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。
人工智能
不同MT平台的翻译比较
Wiki百科上关于Flexner Report里有句Flexner的原文,”An education in medicine,” wrote Flexner, “involves both learning and learning how; the student cannot effectively know, unless he knows how.” 有点拗口,看了下各个翻译平台的结果,可以列出来比较一下。
百度AI辅助临床诊断论文
机器之心上“百度11篇论文被国际自然语言处理顶级会议ACL 2020录用”的新闻,其中第11篇为“Towards Interpretable Clinical Diagnosis with Bayesian Network Ensembles Stacked on Entity-Aware CNNs”
AI领域的研究真是多
在PubMed里订阅的检索更新已经有近100篇没有看摘要了,这个更新速度真是夸张,看来得转变一下策略,肯定是要 … 阅读更多
Evolving technologies may yet provide the medicine that patients expect
4月初BMJ又发了一篇系统综述,并配发了同期述评,结论如标题。
Med_AI学习体会 1
思考的问题
- 机器学习是否需要理解信息的内容?信息本质规律(或者更准确的说是抽象规律)是否能代表信息本身?这种信息学上抽象逻辑是更本质的?
- 是否理解信息的内容才是判断智能的基础,而不是仅通过概率来模拟内容?
- 对于互联网上因立场不同而特意输出的混淆信息,计算机又如何能够识别?
刷了一遍第一篇综述 Deep learning
今天痛苦的、心潮澎湃的刷了一遍 2015年 Nature 上发的Deep Learning 综述,第一感觉是文章真是不错,就是很多地方看不懂,囫囵吞枣地先硬着头皮读了下去(所以说是刷),至少先有个大概印象,看看那些内容是重复的、重要的、核心的,后续再详细了解。读完搜了一下三位作者,因为国外期刊综述一般是邀请行业内文章主题领域内最权威的人来写,看看到底作者是哪位大牛(之前文献分析没看作者),结果是被誉为人工智能之父的三位大神。