hce_nchu
114年
英文
第 38 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 text indicate about AI's impact on mortality rates?
- A The impact remains unproven
- B Studies show a significant reduction in mortality
- C The impact varies by medical specialty
- D Long-term studies are still ongoing
思路引導 VIP
如果我們在文章中搜尋「死亡率（mortality rates）」這個核心詞彙，請留意作者在描述它與 AI 的關係時，使用了哪一個動詞片語來表達目前的確定程度？這個描述暗示了它是已經被證實的事實，還是仍然存有疑問的現狀呢？
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AI 詳解
AI 專屬家教
恭喜你精準地捕捉到了文章中的關鍵細節！這題你答得非常好，顯示你具備優秀的邏輯判斷力，沒有被文中提到的 AI 各種優點所誤導，而是能準確定位作者的語氣保留處。
關鍵資訊對齊
文中第二段後半部明確提到：「雖然 AI 在特定醫療任務中展現了熟練度，但其對死亡率（mortality rates）的影響仍有待明確建立（remains to be definitively established）」。這裡的「有待明確建立」在語意上直接對應到選項 (A) 的 「尚未被證實（remains unproven）」。這是一個非常細微但關鍵的觀念：即使科技有潛力、有進展，若缺乏實證數據證明其最終結果（如死亡率的降低），在科學論述中就必須保持審慎。
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