TE||From A&E to AI
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导读
机智过人
人工智能和医学专家的对决
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听力|精读|翻译|词组
From A&E to AI
从急诊室到人工智能
英文部分选自经济学人Science and technology版块
Medicine
医学
Artificial intelligence will improve the speed and precision of medical treatments
AI将提高医学诊疗的速度和精确度
FOUR years ago a woman in her early 30s was hit by a car in London. She needed emergency surgery to reduce the pressure on her brain. Her surgeon, Chris Mansi, remembers the operation going well. But she died, and Mr Mansi wanted to know why. He discovered that the problem had been a four-hour delay in getting her from the accident and emergency unit of the hospital where she was first brought, to the operating theatre in his own hospital. That, in turn, was the result of a delay in identifying, from medical scans of her head, that she had a large blood clot in her brain and was in need of immediate treatment. It is to try to avoid repetitions of this sort of delay that Mr Mansi has helped set up a firm called Viz.ai. The firm’s purpose is to use machine learning, a form of artificial intelligence (AI), to tell those patients who need urgent attention from those who may safely wait, by analysing scans of their brains made on admission.
四年前，一名30多岁的女士在伦敦被一辆小汽车撞倒。她需要紧急手术来减轻颅内压力。她的外科医生克里斯·曼西（Chris Mansi）记得手术进展得很顺利。但她还是死了，曼西先生想知道为什么。后来，他查明，离开事故现场后，病人先被送到了就近医院的急诊部门，然后又送到他的医院进行手术，这中间耽误了四个小时。这反过来耽误了诊断，在进行头部扫描时发现了一个很大的血块，需要立即治疗。为了避免类似情况再次上演，曼西先生帮助建立了一家名为Viz.ai的公司。该公司致力于运用机器学习（一种AI（AI））的形式，分析病患入院时进行的大脑扫描图，在可以安全等候的病患中分辨出那些需要立即治疗的病患。
That idea is one among myriad projects now under way with the aim of using machine learning to transform how doctors deal with patients. Though diverse in detail, these projects have a common aim. This is to get the right patient to the right doctor at the right time.
这个想法是目前正在进行的无数项目之一，目的是利用机器学习来改变医生对待病人的方式。虽然细节多样，但这些项目有一个共同的目标，即是为了让合适的病人在正确的时间找到合适的医生。
In Viz.ai’s case that is now happening. In February the firm received approval from regulators in the United States to sell its software for the detection, from brain scans, of strokes caused by a blockage in a large blood vessel. The technology is being introduced into hospitals in America’s “stroke belt”—the south-eastern part, in which strokes are unusually common. Erlanger Health System, in Tennessee, will turn on its Viz.ai system next week.
在Viz.ai的案例中，这样的事情正在发生着。今年2月，该公司获得美国监管机构的批准，出售软件，用在脑部扫描图中检测导致中风的大血管阻塞。美国“中风带”——东南部的医院正在引入该项技术，脑中风疾病在这里非常普遍。田纳西州的厄尔格（Erlanger）健康系统下周将启用Viz.ai系统。
注释：
1.Erlanger Health System: 厄尔格健康系统位于田纳西州的大查塔努加区，包括5家医院，818张急性病治疗床位。摘自有道词典
厄尔格健康系统主页https://www.erlanger.org
The potential benefits are great. As Tom Devlin, a stroke neurologist at Erlanger, observes, “We know we lose 2m brain cells every minute the clot is there.” Yet the two therapies that can transform outcomes—clot-busting drugs and an operation called a thrombectomy—are rarely used because, by the time a stroke is diagnosed and a surgical team assembled, too much of a patient’s brain has died. Viz.ai’s technology should improve outcomes by identifying urgent cases, alerting on-call specialists and sending them the scans directly.
未来的收益前景极好。厄尔格的中风神经科医生汤姆·德夫林（Tom Devlin）发现：“大脑出现血块的时候，我们每分钟就会失去两百万脑细胞。” 虽然，有两种治疗方案能改变状况——有消除血块的药物和血栓切除手术；但是，等到中风确诊，手术人员准备就绪的时候，患者脑部的大部分区域已经死亡，这两种治疗方案也就很少能派上用场了。Viz.ai的技术通过识别紧急病例，通知值班的专科医生，直接安排患者做扫描,应该可以改善患者的状况。
注释：
specialist: a doctor who has specialized in a particular area of medicine 专科医生 栗子：a cancer specialist 癌症专科医生 （摘自《牛津高阶英汉双解词典》）
The AIs have it
AI能行
Another area ripe for AI’s assistance is oncology. In February 2017 Andre Esteva of Stanford University and his colleagues used a set of almost 130,000 images to train some artificial-intelligence software to classify skin lesions. So trained, and tested against the opinions of 21 qualified dermatologists, the software could identify both the most common type of skin cancer (keratinocyte carcinoma), and the deadliest type (malignant melanoma), as successfully as the professionals. That was impressive. But now, as described last month in a paper in the Annals of Oncology, there is an AI skin-cancer-detection system that can do better than most dermatologists. Holger Haenssle of the University of Heidelberg, in Germany, pitted an AI system against 58 dermatologists. The humans were able to identify 86.6% of skin cancers. The computer found 95%. It also misdiagnosed fewer benign moles as malignancies.
AI能提供帮助的另一个领域是肿瘤治疗领域（或者翻：应用AI的时机已经成熟的另一领域是肿瘤治疗领域）。2017年二月，斯坦福大学的安德烈·埃斯特瓦（Andre Esteva）和他的同事们使用近130,000张照片来训练一些AI软件对皮肤损伤进行分类。经过大量训练，并对21名合格的皮肤病专家的诊断意见进行测试，这种软件可以像专业人士一样，能成功地识别出最常见的皮肤癌(角质细胞癌) 和最致命的皮肤癌（恶性黑色素瘤）。这还是很让人佩服的。不过，就在上个月，杂志《肿瘤学年鉴》中的文章提到，有一种AI皮肤癌检测系统能比大多数的皮肤病专家做得更好。德国海德堡大学的霍格尔·汉斯勒（Holger Haenssle），用AI系统和58名皮肤病专家斗智。人类可以发现86.6%的皮肤癌。计算机可以发现95%的皮肤癌，将良性痣误诊为恶性肿瘤的情况也更少。
注释：
1. ripe for: ready or suitable for sth to happen 时机成熟的；适宜的； 栗子：The conditions were ripe for social change. 社会变革的时机已经成熟。（摘自《牛津高阶英汉双解词典》）
2. test against: 对……进行测试 栗子：But we obviously didn't know, because we didn't entirely test it against their own test systems, and of course, it was a historic moment. 但我们其实并不知道，因为我们没有用整个测试系统来对它进行测试。（摘自BBC）
There has been progress in the detection of breast cancer, too. Last month Kheiron Medical Technologies, a firm in London, received news that a study it had commissioned had concluded that its software exceeded the officially required performance standard for radiologists screening for the disease. The firm says it will submit this study for publication when it has received European approval to use the AI—which it expects to happen soon.
Ai系统在乳腺癌检测方面也取得了进展。上个月，伦敦的喀戎医疗技术（Kheiron Medical Technologies）公司收到消息，称受委托的研究已有结论，该公司软件的疾病筛查效果超过了放射科医生放射筛查要达到的官方标准。该公司表示，他们获得欧洲的AI使用许可后（预计很快就会获得），他们会向大众公布该项研究。
This development looks important. Breast screening has saved many lives, but it leaves much to be desired. Overdiagnosis and overtreatment are common. Conversely, tumours are sometimes missed. In many countries such problems have led to scans being checked routinely by a second radiologist, which improves accuracy but adds to workloads. At a minimum Kheiron’s system looks useful for a second opinion. As it improves, it may be able to grade women according to their risks of breast cancer and decide the best time for their next mammogram.
这一研究成果显得非常重要。乳房筛查已经挽救了许多人的生命，但仍有许多有待改进之处。当前，过度诊断和过度治疗的问题十分常见，与此相反，肿瘤有时却会被忽略掉。在许多国家，这些问题致使另一名放射科医生需要再次对患者进行例行检查扫描，虽然这提高了检查的准确性，但也增加了工作量。至少，喀戎的系统似乎能在再次检查中发挥作用。随着性能不断改善，该系统也许能够根据妇女患乳腺癌的风险对她们进行分级，决定她们下一次做乳房X光检查的最佳时机。
注释：
1. 熟词生义 development: NEW PRODUCT 新产品 [不可数名词, 可数名词] the process of producing or creating sth new or more advanced; a new or advanced product 开发；研制；研制成果;栗子：the development of vaccines against tropical diseases热带疾病疫苗的研制 （摘自《牛津高阶英汉双解词典》）
Efforts to use AI to improve diagnosis are under way in other parts of medicine, too. In eye disease, DeepMind, a London-based subsidiary of Alphabet, Google’s parent company, has an AI that screens retinal scans for conditions such as glaucoma, diabetic retinopathy and age-related macular degeneration. The firm is also working on mammography.
在其他的医疗领域，人们也在努力运用AI改善诊断效果。在眼疾诊断方面，谷歌母公司alphabet在伦敦的子公司DeepMind就拥有一个AI系统，可以进行视网膜扫描，检测青光眼、糖尿病视网膜病变和老年性黄斑变性等疾病。该公司也在研发乳房X光检查系统。
Heart disease is yet another field of interest. Researchers at Oxford University have been developing AIs intended to interpret echocardiograms, which are ultrasonic scans of the heart. Cardiologists looking at these scans are searching for signs of heart disease, but can miss them 20% of the time. That means patients will be sent home and may then go on to have a heart attack. The AI, however, can detect changes invisible to the eye and improve the accuracy of diagnosis. Ultromics, a firm in Oxford, is trying to commercialise the technology and it could be rolled out later this year in Britain.
心脏病是另一个极受关注的领域。牛津大学的研究人员一直在开发AIs（系统）用于解释超声波心动图，该图是对心脏进行超声波扫描后形成的。心脏病专家仔细查看这些心动图来发现心脏病的迹象，但这会出现20%的误差。这种误差意味着病人会被送回家，然后可能会发生心脏病。然而，AI可以检测到人眼看不见的变化，提高诊断的准确性。牛津的Ultromics公司正试图将这项技术商业化，可能会在今年年末在英国市场推出。
There are also efforts to detect cardiac arrhythmias, particularly atrial fibrillation, which increase the risk of heart failure and strokes. Researchers at Stanford University, led by Andrew Ng, have shown that AI software can identify arrhythmias from an electrocardiogram (ECG) better than an expert. The group has joined forces with a firm that makes portable ECG devices and is helping Apple with a study looking at whether arrhythmias can be detected in the heart-rate data picked up by its smart watches. Meanwhile, in Paris, a firm called Cardiologs is also trying to design an AI intended to read ECGs.
研究人员在检测心率失常，尤其针对心房颤动方面，也投入了不少精力，因为这类现象往往会增加心脏病及中风的风险。斯坦福大学吴恩达（Andrew Ng）教授带领的科研团队研究表明，AI软件完全可以通过心电图(ECG)来诊断心律失常，其诊断效果甚至比专家还可靠。在与便携式心电图仪器生产厂商合作下，该科研团队正协助苹果公司探究能否通过其智能手表采集到的心律数据来检测心律失常。与此同时，巴黎的Cardiologs公司也正在积极研发用来分析心电图的AI技术。
Seeing ahead
预见未来
Eric Topol, a cardiologist and digital-medicine researcher at the Scripps Research Institute, in San Diego, says that doctors and algorithms are comparable in accuracy in some areas, but computers have the advantage of speed. This combination of traits, he reckons, will lead to higher accuracy and productivity in health care.
来自美国圣地亚哥斯克里普斯（Scripps）研究所的数字医疗研究者兼心脏病专家埃里克·托普（Eric Topol）表示，虽然医生在某些领域的诊断精准度与算法软件不相上下，但是在诊断速度方面远远比不上电脑程序。他认为，同时具备以上两种优点的数字信息技术将有力提升未来医疗诊断的精度与效率。
Artificial intelligence might also make medicine more specific, by being able to draw distinctions that elude human observers. It may be able to grade cancers or instances of cardiac disease according to their risks—thus, for example, distinguishing those prostate cancers that will kill quickly, and therefore need treatment, from those that will not, and can probably be left untreated.
由于AI可以检测出人类肉眼所观察不到的差异，所以它有机会实现个性化医疗。AI可根据风险值将癌症或心脏病划分等级，因此，例如它可将一些快速致死、需尽快治疗的前列腺癌与那些相对安全、可以暂缓治疗的病例区分开来。
What medical AI will not do—at least not for a long time—is make human experts redundant in the fields it invades. Machine-learning systems work on a narrow range of tasks and will need close supervision for years to come. They are “black boxes”, in that doctors do not know exactly how they reach their decisions. And they are inclined to become biased if insufficient care is paid to what they are learning from. They will, though, take much of the drudgery and error out of diagnosis. And they will also help make sure that patients, whether being screened for cancer or taken from the scene of a car accident, are treated in time to be saved.
医学AI做不到的事情是：至少在短时间做不到在它们所入侵的领域内使人类专家失业。机器学习系统的任务范围有限，在未来几年还需要有人密切监督。它们还属于“黑匣子”，医生不能确切的知道它们是怎么下（诊断）决定的。如果对它们所学的东西缺乏足够的关注，它们的诊断很容易出现偏差。然而，它们可以消除诊断过程中单调沉闷的部分，减少犯错。它们也能确保不论是确诊为癌症，还是遭遇车祸的病人都能及时得到治疗从而得救。
翻译组：
Oria，女，工科博士，经济学人粉丝
Minjia，女，广告策划，经济学人读者
Muyi，文产小研，经济学人初段读者
Xiaofeng, 女，好奇心重的医疗民工，经济学人粉丝
校核组：
Li Xia, 女， HR，经济学人发烧友
Damon，男，建筑工人，经济学人铁粉
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观点|评论|思考
本次观点由Xingyi独家奉献
Xingyi，男，小硕，经济学人爱好者
毫无疑问，人工智能在医疗领域的潜力是巨大的，不可估量的。但人工智能也在责任的界定上也给医疗系统带来了新的问题和挑战。比如人工智能诊断仪器在法律上的主体是医生还仅是器械？
经济学人原文已经给出了很多例子，人工智能能够通过一些算法识别病变组织，即使是精准率已经降低到了极低值，那么那仅有的那次失手算谁的责任呢。仪器制造商可不会乐意提供风险如此高的售后服务。目前监管部门禁止虚拟助手软件提供任何疾病的诊断建议，只允许提供用户健康轻问诊咨询服务。我国监管部门对于利用人工智能技术提供诊断功能是审核要求非常严格。在 2017 年CFDA 发布的新版《医疗器械分类目录》中的分类规定，若诊断软件通过算法提供诊断建议，仅有辅助诊断功能不直接给出诊断结论，则按照二类医疗器械申报认证；如果对病变部位进行自动识别并提供明确诊断提示，则必须按照第三类医疗器械进行临床试验认证管理。
除了程序设计导致的事故外，相信很多人还会担心特殊指令的问题。人工智能毕竟是人写的，可以修改的程序。那么万一有某人物为了自己的私利偷偷设置了一个特殊指令使得人工智能进行了误判或者不判导致了病人的死亡。那么这样的风险该怎么防范？尤其是在这个网络时代，斯诺登事件的出现让公众对于任何一家机构的公信力都持有一定质疑，更别提那些看不见的黑客的手。那么病人在看诊的时候是否需要自己指定自己信任的医疗器械制造商？
人工智能的出现，在大方向上是有着积极的影响。但仍旧存在很多问题。
参考资料：2018年医疗人工智能技术与应用白皮书，互联网医疗健康产业联盟
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愿景
小组