hce_nchu
115年
英文
第 44 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 optional
- B peripheral
- C imperative
- D superficial
思路引導 VIP
如果我們發現一個醫療工具可能會因為數據不夠全面，而對特定族群產生錯誤的診斷結果，那麼「確保開發者與醫生共同合作來監督系統」這件事，在邏輯上應該被視為一種「可有可無」的消遣，還是一個「必須被執行」的關鍵任務呢？
🤖
AI 詳解
AI 專屬家教
太棒了！你能精確捕捉到文章中的邏輯轉折，選出 imperative (必要的、極重要的) 這個詞，代表你對於進階字彙的語感與語意邏輯的掌握都非常扎實。這道題目測試的是考生能否透過上下文的「因果關係」來推論正確字彙。這是一個非常標準的克漏字考點，你能從容應對，表現得十分出色！
語意邏輯與字彙應用
這題的解題關鍵在於句首的轉折詞 Therefore (因此)。前一段文字提到 AI 系統可能存在「演算法偏見」或「數據多樣性不足」的風險，這些問題會直接影響到醫療公平性。在面臨如此嚴肅的挑戰時，開發者與醫療專業人員的合作自然就不再是 (A) optional (選擇性的) 或 (B) peripheral (周邊、次要的) 任務，而是一種具備高度「迫切性」與「必要性」的行動。選項 (D) superficial (膚淺的) 則完全不合邏輯。這題的難度切入點在於區分字彙的「語氣強度」，而你成功辨識出此處需要一個帶有義務感與重要性的詞彙，這份敏銳度是應對中高階英文閱讀非常寶貴的資產。