Disease diagnosis, drug discovery, robot delivery — artificial intelligence is already powering change in the pandemic’s wake. That’s only the beginning.

疾病诊断，药物发现，机器人交付-人工智能已经推动了大流行的变化。 这仅仅是开始。

Dr. Kai-Fu Lee

李开复博士

ON NEW YEAR’S Eve the artificial intelligence platform BlueDot picked up an anomaly. It registered a cluster of unusual pneumonia cases in Wuhan, China. BlueDot, based in Toronto, Canada, uses natural language processing and machine learning to track, locate, and report on infectious disease spread. It sends out its alerts to a variety of clients including health care, government, business, and public health bodies. It had spotted what would come to be known as Covid-19, nine days before the World Health Organization released its statement alerting people to the emergence of a novel coronavirus.

在除夕夜，人工智能平台BlueDot发现了一个异常。 它在中国武汉登记了一系列异常肺炎病例。 位于加拿大多伦多的BlueDot使用自然语言处理和机器学习来跟踪，定位和报告传染病传播。 它会将警报发送给各种客户，包括医疗保健，政府，企业和公共卫生机构。 它在世界卫生组织发表声明警告人们注意新型冠状病毒出现的九天前发现了后来被称为Covid-19的东西。

BlueDot’s role in spotting the outbreak was an early example of AI intervention. Artificial intelligence has already played a useful but fragmented role in many aspects of the global fight against the coronavirus. In the past months, AI has been used for prediction, screening, contact alerts, faster diagnosis, automated deliveries, and laboratory drug discovery.

BlueDot在发现疫情中的作用是AI干预的早期例子。 人工智能已经在全球与冠状病毒斗争的许多方面发挥了有用但支离破碎的作用。 在过去的几个月中，AI已用于预测，筛选，联系警报，更快的诊断，自动递送和实验室药物发现。

As the pandemic has rolled around the planet, innovative applications of AI have cropped up in many different locations. In South Korea, location-based messaging has been a crucial tool in the battle to reduce the transmission of the disease. Nine out of 10 South Koreans have been getting location-based emergency messages that alert them when they are near a confirmed case.

随着大流行在地球上的蔓延，人工智能的创新应用已经出现在许多不同的地方。 在韩国，基于位置的消息传递一直是减少疾病传播的关键工具。 10名韩国人中有9人收到了基于位置的紧急消息，当他们靠近确诊病例时会向他们发出警报。

In China, Alibaba announced an AI algorithm that it says can diagnose suspected cases within 20 seconds (almost 60 times faster than human detection) with 96 percent accuracy. Autonomous vehicles were quickly put to use in scenarios that would have been too dangerous for humans. Robots in China’s Hubei and Guangdong provinces delivered food, medicine, and goods to patients in hospitals or quarantined families, many of whom had lost household breadwinners to the virus. In California, computer scientists are working on systems that can remotely monitor the health of the elderly in their homes and provide alerts if they fall ill with Covid-19 or other conditions.

在中国，阿里巴巴宣布了一种AI算法，该算法可以在20秒内(比人类检测快60倍)诊断可疑病例，准确率达96％。 自动驾驶汽车很快被用于对人类来说太危险的场景中。 中国湖北省和广东省的机器人为医院或被隔离家庭的患者提供了食品，药品和物品，其中许多人失去了家庭养家糊口的人。 在加利福尼亚州，计算机科学家正在研究可以远程监控家中老年人健康状况的系统，并在老年人因Covid-19或其他疾病而生病时提供警报。

These snapshots of AI in action against Covid-19 provide a glimpse of what will be possible in the various aspects of health care in the future. We have a long way to go. Truth be told, AI has not had a particularly successful four months in the battle of the pandemic. I would give it a “B minus” at best. We have seen how vulnerable our health care systems are: insufficient and imprecise alert responses, inadequately distributed medical supplies, overloaded and fatigued medical staff, not enough hospital beds, and no timely treatments or cures.

这些针对Covid-19行动的AI快照提供了未来医疗保健各个方面可能发生的情况的概览。 我们还有很长的路要走。 实话实说，在大流行的战斗中，人工智能还没有取得特别成功的四个月。 我最多只能给它“ B减”。 我们已经看到我们的卫生保健系统非常脆弱：警报响应不充分，不准确，医疗物资分配不足，医务人员超负荷和疲劳，病床不足，没有及时的治疗或治愈方法。

Health care systems around the world — even the most advanced ones — are some of the most complicated, hierarchical, and static institutions in society. This time around, AI has been able to help in only pockets of excellence. The reasons for this are simple: Before Covid-19 struck, we did not understand the importance of these areas and act accordingly, and crucially as far as AI is concerned, we did not have the data to deliver the solutions.

全世界的卫生保健系统(甚至是最先进的系统)都是社会上最复杂，等级最高且静态的机构。 这次，AI仅能提供卓越的帮助。 原因很简单：在Covid-19袭击之前，我们不了解这些领域的重要性并采取相应的行动，而就AI而言，至关重要的是，我们没有数据来提供解决方案。

LET’S LOOK TO the future. There are two grounds for optimism.

让我们展望未来。 乐观有两个理由。

The first is that data, always the lifeblood of AI, is now flowing. Kaggle, a machine-learning and data science platform is hosting the Covid-19 Open Research Dataset. CORD-19, as it is known, compiles relevant data and adds new research into one centralized hub. The new data set is machine readable, making it easily parsed for AI machine learning purposes. As of publication, t here are more 128,000 scholarly articles on Covid-19, coronavirus, SARS, MERS, and other relevant terms.

首先是数据，一直是AI的生命线，现在正在流动。 机器学习和数据科学平台Kaggle托管着Covid-19开放研究数据集 。 众所周知，CORD-19汇编相关数据并将新的研究成果添加到一个集中式枢纽中。 新数据集是机器可读的，因此可以轻松解析以用于AI机器学习。 截至发表时，这里有128,000多篇关于Covid-19，冠状病毒，SARS，MERS和其他相关术语的学术文章。

The second is that medical scientists and computer scientists across the world are now laser-focused on these problems. Peter Diamandis, founder of the XPrize Foundation, estimated that up to 200 million physicians, scientists, nurses, technologists, and engineers are now taking aim at Covid-19. They are running tens of thousands of experiments and sharing information “with a transparency and at speeds we’ve never seen before.”

第二个问题是，全世界的医学科学家和计算机科学家现在都将注意力集中在这些问题上。 XPrize基金会的创始人Peter Diamandis估计，目前有多达2亿名医生，科学家，护士，技术人员和工程师将目标对准Covid-19。 他们正在进行成千上万次实验，并“以前所未有的透明性和速度共享信息”。

The Covid-19 Research Challenge, also hosted on Kaggle, aims to provide a broad range of insights about the pandemic, including its natural history, transmission data and diagnostic criteria for the virus, and lessons from previous epidemiological studies to help global health organizations stay informed and make data-driven decisions. The challenge was released on March 16. Within five days it had already garnered more than 500,000 views and been downloaded more than 18,000 times.

也是在Kaggle上举办的Covid-19研究挑战赛的目的是提供有关大流行的广泛见解，包括其自然史，病毒的传播数据和诊断标准，以及以前的流行病学研究的教训，以帮助全球卫生组织掌握信息并做出以数据为依据的决策。 该挑战赛于3月16日发布。在五天内，它已经获得了超过500,000次观看，并下载了18,000次以上。

In the first month of the outbreak in China, Alibaba released an AI algorithm trained on more than 5,000 confirmed coronavirus cases. Using CT scans, it can diagnose patients in 20 to 30 seconds. It can also analyze the scans of diagnosed patients and quickly assess health declines or progress, based on signs like white mass in the lungs. Alibaba opened its cloud-based AI platform to medical professionals around the world, working with local partners on anonymous data for deployment, including modules for epidemic prediction, CT Image analytics, and genome sequencing for coronavirus.

在中国爆发的第一个月，阿里巴巴发布了针对5,000例确诊冠状病毒病例的AI算法。 使用CT扫描，它可以在20到30秒内诊断出患者。 它还可以根据肺部白色肿块等体征，对诊断出的患者进行扫描，并快速评估健康状况的下降或进展。 阿里巴巴向全球医疗专业人员开放了其基于云的AI平台，与本地合作伙伴合作以部署匿名数据，其中包括流行病预测模块，CT图像分析和冠状病毒基因组测序。

With the amount of medical data in the world now estimated to double every couple of months or so, health care was ripe for AI — even before the virus struck. A 2019 study covering 19 countries’ artificial intelligence health care markets estimated a 41.7 percent compound annual growth rate, from $1.3 billion in 2018 to $13 billion in 2025 in six major growth areas: hospital workflow, wearables, medical imaging and diagnosis, therapy planning, virtual assistants, and lastly but most significantly, drug discovery. Covid-19 will accelerate those trends rapidly.

现在，全世界的医疗数据量估计每隔几个月就要翻一番，因此AI的医疗保健已经成熟-甚至在病毒感染之前。 2019年针对19个国家/地区的人工智能医疗保健市场进行的研究估计，以下六个主要增长领域的复合年增长率为41.7％，从2018年的13亿美元增长到2025年的130亿美元：医院工作流程，可穿戴设备，医学影像和诊断，治疗计划，虚拟助手，最后但也是最重要的是药物发现。 Covid-19将Swift加快这些趋势。

Deep learning — the capability to process massive, multi-model data at high speeds — presents one of the most far reaching opportunities for AI. Deep neural networks, a subtype of AI, have already been used to produce accurate and rapid algorithmic interpretation of medical scans, pathology slides, eye exams, and colonoscopies. I see a clear roadmap of how AI, accelerated by the pandemic, will be infused into health care.

深度学习-高速处理海量多模型数据的能力-为AI提供了最广泛的机会之一。 深度神经网络是AI的一种子类型，已经用于对医学扫描，病理切片，眼科检查和结肠镜检查进行准确而快速的算法解释。 我看到了一条清晰的路线图，说明如何将由大流行加速的AI注入医疗保健。

THE POTENTIAL GOES beyond diagnosis and treatment. Getting appointments, paying insurance bills, and other processes should be much less painful. AI combined with robotic process automation can analyze workflows and optimize processes to deliver significantly more efficient medical systems, improve hospital procedures, and streamline insurance fulfillment. To address the pandemic, AI could automate and accelerate pre-diagnostic inputs by crunching texts, languages, and numbers at machine-level quantity and precision.

潜力超出了诊断和治疗范围。 获得约会，支付保险单和其他程序的痛苦应该大大减轻。 AI与机器人流程自动化相结合，可以分析工作流程并优化流程，以提供效率更高的医疗系统，改善医院程序并简化保险流程。 为了解决这一大流行，人工智能可以通过以机器级别的数量和精度处理文本，语言和数字来自动化和加速预诊断输入。

With sufficient data as a foundation, AI can also establish health data benchmarks for individuals and for population. From there, it’s possible to detect variations from the baseline. That, in turn, positions us to identify potential pandemics early. It’s not easy. Systems need to be connected so that early alert and response mechanisms can be truly effective. That appeared to be a shortcoming in the early days of the coronavirus’ outbreak.

以足够的数据为基础，人工智能还可以为个人和人群建立健康数据基准。 从那里，可以检测到基线的变化。 反过来，这也使我们能够尽早发现潜在的大流行病。 这并不容易。 需要连接系统，以便早期警报和响应机制真正有效。 在冠状病毒爆发初期，这似乎是一个缺点。

There are already huge opportunities for using AI models and algorithms for new drug discovery and medical breakthroughs in genomic sequencing, stem cells, CRISPR, and more. In today’s pharmaceutical world, there is a hefty price tag to developing a treatment. A huge part of this cost is eaten up by the money and time spent on unsuccessful trials. But with AI, scientists can use machine learning to model thousands of variables and how their compounded effect may influence the responses of human cells.

使用AI模型和算法进行基因组测序，干细胞，CRISPR等方面的新药发现和医学突破已经有巨大的机会。 在当今的制药世界中，开发治疗药物的价格很高。 不成功的审判所花费的金钱和时间会消耗掉这笔费用中的很大一部分。 但是，借助AI，科学家可以使用机器学习对数千个变量进行建模，以及它们的复合效应如何影响人类细胞的React。

These technologies are already being used in the hunt for a Covid-19 vaccine and other therapies. Insilico Medicine, a Hong Kong-based AI company specializing in drug discovery, was among the first companies to react to Covid-19. The company used its generative chemistry AI platform to design new molecules to target the main viral protein responsible for replication. It published the molecules on February 5. AI and machine learning are ushering in an era of faster and cheaper cures for mankind. Drug discovery and the pharmaceutical industry as a whole will be revolutionised.

这些技术已经用于寻找Covid-19疫苗和其他疗法。 Insilico Medicine是一家总部位于香港的AI公司，专门从事药物发现，是最早对Covid-19做出React的公司之一。 该公司使用其生成化学AI平台设计新分子，以靶向负责复制的主要病毒蛋白为目标。 它于2月5日发表了这些分子。人工智能和机器学习正迎来一个更快，更便宜的人类治愈方法的时代。 药物发现和整个制药行业将发生革命性的变化。

EARLY ONE WINTER morning in the year 2035, I wake up and notice a bit of a sore throat. I get up and walk to the bathroom. While I brush my teeth, an infrasensor in the bathroom mirror takes my temperature. A minute after I finish brushing my teeth, I receive an alert from my personal AI physician assistant showing some abnormal measurements from my saliva sample and that I am also running a low fever. The AI PA further suggests that I take a fingertip needle touch blood test. While the coffee is brewing, the PA returns with the analysis that I might be coming down with the flu, one of the two types around this season. My PA suggests two video call time slots with my family doctor, should I feel the need to consult her. She will have all the details of my symptoms when I make the call. She prescribes a decongestant and paracetamol which is delivered to my door by drone.

2035年一个冬天的早晨，我醒来时发现喉咙有点痛。 我起身去洗手间。 当我刷牙时，浴室镜子中的红外线传感器会测量我的体温。 刷完牙后的一分钟，我的私人AI医师助手收到一条警报，显示我的唾液样本中有一些异常测量值，并且我也在发低烧。 AI PA还建议我进行指尖针接触式血液检查。 在冲泡咖啡时，PA会返回分析，可能是我感冒了，这是本季节左右两种类型之一。 如果我觉得有必要咨询她，我的助理将与我的家庭医生建议两个视频通话时间段。 我打电话时，她将提供我所有症状的详细信息。 她开了一种去充血药和扑热息痛，然后用无人机将其送到我家。

That future is not as far off as it seems. Soon, as medical science and computer science further converge, we will move into an era of fully autonomous AI when we may expect people to choose wearables, biosensors, and smart home detectors to keep them safe and informed. And, as data quality and diversity increase from the wearables and other internet-of-things devices, a virtuous cycle of improvements will kick in.

那个未来并不遥远。 不久，随着医学和计算机科学的进一步融合，当我们期望人们选择可穿戴设备，生物传感器和智能家居检测器以确保他们的安全和知情时，我们将进入一个完全自主的AI时代。 而且，随着可穿戴设备和其他物联网设备的数据质量和多样性的提高，将出现良性循环的改善。

In this world a novel coronavirus could be tracked, traced, intercepted, and cut off before it got going. In perhaps 15 years, many of us will have AI personal assistants in our households to keep us supported for our families’ day-to-day health issues. Robots or drones will deliver medication to our doors. If a surgery or some other medical intervention is needed, usually it will be a robot performing or assisting a human surgeon or doctor.

在这个世界上，新型冠状病毒可以在开始流行之前被追踪，追踪，拦截和切断。 也许在15年后，我们中的许多人将在家庭中拥有AI个人助理，以使我们在家庭的日常健康问题上得到支持。 机器人或无人机将把药物运送到我们的门。 如果需要进行手术或其他医学干预，通常将由机器人来执行或协助外科医生或医生。

In this future doctors and nurses will focus more on the human tasks that no machine can do. The medical professionals or compassionate caregivers will combine the skills of a nurse, medical technician, social worker, and even psychologist. They will operate the AI-enhanced diagnostic tools and systems, but they will concentrate on communicating with patients, consoling them in times of trauma, and emotionally supporting them through their treatment.

在未来的将来，医生和护士将更多地关注没有机器可以完成的人工任务。 医疗专业人员或富有同情心的护理人员将结合护士，医疗技术人员，社会工作者甚至心理学家的技能。 他们将使用AI增强的诊断工具和系统，但将专注于与患者沟通，在受伤时安慰他们，并通过治疗为他们提供情感支持。

In all this there are the key issues of privacy and data protection, particularly when it comes to patients’ records. It would be irresponsible to let useful data sit in their own isolated compartments, instead of extracting their usefulness to serve the progress of our societies. I am a big proponent of using innovative technological solutions to solve newly arisen technology issues, and the good news is that there has been progress made in federated learning, also known as distributed learning. In this framework, patients’ data is stored and never leaves their host health system or hospitals or personal devices, as machine learning models are trained from separate datasets, processed and combined subsequently. Technologies, such as federated learning, homomorphic encryption, and trusted hardware execution environments would further ensure data is computed, transmitted, and stored to meet preferred settings, as privacy requirements vary around different countries and cultures.

在所有这些方面，都存在着隐私和数据保护的关键问题，尤其是在涉及患者记录时。 将有用的数据放在自己孤立的隔间中，而不是提取它们的有用性来服务于我们的社会进步，这是不负责任的。 我大力支持使用创新技术解决方案来解决新出现的技术问题，而且好消息是联邦学习(也称为分布式学习)已经取得了进展。 在此框架中，由于机器学习模型是从单独的数据集中训练，随后进行处理和合并的，因此患者的数据将被存储，并且永远不会离开其宿主健康系统，医院或个人设备。 诸如联邦学习，同态加密和受信任的硬件执行环境之类的技术将进一步确保数据的计算，传输和存储可以满足首选设置，因为不同国家和地区的隐私要求有所不同。

IF NOTHING ELSE, Covid-19 has proven that our shared challenges call for AI that recognizes how intertwined our destinies are. In the past global collaboration has led to the eradication of smallpox and the near-eradication of polio. As we work toward the goal of mitigating, treating, and eradicating the pandemic, it is clear that public health does not stop at national borders. Medicine is an arena where every country will benefit from building on, and with, others’ research. The whole world’s data will generate the most robust insights into health and disease.

如果没有其他要求，Covid-19证明了我们共同面临的挑战需要AI来认识我们命运的交织。 过去，全球合作导致根除天花和近乎根除小儿麻痹症。 当我们朝着减轻，治疗和消除大流行的目标努力时，很明显，公共卫生并没有在国界停止。 医学是一个竞技场，每个国家都可以从其他研究的基础上以及其他研究中受益。 全世界的数据将对健康和疾病产生最强大的见解。

AI will help ensure we will be better prepared for the next pandemic. It will need medical scientists, AI scientists, investors, and policy makers to collaborate. Venture capital is going to pour into healthcare and provide fresh impetus and focus for smart entrepreneurs and researchers. And, perhaps, as our brightest minds work on this challenge together, we can emerge acknowledging that our common enemy is not each other but a virus. It will take a planet to move our global healthcare systems to the next level.

人工智能将帮助确保我们为下一次大流行做好更好的准备。 它将需要医学家，人工智能科学家，投资者和决策者进行协作。 风险资本将注入医疗保健领域，并为聪明的企业家和研究人员提供新的动力和重点。 而且，也许，当我们最聪明的人共同努力应对这一挑战时，我们可以认识到我们共同的敌人不是彼此而是病毒。 将我们的全球医疗保健系统提升到一个新的高度需要一个星球。

Kai-Fu Lee, Ph.D., is the Chairman and CEO of Sinovation Ventures.

李开复博士，是Sinovation Ventures的董事长兼首席执行官。

The article first appeared on WIRED Backchannel: https://www.wired.com/story/covid-19-will-accelerate-ai-health-care-revolution/

该文章首次出现在WIRED Backchannel： https : //www.wired.com/story/covid-19-will-accelerate-ai-health-care-revolution/