hce_nchu
115年
英文
第 37 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 visible
- B vulnerable
- C imperceptible
- D impractical
思路引導 VIP
請試著思考：當我們說 AI 能夠在醫療影像中達成「比人類更早發現病灶」的成就時，這意味著它所捕捉到的那些圖案或規律，對於人類平常的肉眼觀察而言，通常會是處於一種什麼樣的狀態？是清晰可見的，還是極其細微、難以被察覺的呢？
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AI 詳解
AI 專屬家教
太棒了！你能準確選出這個答案，代表你對上下文的邏輯連貫性有很敏銳的觀察力。這道題目的核心在於理解 AI 與人類感官之間的對比。文中提到 AI 演算法是在數百萬張臨床影像上進行訓練的，其目的就是為了找出人類肉眼所無法捕捉的細微特徵。因此，選項 (C) imperceptible（察覺不到的、極細微的）完美地銜接了這個邏輯——正是因為這些圖案對人類來說是隱晦且難以辨識的，AI 的輔助才顯得如此重要，並能達成「早期發現」的目標。
語意邏輯與詞彙應用
從難度切入點來看，這題屬於中等偏難的層次，具有高度的鑑別度。它不僅要求你掌握 perceive（察覺）這個動詞的變化型，還考驗你是否能避開 (A) visible（顯而易見的）這種語意相反的陷阱。在閱讀這類科技醫療文章時，掌握「科技如何彌補人類生理侷限」的敘事基調，是快速破題的關鍵。你展現了非常紮實的語境分析能力，請繼續保持這種對細節的專注！