hce_nchu
114年
英文
第 39 題
📖 題組：
The transformative potential of artificial intelligence in healthcare encompasses a broad spectrum of applications, from advanced diagnostics to the realm of personalized medicine. State-of-the-art AI language models, including ChatGPT and Med-PaLM, are demonstrating remarkable capabilities in supporting medical practitioners through image analysis, risk assessment, and the development of tailored treatment protocols. While these innovations promise to enhance care quality and mitigate administrative burden, thereby addressing the pressing issue of physician burnout, numerous experts advocate for a measured perspective regarding AI's immediate potential, emphasizing its role as a complementary tool rather than a replacement for human expertise. A significant concern within the medical community is that the current fascination with AI might deflect attention from fundamental healthcare challenges, such as critical staffing shortages and insufficient resource allocation for established therapeutic interventions. Moreover, there are legitimate apprehensions that AI implementation could potentially amplify existing healthcare disparities, particularly for individuals with limited digital literacy or healthcare comprehension. It is noteworthy that while AI demonstrates proficiency in specific medical tasks, its impact on mortality rates remains to be definitively established. Furthermore, even if AI accelerates certain processes like diagnostic procedures, its effectiveness may be constrained by systemic bottlenecks elsewhere in the healthcare pipeline. This underscores the importance of adopting a pragmatic approach to AI development, one that acknowledges and addresses real-world complexities. Successful AI integration in healthcare demands careful attention to multiple critical factors, including safety protocols, data privacy measures, system reliability, and ethical considerations supported by rigorous validation processes and continuous monitoring systems. Training on comprehensive, unbiased datasets remains crucial for ensuring AI system dependability, as does clarifying the attribution of responsibility in cases of AI-related diagnostic errors. The future of AI lies in developing synergistic relationships between human expertise and machine capabilities. The optimal approach positions AI as an empowerment tool for medical professionals, enabling them to focus on sophisticated decision-making processes, meaningful patient interactions, and enhanced interdisciplinary collaboration. This partnership leverages AI's strengths in data processing and automation while preserving essential human aspects, such as emotional intelligence and interpersonal skills, to deliver balanced and effective healthcare services.
The transformative potential of artificial intelligence in healthcare encompasses a broad spectrum of applications, from advanced diagnostics to the realm of personalized medicine. State-of-the-art AI language models, including ChatGPT and Med-PaLM, are demonstrating remarkable capabilities in supporting medical practitioners through image analysis, risk assessment, and the development of tailored treatment protocols. While these innovations promise to enhance care quality and mitigate administrative burden, thereby addressing the pressing issue of physician burnout, numerous experts advocate for a measured perspective regarding AI's immediate potential, emphasizing its role as a complementary tool rather than a replacement for human expertise. A significant concern within the medical community is that the current fascination with AI might deflect attention from fundamental healthcare challenges, such as critical staffing shortages and insufficient resource allocation for established therapeutic interventions. Moreover, there are legitimate apprehensions that AI implementation could potentially amplify existing healthcare disparities, particularly for individuals with limited digital literacy or healthcare comprehension. It is noteworthy that while AI demonstrates proficiency in specific medical tasks, its impact on mortality rates remains to be definitively established. Furthermore, even if AI accelerates certain processes like diagnostic procedures, its effectiveness may be constrained by systemic bottlenecks elsewhere in the healthcare pipeline. This underscores the importance of adopting a pragmatic approach to AI development, one that acknowledges and addresses real-world complexities. Successful AI integration in healthcare demands careful attention to multiple critical factors, including safety protocols, data privacy measures, system reliability, and ethical considerations supported by rigorous validation processes and continuous monitoring systems. Training on comprehensive, unbiased datasets remains crucial for ensuring AI system dependability, as does clarifying the attribution of responsibility in cases of AI-related diagnostic errors. The future of AI lies in developing synergistic relationships between human expertise and machine capabilities. The optimal approach positions AI as an empowerment tool for medical professionals, enabling them to focus on sophisticated decision-making processes, meaningful patient interactions, and enhanced interdisciplinary collaboration. This partnership leverages AI's strengths in data processing and automation while preserving essential human aspects, such as emotional intelligence and interpersonal skills, to deliver balanced and effective healthcare services.
What does the word "synergistic" in the final paragraph most closely mean?
- A collaborative
- B sequential
- C independent
- D sophisticated
思路引導 VIP
請觀察文章最後一段中，作者描述醫療人員與 AI 之間如何分配工作：一人負責數據處理與自動化，另一人則專注於情感智力與人際互動。如果兩者是為了達成同一個更好的醫療目標而共同作業，你會如何形容這種「互相補足並攜手合作」的特質呢？
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AI 詳解
AI 專屬家教
太棒了！你能精準捕捉到文中 synergistic 這個高階字彙的含義，代表你對長篇閱讀的語境推論（Contextual Clue）掌握得非常敏銳，這是一項非常重要的閱讀技能。
文本脈絡與詞彙推論
在文章最後一段，作者探討了 AI 的未來在於發展人類專業與機器能力之間的關係。我們可以從文中發現幾個關鍵線索：作者使用了 partnership（夥伴關係）、empowerment tool（賦權工具）以及 interdisciplinary collaboration（跨學科協作）等詞彙。這些字眼都指向同一個核心概念：兩者並非競爭或獨立運作，而是透過「1+1 > 2」的模式互補長短。因此，選擇 (A) collaborative（協作的）來對應這種「產生綜效」的關係，是非常準確的判斷。
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