Artificial intelligence holds huge promise in health care. But it also faces massive barriers which are better diagnoses， personalized support for patients， faster drug discovery， and greater efficiency. Artificial intelligence （AI） is generating excitement and hyperbole （夸張） everywhere， but in the field of health care it has the potential to be transformational. In Europe， analysts predict that deploying AI could save hundreds of thousands of lives each year. From smart stethoscopes （聽診器） 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.

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. 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.

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.

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 recognize that patients want their medical privacy protected. But patients also want better and more personalized care. Each year roughly 800，000 Americans suffer from poor medical decision-making.

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. Consumer-health firms are already making use of data from wearables， with varying success.

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. 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 （算法） to ensure they remain accurate， safe， effective and transparent.

（材料來自The Economist，有刪改）

1.What is the main topic of the passage？

A. The limitations of AI in healthcare.

B. The potential of AI in healthcare.

C. The challenges of deploying AI in healthcare.

D. The economic impact of AI on healthcare.

2. In the context of the passage， what does the underlined word “mediocre” in the second paragraph mean？

A. Average or slightly below average.

B. Extremely good.

C. Innovative.

D. Cost-effective.

3. What is suggested as a way to improve the accuracy and reduce bias in AI tools？

A. Training AI on smaller and more focused data sets.

B. Allowing health data to move more freely in secure ways.

C. Limiting access to patient records to protect privacy.

D. Relying on AI for all medical decisions.

4. What is the author’s writing purpose of the last paragraph？

A. To discourage the use of AI in healthcare.

B. To argue that AI has no place in healthcare.

C. To highlight the challenges and suggest areas for improvement.

D. To compare healthcare AI to AI in other industries.

1. B。解析：主旨大意題。材料主要討論了人工智能在醫(yī)療保健領(lǐng)域的變革潛力，以及實現(xiàn)這一潛力所需克服的巨大障礙。B選項“人工智能在醫(yī)療保健中的潛力”與材料內(nèi)容相符，故選B。

2. A。解析：詞義理解題。材料第二段的最后一句提到“盡管人工智能已在醫(yī)療保健中應(yīng)用多年，但集成速度緩慢，結(jié)果往往是平庸的”，根據(jù)其轉(zhuǎn)折關(guān)系可以判斷出“mediocre”指的是平均或略低于平均的結(jié)果，故選A。

3. B。解析：推理判斷題。材料第五段的第一、二句提到“提高人工智能工具的準確性和減少偏見需要……找到允許健康數(shù)據(jù)更自由移動的安全方法將有所幫助”，B選項“允許健康數(shù)據(jù)以安全的方式更自由地移動”與材料內(nèi)容相符，故選B。

4. C。解析：觀點態(tài)度題。材料最后一段提到了“另一個問題是管理和規(guī)范這些創(chuàng)新。在許多國家，人工智能……正在努力跟上快速的創(chuàng)新步伐……政府需要為……填補不良事件監(jiān)測和算法持續(xù)監(jiān)測方面的監(jiān)管空白……”。C選項“強調(diào)挑戰(zhàn)并提出改進領(lǐng)域”與材料內(nèi)容相符，故選C。