ability to provide therapeutic interventions, such as with real-time decision-support and computer-assisted surgery. 计算能力的进步和数字数据的可用性导致了人工智能（AI）算法的重大进展。因此，人工智能在医疗保健领域的新颖和创新的应用继续以很快的速度出现在科学界和非专业媒体上。人工智能是计算机科学的一个领域，其重点是开发算法，使机器能够做出高水平的理性反应、互动以及高级认知和感知功能。在过去十年里，人工智能的一个领域特别蓬勃发展，那就是计算机视觉（CV）–这是一个跨学科的科学领域，涉及到计算机如何获得对数字图像或视频的高层次理解，以及执行功能的能力，如物体识别和跟踪以及场景识别1。医学的各个领域在开发能够使用CV执行各种诊断功能的人工智能模型方面取得了重大成功（例如，在诊断放射学中识别异常，识别恶性皮肤病变，以及解释心电图），并且有可能在外科等程序性专业领域取得类似的成功。临床医生和创新者都在寻求开发能够提高我们提供治疗性干预能力的人工智能算法，如实时决策支持和计算机辅助手术。

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