hce_nchu
115年
英文
第 40 題
📖 題組：
The integration of Artificial Intelligence (AI) into healthcare is no longer a futuristic concept but a rapidly unfolding reality. One of the most __36__ applications is in the field of diagnostic imaging. By training on millions of clinical images, AI algorithms can now identify patterns that are virtually __37__ to the human eye, leading to much earlier detection of life-threatening conditions. Beyond diagnostics, AI is significantly __38__ the pharmaceutical industry. Historically, drug discovery has been an __39__ endeavor, often taking over a decade and billions of dollars to bring a single medication to market. AI models can now predict how specific molecular structures will interact with biological targets, thereby __40__ the initial stages of research. This efficiency was particularly __41__ during the recent global efforts to develop vaccines at record speed. However, this technological shift is not without its __42__. As healthcare providers become increasingly dependent on automated systems, questions regarding data privacy and "algorithmic bias" have surfaced. If the data used to train AI is not diverse, the resulting tools may provide __43__ outcomes for certain demographic groups. Therefore, it is __44__ that developers and medical professionals work together to ensure these systems are both transparent and __45__. The goal remains a synergy between human empathy and machine precision.
The integration of Artificial Intelligence (AI) into healthcare is no longer a futuristic concept but a rapidly unfolding reality. One of the most __36__ applications is in the field of diagnostic imaging. By training on millions of clinical images, AI algorithms can now identify patterns that are virtually __37__ to the human eye, leading to much earlier detection of life-threatening conditions. Beyond diagnostics, AI is significantly __38__ the pharmaceutical industry. Historically, drug discovery has been an __39__ endeavor, often taking over a decade and billions of dollars to bring a single medication to market. AI models can now predict how specific molecular structures will interact with biological targets, thereby __40__ the initial stages of research. This efficiency was particularly __41__ during the recent global efforts to develop vaccines at record speed. However, this technological shift is not without its __42__. As healthcare providers become increasingly dependent on automated systems, questions regarding data privacy and "algorithmic bias" have surfaced. If the data used to train AI is not diverse, the resulting tools may provide __43__ outcomes for certain demographic groups. Therefore, it is __44__ that developers and medical professionals work together to ensure these systems are both transparent and __45__. The goal remains a synergy between human empathy and machine precision.
- A obstructing
- B streamlining
- C complicating
- D exhausting
思路引導 VIP
請觀察前一兩句對傳統藥物研發過程的描述（如「超過十年」、「數十億美元」），再對照後文提到的「效率」(efficiency)。當一個新技術能讓原本「繁雜且漫長」的過程變得更有成效時，我們會用什麼樣性質的動詞來描述這種「精簡流程」的動作呢？
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AI 詳解
AI 專屬家教
太棒了！你能精確掌握上下文的因果邏輯，選出 streamlining（簡化、使效率化）反映了你對文章結構有著敏銳的觀察力。這題的正確性建立在強大的語境支撐上：前文提到傳統藥物研發是一個極其漫長且昂貴的過程，而 AI 能夠預測分子互動，這顯然是為了優化這個流程。下一句提到的 efficiency（效率）更是最直接的線索，指引我們選擇一個代表「使過程更順暢」的動詞。
語境線索與對比判讀
從題目難度來看，這屬於中等難度的詞彙應用題，具備良好的鑑別度。它不只考驗單字量，更測試學生是否能從「傳統開發的艱辛」與「AI 帶來的效率」之間建立對比。在四個選項中，只有 (B) 選項能與後文的「效率提升」達成語意邏輯的一致性，而 (A) 阻礙或 (C) 使複雜化則與全文讚揚技術進步的基調背道而馳。你能夠避開干擾項並鎖定核心語意，表現得非常專業！