背景介绍：
人类历史上医学模式的变化是随着科学技术的发展而改变的。人工智能的发展，将会对医学模式产生巨大影响。AI医生培训和完善的过程，将与其它的智能诊疗技术结合，逐渐形成智能医学，将来的医学模式将发生巨大的变革，进入到智能医学模式。充分发展的智能医生，能使基层、偏远地区的人民得到与发达城市一样的高水平的医疗诊疗服务，人们不再因为地域因素而得不到良好的医疗保障。
The AI doctor
will see you…eventually
终有一天，会由 AI 医生给您看病
Artificial intelligence holds huge promise in health care. But it also faces massive barriers
人工智能在医疗保健领域前景广阔，但也面临巨大障碍
Better diagnoses. Personalised support for patients. Faster drug discovery. Greater efficiency. Artificial intelligence (AI) is generating excitement and hyperbole everywhere, but in the field of health care it has the potential to be transformational.
更精准的诊断。个性化的患者支持。更快的药物发现。更高的效率。AI 在各个领域都在引发兴奋和炒作，但在医疗保健领域，它确实有可能带来变革性的影响。
In Europe analysts predict that deploying AI could save hundreds of thousands of lives each year; in America, they say, it could also save money, shaving $200bn-360bn from overall annual medical spending, now $4.5trn a year (or 17% of GDP).
在欧洲，分析师预测部署 AI 每年可以挽救数十万人的生命；在美国，他们认为 AI 还能节省资金，从目前每年4.5万亿美元(占 GDP 的17%)的总医疗支出中省下2000亿至3600亿美元。
From smart stethoscopes and robot surgeons to the analysis of large data sets or the ability to chat to a medical AI with a human face, opportunities abound.
从智能听诊器和机器人外科医生，到大数据集分析或者与有一副人类面孔的医疗 AI 交流，机遇无处不在。
There is already evidence that AI systems can enhance diagnostic accuracy and disease tracking, improve the prediction of patients’ outcomes and suggest better treatments. It can also boost efficiency in hospitals and surgeries by taking on tasks such as medical transcription and monitoring patients, and by streamlining administration.
已有证据表明，AI 系统可以提高诊断准确性、方便疾病跟踪，改进预后、并建议更好的疗法。它还可以承担转录医生口述内容和监测患者等任务，简化行政流程，从而提高医院和诊所的效率。
It may already be speeding the time it takes for new drugs to reach clinical trials. New tools, including generative AI, could supercharge these abilities. Yet as our Technology Quarterly this week shows, although AI has been used in health care for many years, integration has been slow and the results have often been mediocre.
它可能已经加快了新药物进入临床试验阶段的速度。包括生成式 AI 在内的新工具可以进一步增强这些能力。然而，尽管AI在医疗保健领域已经运用多年，但系统整合的进展缓慢，结果往往也平平无奇。
There are good and bad reasons for this. The good reasons are that health care demands high evidentiary barriers when introducing new tools, to protect patients’ safety. The bad reasons involve data, regulation and incentives. Overcoming them could hold lessons for AI in other fields.
这其中的原因有好有坏。好的原因是，为保障患者安全，在引入新的工具时，医疗行业对证据门槛的要求很高。不好的原因涉及数据、监管和激励机制。克服这些问题可能会为 AI 在其他领域的应用提供启示。
AI systems learn by processing huge volumes of data, something health-care providers have in abundance. But health data is highly fragmented; strict rules control its use. Governments recognise that patients want their medical privacy protected. But patients also want better and more personalised care. Each year roughly 800,000 Americans suffer from poor medical decision-making.
AI 系统通过处理大量数据来学习，而医疗机构最不缺的就是数据。但这些数据高度分散，且对它们的使用有严格规定。政府认识到患者想要保护自己的医疗隐私。但他们也希望能获得更好和更个性化的护理。每年大约有80万美国人因医疗决策不当而遭受痛苦。
Improving accuracy and reducing bias in AI tools requires them to be trained on large data sets that reflect patients’ full diversity. Finding secure ways to allow health data to move more freely would help. But it could benefit patients, too: they should be given the right to access their own records in a portable, digital format.
要提高 AI 工具的准确性和降低其偏误，需要用能全面体现患者多样性的大型数据集来训练它们。找到安全的途径来让医疗数据更自由地流动会有帮助。而这也可能让患者受益。他们应该有权访问自己的便携式数字病历。
Consumer-health firms are already making use of data from wearables, with varying success. Portable patients’ records would let people make fuller use of their data and take more responsibility for their health.
消费者健康公司已经开始利用可穿戴设备的数据，取得了不同程度的成功。便携式病历将能让人们更充分地利用自己的数据，并对自己的健康承担起更多的责任。
Another problem is managing and regulating these innovations. In many countries the governance of AI in health, as in other areas, is struggling to keep up with the rapid pace of innovation. Regulatory authorities may be slow to approve new AI tools or may lack capacity and expertise.
另一个问题是对这些创新的管理和监管。在许多国家，对医疗部门 AI 的治理和在其他领域一样难以跟上快节奏的创新。监管机构审批新的 AI 工具可能需要很长时间，或者可能缺乏相关能力和专业知识。
Governments need to equip regulators to assess new AI tools. They also need to fill regulatory gaps in the surveillance of adverse events, and in the continuous monitoring of algorithms to ensure they remain accurate, safe, effective and transparent.
政府需要帮助监管机构提升评估新 AI 工具的能力，还需要填补监管空白，包括监测不良事件，以及持续监测算法以确保它们保持准确、安全、有效和透明。
That will be hard. One solution would be for countries to work together, to learn from each other and create minimum global standards. A less complex international regulatory system would also help create a market in which small companies can innovate.
这并不容易做到。一个解决方案是各国携手合作，相互学习借鉴，并制定全球最低标准。一个不那么复杂的国际监管体系也将有助于创建一个让小公司也能创新的市场。
Poorer countries, with less developed health infrastructure, have much to gain from introducing new tools, such as an AI-powered portable ultrasound device for obstetrics.
卫生基础设施欠发达的较贫穷国家将能从引入新工具（例如用于产科的便携式 AI 超声设备）中显著受益。
Because the alternative to an AI tool is often no treatment at all, they may even be able to leapfrog the entrenched health systems of rich countries—though a lack of data, connectivity and computing power will get in the way.
由于没有 AI 工具往往就意味着不做任何治疗，较贫穷国家甚至可能不必再建立富裕国家那种难以改变的卫生系统。不过，数据、连接性和计算能力的缺乏会成为障碍。
