人工智能 ai基础知识
Only 7 percent of a message is based on the words it contains. The rest, 93 percent, comes from the speaker’s tone of voice, body language and facial expressions (1).
消息中只有7％是基于消息中包含的单词。 其余的93％来自说话者的语气，肢体语言和面部表情(1) 。
Imagine a visit to the doctor. You describe your situation and symptoms while the doctor looks and listens carefully. Imagine that this consultation is being recorded on video.
想象一下去看医生。 您在医生仔细观察和聆听的同时描述您的情况和症状。 想象一下，这次咨询正在录制在视频中。
This video will then contain your speech (text, words) that can be automatically extracted as a transcript and analyzed. Your voice and its related characteristics like tone, volume or tremor can also be extracted and analyzed. Similarly, your movements including facial expressions, fidgeting, hand gestures, posture and physical distance can be analyzed.
然后，该视频将包含您的语音(文本，单词)，可以自动将其提取为笔录并进行分析。 您的声音及其相关特征(如语气，音量或震颤)也可以提取和分析。 同样，可以分析您的动作，包括面部表情，坐立不安，手势，姿势和身体距离。
Let’s consider the video constituents that can be analyzed separately:
让我们考虑可以单独分析的视频成分：
· The text can be summarized with main symptoms extraction which then can be fed to an internal medical knowledge base (ontology) module through which diagnostics insights and related recommendations could be provided.
·可以用主要症状摘录对文本进行总结，然后将其提取到内部医学知识库(本体论)模块中，通过该模块可以提供诊断见解和相关建议。
· Your voice and its characteristics, or vocal biomarkers (a medical biomarker is a medical sign that indicates a patient’s medical state as observed from “outside” the patient) can be used to explore the likelihood of the presence a specific disease. Among various related studies that illustrate this, in a double-blind study carried out by Beyond Verbal and the Mayo Clinic which involved 120 patients and a group of controls undergoing coronary angiography in the context of Coronary Artery Disease (CAD), a mobile app was used to measure their voice signal prior to a coronary angiograph. One voice characteristic in particular (not audible by the human ear) indicated an almost 20-fold increase in the likelihood of CAD.
·您的声音及其特征或声音生物标志物(医学生物标志物是指从患者“外部”观察到的指示患者的医学状态的医学体征)，可用于探索存在特定疾病的可能性。 在说明这一点的各种相关研究中，由Beyond Verbal和Mayo Clinic进行的一项双盲研究涉及120例患者和一组在冠状动脉疾病(CAD)背景下接受冠状动脉造影的对照组，其中一个移动应用是用于在冠状动脉造影之前测量其语音信号。 特别是一种声音特征(人耳无法听到)表明CAD的可能性几乎提高了20倍。
· The video in itself can be used to analyze body language and facial expressions. As an example, there is no specific test to definitively diagnose Parkinson’s disease. Neurologists clinically diagnose Parkinson’s based on medical history, a review of signs and symptoms, and a physical examination. If we train a machine learning based AI system to learn body movement, facial expressions, tremor and so on from a number of Parkinson patient video interviews, this can be used to potentially diagnose the disease given newly recorded (or even live streamed) patient consultations. The accuracy that can be achieved, given for example a training and testing data set of 10.000 interviews, would be positively surprising.
·视频本身可用于分析肢体语言和面部表情。 例如，没有可以明确诊断帕金森氏病的特定测试。 神经科医生根据病史，体征和症状检查以及体格检查对帕金森氏病进行临床诊断。 如果我们训练基于机器学习的AI系统以通过多次帕金森病患者视频采访来学习身体运动，面部表情，震颤等，则可以根据新记录(甚至实时直播)的患者咨询来潜在地诊断疾病。 例如，给定一个包含10,000个访谈的培训和测试数据集，所能达到的准确性将令人惊讶。
Intelligent Digital Medical Assistants
智能数字医疗助手
The AI powered processing of patient interaction using text, voice and video along with a knowledge base of medical knowledge, patient history and an automated communications module are the tools that constitute an Intelligent Digital Medical Assistant. Eventually, most clinicians and private practices will use this technology to initially interact with their patients.
使用文本，语音和视频以及医学知识知识库，患者病历和自动通讯模块的AI驱动的患者交互处理是构成智能数字医疗助手的工具。 最终，大多数临床医生和私人诊所将使用该技术与患者进行最初的互动。
Importantly however, digital medical assistants will never replace experienced doctors. They will simply make some doctors better than others that are not using this technology! Doctors will have more time for meaningful consultations with patients.
但是，重要的是，数字医疗助手将永远无法取代经验丰富的医生。 他们只会使一些医生比不使用该技术的医生更好！ 医生将有更多时间与患者进行有意义的咨询。
Inevitably the future of healthcare will involve patients initially getting in touch with intelligent medical digital assistants through the web, mobile or IOT interfaces that will automatically analyze patient speech, voice and videos. Unless comfortably addressed, the issues raised will then be automatically transferred to a medical professional for further consultation.
不可避免地，医疗保健的未来将涉及患者最初通过Web，移动或IOT界面与智能医疗数字助理进行联系，这些界面将自动分析患者的语音，语音和视频。 除非舒适地解决，否则提出的问题将自动转移给医疗专业人员进行进一步咨询。
In summary, Intelligent Digital Medical Assistants will:
总之，智能数字医疗助手将：
· Reply to questions using an internal medical knowledge ontology (for example it will store all we know about sore throats in order to respond to related questions)
·使用内部医学知识本体回答问题(例如，它将存储我们对咽痛的所有了解，以便回答相关问题)
· Use Machine Learning and Big Data to provide insights based on previous patient health parameters and outcomes (i.e. given patient X with Y blood tests and underlying disease A it would be useful to run test Z, or to watch out for condition B)
·使用机器学习和大数据来基于以前的患者健康参数和结果提供见解(即，给具有X血液测试和潜在疾病A的患者X，运行测试Z或注意状况B会很有用)
· Track patient health parameters (fluctuations, repeated symptoms)
·跟踪患者的健康参数(波动，重复症状)
· Manage medication
·管理药物
· Provide simple actionable recommendations
·提供简单可行的建议
· Provide personalized health tips and lifestyle coaching.
·提供个性化的健康提示和生活方式指导。
· Immediately provide first line help in emergency situations
·在紧急情况下立即提供第一线帮助
Conclusion
结论
Artificial Intelligence (AI) will fully transform health care. It can improve outcomes and patient experience while democratizing access to healthcare services.
人工智能(AI)将彻底改变医疗保健。 它可以改善结果和患者体验，同时使获得医疗服务的民主化。
AI can help improve the experience of healthcare practitioners, enabling them to reduce burnout and spend more time in serious direct patient care. AI can help healthcare systems manage population health proactively through the allocation of resources with a view to maximum impact.
AI可以帮助改善医疗保健从业者的体验，使他们减少倦怠并在认真的直接患者护理上花费更多时间。 AI可以通过分配资源来帮助医疗保健系统主动管理人口健康，以最大程度地发挥作用。
Take away
带走
Using a mobile or web based Intelligent Digital Medical Assistant through which to provide remote video based medical consultations and almost instantaneously extracting knowledge from the video in order to support or suggest a diagnosis, while at the same time using the same system to organize and follow up on these interactions, is the way to use AI technology to improve outcomes and efficiency in primary healthcare, particularly in the midst of a global pandemic!
使用基于移动或网络的智能数字医疗助手，通过该智能助手可以提供基于远程视频的医疗咨询，并几乎即时从视频中提取知识以支持或建议诊断，同时使用同一系统进行组织和跟进在这些互动中，这就是使用AI技术改善初级医疗保健(尤其是在全球大流行中)的结果和效率的方法！
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(1) Albert Mehrabian’s definitive study
(1)阿尔伯特·梅哈拉比安的权威研究
人工智能 ai基础知识