Mở bài
Chủ đề về trí tuệ nhân tạo (AI) và công nghệ y tế là một trong những chủ đề nóng bỏng và xuất hiện với tần suất ngày càng cao trong các đề thi IELTS Reading thực tế. Với sự phát triển vượt bậc của công nghệ trong thập kỷ qua, Cambridge IELTS và các tổ chức ra đề như British Council, IDP thường xuyên đưa các bài đọc liên quan đến sự giao thoa giữa công nghệ và y học vào kỳ thi.
Bài viết này cung cấp một đề thi IELTS Reading hoàn chỉnh với 3 passages từ dễ đến khó, bám sát format thi thật 100%. Bạn sẽ được luyện tập với 40 câu hỏi đa dạng dạng, từ Multiple Choice, True/False/Not Given, Matching đến Summary Completion. Mỗi câu hỏi đều có đáp án chi tiết kèm giải thích cụ thể về vị trí thông tin trong bài và cách paraphrase.
Đề thi này phù hợp cho học viên từ band 5.0 trở lên, giúp bạn làm quen với độ khó tăng dần, học từ vựng chuyên ngành y tế-công nghệ, và rèn luyện kỹ năng quản lý thời gian hiệu quả. Đây là tài liệu lý tưởng để tự đánh giá năng lực và chuẩn bị tốt nhất cho kỳ thi IELTS Reading sắp tới.
1. Hướng Dẫn Làm Bài IELTS Reading
Tổng Quan Về IELTS Reading Test
IELTS Reading Test là phần thi kéo dài 60 phút với 3 passages và tổng cộng 40 câu hỏi. Đây là bài thi kiểm tra khả năng đọc hiểu, phân tích thông tin và quản lý thời gian của thí sinh. Mỗi câu trả lời đúng được tính 1 điểm, không bị trừ điểm khi sai.
Phân bổ thời gian khuyến nghị:
- Passage 1 (Easy): 15-17 phút (13 câu hỏi)
- Passage 2 (Medium): 18-20 phút (13 câu hỏi)
- Passage 3 (Hard): 23-25 phút (14 câu hỏi)
Lưu ý quan trọng: Độ khó tăng dần từ Passage 1 đến Passage 3. Đừng dành quá nhiều thời gian cho phần đầu và bị thiếu thời gian cho phần cuối. Luôn dành 2-3 phút cuối để chuyển đáp án vào answer sheet.
Các Dạng Câu Hỏi Trong Đề Này
Đề thi mẫu này bao gồm 7 dạng câu hỏi phổ biến nhất trong IELTS Reading:
- Multiple Choice Questions – Câu hỏi trắc nghiệm nhiều lựa chọn
- True/False/Not Given – Xác định thông tin đúng/sai/không có trong bài
- Matching Information – Nối thông tin với đoạn văn
- Sentence Completion – Hoàn thành câu
- Matching Headings – Nối tiêu đề với đoạn văn
- Summary Completion – Hoàn thành đoạn tóm tắt
- Short-answer Questions – Câu hỏi trả lời ngắn
2. IELTS Reading Practice Test
PASSAGE 1 – The Digital Revolution in Healthcare
Độ khó: Easy (Band 5.0-6.5)
Thời gian đề xuất: 15-17 phút
The introduction of artificial intelligence (AI) into healthcare has marked one of the most significant transformations in modern medicine. Over the past decade, hospitals and clinics worldwide have gradually adopted various AI-powered tools to improve patient care, reduce costs, and enhance diagnostic accuracy. This technological revolution is reshaping how doctors diagnose diseases, how patients receive treatment, and how healthcare systems operate on a daily basis.
AI diagnostic systems represent one of the most visible applications of this technology. These computer programs can analyze medical images such as X-rays, CT scans, and MRIs with remarkable precision. In many cases, AI algorithms can detect abnormalities that human eyes might miss, particularly in the early stages of diseases like cancer. For example, a study conducted at Stanford University showed that an AI system could identify skin cancer with an accuracy level comparable to experienced dermatologists. The system was trained using thousands of images of various skin conditions, enabling it to recognize patterns and features associated with different types of cancer.
Another area where AI is making substantial progress is in predicting patient outcomes. Machine learning algorithms can analyze vast amounts of patient data, including medical history, test results, and lifestyle factors, to forecast the likelihood of certain health events. This predictive capability allows doctors to intervene earlier and implement preventive measures before conditions worsen. For instance, AI systems in intensive care units can predict which patients are at highest risk of deteriorating health, enabling medical staff to allocate resources more efficiently and provide timely interventions.
Drug discovery and development have also benefited enormously from AI technology. Traditionally, developing a new medication could take over a decade and cost billions of dollars. AI is accelerating this process by rapidly analyzing molecular structures and predicting which compounds are most likely to be effective against specific diseases. Pharmaceutical companies are now using AI to screen millions of potential drug candidates in a fraction of the time it would take using conventional methods. This breakthrough has particular significance for rare diseases, where limited patient populations make traditional drug development economically challenging.
The impact of AI extends to administrative tasks within healthcare facilities. Hospitals generate enormous amounts of paperwork, from patient records to insurance claims. AI-powered systems can automate many of these processes, reducing the administrative burden on healthcare workers and minimizing errors. Voice recognition software allows doctors to dictate notes directly into electronic health records, saving valuable time that can be spent with patients. Chatbots and virtual assistants can handle routine patient inquiries, schedule appointments, and provide basic health information, freeing up human staff to focus on more complex tasks.
However, the integration of AI into healthcare is not without challenges. One major concern is data privacy and security. Medical records contain highly sensitive personal information, and any breach could have serious consequences. Healthcare organizations must implement robust security measures to protect patient data from cyber threats. Additionally, there are questions about liability and accountability when AI systems make mistakes. If an AI diagnostic tool misses a cancer diagnosis or recommends an incorrect treatment, determining who is responsible becomes complicated.
Despite these challenges, the future of AI in healthcare looks promising. Experts predict that AI will become an indispensable tool for healthcare professionals rather than a replacement for human expertise. The combination of human judgment and AI capabilities can deliver better outcomes than either could achieve alone. As technology continues to advance and more data becomes available, AI systems will become increasingly sophisticated and reliable, ushering in a new era of personalized, efficient, and accessible healthcare for people around the world.
Hệ thống trí tuệ nhân tạo phân tích hình ảnh y khoa tại bệnh viện hiện đại
Questions 1-13
Questions 1-5: Multiple Choice
Choose the correct letter, A, B, C or D.
1. According to the passage, AI systems in healthcare are primarily being used to:
A. Replace human doctors completely
B. Improve diagnosis, treatment and operations
C. Reduce the number of hospital staff needed
D. Make healthcare more expensive
2. The Stanford University study mentioned in the passage demonstrated that:
A. AI cannot diagnose skin conditions accurately
B. AI is better than all dermatologists
C. AI can match experienced dermatologists in accuracy
D. AI needs more training to be useful
3. Which of the following is NOT mentioned as a benefit of AI in healthcare?
A. Faster drug development
B. Better prediction of patient outcomes
C. Reduced need for medical training
D. Automation of administrative tasks
4. The passage suggests that AI in intensive care units helps by:
A. Replacing nurses and doctors
B. Identifying high-risk patients early
C. Performing surgical procedures
D. Communicating with patients’ families
5. According to the passage, traditional drug development is:
A. Faster than AI-assisted methods
B. Less expensive than using AI
C. Time-consuming and costly
D. More effective for rare diseases
Questions 6-9: True/False/Not Given
Do the following statements agree with the information given in the passage?
Write:
- TRUE if the statement agrees with the information
- FALSE if the statement contradicts the information
- NOT GIVEN if there is no information on this
6. AI diagnostic systems can always detect diseases better than human doctors.
7. Voice recognition software helps doctors save time on paperwork.
8. Most patients prefer receiving health advice from chatbots rather than human staff.
9. Data security is a significant concern when implementing AI in healthcare.
Questions 10-13: Sentence Completion
Complete the sentences below.
Choose NO MORE THAN THREE WORDS from the passage for each answer.
10. AI algorithms can identify __ in medical images that might be overlooked by humans.
11. Machine learning can analyze patient information to predict __ of certain health problems.
12. Pharmaceutical companies use AI to examine millions of __ much faster than before.
13. Experts believe AI will become a crucial tool rather than a __ for medical professionals.
PASSAGE 2 – AI-Powered Precision Medicine: A Paradigm Shift
Độ khó: Medium (Band 6.0-7.5)
Thời gian đề xuất: 18-20 phút
The healthcare industry is undergoing a fundamental transformation driven by the convergence of artificial intelligence and genomic science, giving rise to what experts call precision medicine. Unlike traditional medicine, which often employs a one-size-fits-all approach to treatment, precision medicine recognizes that each patient is unique, with distinct genetic profiles, environmental exposures, and lifestyle factors that influence their health and response to treatment. AI has emerged as the critical enabler of this personalized approach, capable of processing and interpreting the staggering volumes of biological data required to tailor medical interventions to individual patients.
A. The Genomic Revolution
The completion of the Human Genome Project in 2003 marked a watershed moment in medical science, providing the first complete map of human DNA. However, the real challenge lay not in sequencing the genome but in understanding what the billions of genetic variations mean for human health. This is where AI excels. Deep learning algorithms can identify subtle patterns and correlations within genomic data that would be impossible for human researchers to detect manually. These systems analyze genetic mutations associated with diseases, predict how individuals will respond to specific medications, and identify potential therapeutic targets for drug development.
B. Personalized Treatment Protocols
One of the most tangible applications of AI-driven precision medicine is in oncology. Cancer is not a single disease but rather hundreds of different conditions, each with unique molecular characteristics. AI systems can analyze a patient’s tumor genetic profile alongside vast databases of treatment outcomes to recommend the most effective therapy. For instance, IBM’s Watson for Oncology processes medical literature, clinical trial data, and patient records to suggest evidence-based treatment options tailored to each patient’s specific cancer subtype. Studies have shown that AI recommendations often uncover treatment possibilities that oncologists might not have considered, potentially improving patient survival rates.
C. Predictive Healthcare Analytics
Beyond treatment, AI is revolutionizing disease prevention through predictive analytics. By integrating data from electronic health records, genetic information, wearable devices, and even social determinants of health, AI models can calculate an individual’s risk for developing specific conditions years or even decades in advance. This prognostic capability enables proactive interventions rather than reactive treatments. For example, AI algorithms can identify individuals at high risk for type 2 diabetes based on their genetic predisposition, lifestyle patterns, and metabolic markers, allowing healthcare providers to implement preventive strategies such as personalized diet plans and exercise regimens before the disease manifests.
D. Challenges in Implementation
Despite its promise, implementing AI-powered precision medicine faces several formidable obstacles. The first is data standardization and interoperability. Healthcare data exists in numerous formats across different systems, making it difficult for AI algorithms to access and analyze information comprehensively. Additionally, training AI systems requires enormous datasets that are representative of diverse populations. Much of the existing medical data comes from studies conducted primarily on individuals of European descent, raising concerns about whether AI systems will perform equally well for people from different ethnic backgrounds. This algorithmic bias could potentially exacerbate existing health disparities if not addressed carefully.
E. Ethical and Regulatory Considerations
The rise of AI in precision medicine also raises profound ethical questions. Who owns the genetic and health data used to train AI systems? How do we ensure patient consent is truly informed when AI analysis might reveal unexpected health risks or predispositions? There’s also the question of algorithmic transparency. Many AI systems, particularly those using deep learning, operate as “black boxes,” making decisions through processes that even their creators don’t fully understand. This opacity creates challenges for regulatory bodies trying to ensure these systems are safe and effective, and for doctors who must explain AI recommendations to patients.
F. The Future Landscape
Looking ahead, experts anticipate that precision medicine will become increasingly mainstream as AI technology matures and data infrastructure improves. The integration of AI with emerging technologies such as CRISPR gene editing and nanotechnology could enable treatments once considered science fiction, such as bespoke therapies designed for individual patients’ unique genetic makeup. However, realizing this vision will require not only technological advances but also changes in how healthcare systems are organized and funded. The shift from reactive sick-care to proactive health maintenance represents a fundamental reimagining of medicine’s role in society, one that promises better health outcomes but demands careful navigation of complex technical, ethical, and social challenges.
Bác sĩ phân tích dữ liệu gen người bệnh với hỗ trợ trí tuệ nhân tạo để điều trị cá nhân hóa
Questions 14-26
Questions 14-18: Matching Headings
The passage has six paragraphs, A-F.
Choose the correct heading for paragraphs B-F from the list of headings below.
Write the correct number, i-ix.
List of Headings:
i. The problem of unequal data representation
ii. Future integration with advanced technologies
iii. Understanding the human genetic code
iv. Customized cancer treatment strategies
v. Transparency issues in AI decision-making
vi. Forecasting diseases before they occur
vii. Barriers to widespread adoption
viii. The cost of implementing AI systems
ix. Training requirements for medical staff
14. Paragraph B
15. Paragraph C
16. Paragraph D
17. Paragraph E
18. Paragraph F
Questions 19-23: Yes/No/Not Given
Do the following statements agree with the claims of the writer in the passage?
Write:
- YES if the statement agrees with the claims of the writer
- NO if the statement contradicts the claims of the writer
- NOT GIVEN if it is impossible to say what the writer thinks about this
19. Traditional medicine typically provides the same treatment to all patients with similar symptoms.
20. The Human Genome Project made it easy to understand all genetic variations immediately.
21. AI recommendations for cancer treatment are always more accurate than human oncologists’ decisions.
22. Most medical research data has been collected from diverse ethnic populations.
23. Regulatory bodies find it difficult to evaluate AI systems that lack transparency.
Questions 24-26: Summary Completion
Complete the summary below.
Choose NO MORE THAN TWO WORDS from the passage for each answer.
Precision medicine represents a significant departure from conventional approaches by acknowledging individual differences in genetics and lifestyle. AI serves as the (24) __ that makes this personalized approach feasible by analyzing massive amounts of biological information. In cancer care, AI examines a patient’s (25) __ to recommend optimal treatments. For disease prevention, AI uses (26) __ to assess future health risks, enabling doctors to intervene before illnesses develop.
PASSAGE 3 – The Socioeconomic Implications of AI in Global Healthcare Systems
Độ khó: Hard (Band 7.0-9.0)
Thời gian đề xuất: 23-25 phút
The proliferation of artificial intelligence technologies throughout global healthcare systems represents far more than a mere technological upgrade; it constitutes a paradigmatic transformation that carries profound implications for healthcare accessibility, economic structures, professional hierarchies, and societal equity. While proponents tout AI’s potential to democratize healthcare by making expert-level diagnostics available to underserved populations, critics caution that without careful governance and equitable distribution, these technologies may inadvertently entrench existing disparities and create new forms of stratification based on access to algorithmic medicine.
The economic ramifications of AI integration into healthcare are multifaceted and potentially contradictory. On one hand, AI promises substantial cost reductions through increased efficiency, reduced diagnostic errors, and optimized resource allocation. McKinsey Global Institute estimates that AI applications could generate up to $100 billion annually in savings for the United States healthcare system alone through operational efficiencies and improved clinical outcomes. Automated triage systems can streamline patient flow, reducing wait times and emergency room overcrowding. Predictive analytics can optimize hospital bed management and staff scheduling, minimizing wasteful expenditure while maintaining care quality. These efficiencies could theoretically translate into more affordable healthcare, particularly benefiting resource-constrained health systems in developing nations where every dollar saved can significantly amplify service delivery capacity.
However, the capital-intensive nature of AI implementation presents a formidable barrier to entry, potentially widening the healthcare gap between wealthy and poor nations, and between well-funded and under-resourced facilities within the same country. Developing and deploying sophisticated AI systems requires substantial investments in computational infrastructure, data storage, cybersecurity measures, and technical expertise—resources that are disproportionately concentrated in high-income countries and elite medical institutions. This technological divide threatens to create a two-tiered global healthcare system where patients in affluent regions benefit from cutting-edge AI diagnostics and personalized treatment protocols, while those in poorer areas continue to rely on outdated methodologies and overstretched human resources. The irony is palpable: technologies capable of ameliorating healthcare inequities may instead exacerbate them if deployment follows existing patterns of resource distribution.
The impact on healthcare professionals constitutes another dimension of this transformation worthy of rigorous examination. Contrary to dystopian narratives of wholesale job displacement, most analyses suggest AI will augment rather than replace human healthcare workers, though the nature of medical practice will undoubtedly evolve. Radiologists, for instance, may spend less time identifying obvious abnormalities—tasks AI performs admirably—and more time on complex case interpretation, patient communication, and treatment planning, activities requiring nuanced human judgment. This shift necessitates substantial reskilling initiatives to prepare current and future healthcare professionals for collaborative practice with AI systems. Medical education must evolve to incorporate computational literacy and critical evaluation of algorithmic recommendations, ensuring clinicians can effectively leverage AI tools while maintaining the discernment to recognize their limitations.
Yet this occupational transition poses particular challenges for healthcare workers in low-and-middle-income countries (LMICs), where medical education infrastructure may lack resources to implement necessary curricular reforms. Furthermore, if AI systems are primarily designed and trained using data from high-income contexts, their performance may deteriorate when applied to patient populations with different disease profiles, genetic backgrounds, or environmental exposures—a phenomenon known as model drift or distribution shift. This technological colonialism, wherein AI systems developed in wealthy nations are exported to poorer ones without adequate validation or customization, risks embedding biases and perpetuating clinical approaches ill-suited to local contexts.
The governance frameworks surrounding AI in healthcare remain nascent and fragmented, struggling to keep pace with rapid technological advancement. Regulatory harmonization across jurisdictions is complicated by divergent approaches to data privacy, medical device approval, and algorithmic accountability. The European Union’s Medical Device Regulation and the FDA’s evolving guidelines for AI/ML-based medical devices represent attempts to ensure safety and efficacy, but significant ambiguities persist regarding validation standards, post-market surveillance, and liability attribution when AI systems contribute to adverse outcomes. The adaptive nature of machine learning systems—their capacity to continuously update based on new data—challenges traditional regulatory models predicated on evaluating fixed devices or pharmaceuticals.
Perhaps most philosophically vexing are questions surrounding medical agency and epistemic authority in an era of algorithmic medicine. When AI recommendations conflict with clinical intuition, what epistemic weight should each carry? The risk of automation bias—whereby human decision-makers uncritically defer to algorithmic outputs—is well-documented across domains. Conversely, algorithm aversion—the tendency to lose confidence in algorithms after observing errors, despite their statistical superiority—may prevent optimal utilization of AI capabilities. Cultivating appropriate epistemic humility regarding both human and machine capabilities, while maintaining human accountability for medical decisions, represents a delicate calibration that healthcare systems worldwide are only beginning to attempt.
The trajectory of AI in healthcare ultimately hinges not merely on technological sophistication but on deliberate policy choices regarding accessibility, governance, and value alignment. Will AI be harnessed primarily to maximize profits for technology corporations and healthcare conglomerates, or will it be stewarded as a public good aimed at universal health coverage and equitable outcomes? The answer will determine whether AI fulfills its emancipatory potential or becomes another mechanism through which privilege begets privilege, leaving the world’s most vulnerable populations further behind in an increasingly digital healthcare landscape.
So sánh hệ thống y tế sử dụng AI giữa các quốc gia phát triển và đang phát triển
Questions 27-40
Questions 27-31: Multiple Choice
Choose the correct letter, A, B, C or D.
27. According to the passage, critics of AI in healthcare are primarily concerned that:
A. The technology is not yet sufficiently advanced
B. AI may worsen existing healthcare inequalities
C. AI will completely replace human doctors
D. The cost savings are exaggerated
28. The McKinsey Global Institute estimate mentioned in the passage refers to:
A. Global healthcare savings worldwide
B. Potential annual savings in US healthcare alone
C. Investment required for AI implementation
D. Current spending on AI healthcare technology
29. The author suggests that the “irony” of AI in healthcare is that:
A. It costs more than traditional methods
B. It may increase inequalities despite its potential to reduce them
C. Patients prefer human doctors despite AI being better
D. It works better in poor countries than rich ones
30. The passage indicates that radiologists working with AI will likely:
A. Lose their jobs to machines
B. Focus more on complex interpretation and patient interaction
C. Need to learn computer programming
D. Work shorter hours due to automation
31. “Model drift” or “distribution shift” refers to:
A. AI systems becoming obsolete over time
B. Changes in hospital organizational structures
C. AI performance declining when used on different populations
D. The movement of AI technology from rich to poor countries
Questions 32-36: Matching Features
Match each challenge (Questions 32-36) with the correct stakeholder group (A-F).
You may use any letter more than once.
Stakeholder Groups:
A. Patients in developing countries
B. Healthcare professionals
C. Regulatory authorities
D. Technology companies
E. Medical educators
F. Healthcare administrators
32. Difficulty in establishing consistent standards for AI validation across different regions
33. Need to reform curricula to include computational skills for future doctors
34. Risk of receiving AI systems that were not designed for their specific population
35. Challenge of deciding when to trust AI recommendations over personal judgment
36. Investment in expensive infrastructure required for AI implementation
Questions 37-40: Short-answer Questions
Answer the questions below.
Choose NO MORE THAN THREE WORDS from the passage for each answer.
37. What term describes the tendency of humans to blindly accept algorithmic recommendations?
38. According to the passage, what type of good should AI ideally be treated as, rather than a profit-making tool?
39. What quality must healthcare systems develop regarding both human and AI capabilities?
40. What will ultimately determine whether AI achieves its liberating potential in healthcare?
3. Answer Keys – Đáp Án
PASSAGE 1: Questions 1-13
- B
- C
- C
- B
- C
- FALSE
- TRUE
- NOT GIVEN
- TRUE
- abnormalities
- the likelihood
- potential drug candidates / drug candidates
- replacement
PASSAGE 2: Questions 14-26
- iii
- iv
- vii
- v
- ii
- YES
- NO
- NOT GIVEN
- NO
- YES
- critical enabler
- tumor genetic profile / genetic profile
- predictive analytics
PASSAGE 3: Questions 27-40
- B
- B
- B
- B
- C
- C
- E
- A
- B
- F
- automation bias
- public good / a public good
- epistemic humility
- policy choices / deliberate policy choices
4. Giải Thích Đáp Án Chi Tiết
Passage 1 – Giải Thích
Câu 1: B
- Dạng câu hỏi: Multiple Choice
- Từ khóa: AI systems, healthcare, primarily used
- Vị trí trong bài: Đoạn 1, dòng 2-4
- Giải thích: Bài viết nói rõ AI được sử dụng để “improve patient care, reduce costs, and enhance diagnostic accuracy” và “reshaping how doctors diagnose diseases, how patients receive treatment, and how healthcare systems operate”. Đây tương ứng với đáp án B về cải thiện chẩn đoán, điều trị và hoạt động. Đáp án A sai vì không có thông tin về thay thế hoàn toàn bác sĩ. Đáp án C sai vì mục tiêu không phải giảm nhân viên. Đáp án D sai vì mục tiêu là giảm chi phí (reduce costs).
Câu 2: C
- Dạng câu hỏi: Multiple Choice
- Từ khóa: Stanford University study, demonstrated
- Vị trí trong bài: Đoạn 2, dòng 6-8
- Giải thích: Nghiên cứu cho thấy “an AI system could identify skin cancer with an accuracy level comparable to experienced dermatologists”. “Comparable to” được paraphrase thành “match” trong đáp án C. Đáp án A và D mâu thuẫn với thông tin. Đáp án B quá tuyệt đối (better than ALL dermatologists).
Câu 6: FALSE
- Dạng câu hỏi: True/False/Not Given
- Từ khóa: always detect diseases better
- Vị trí trong bài: Đoạn 2, dòng 4-6
- Giải thích: Bài viết chỉ nói AI “can detect abnormalities that human eyes might miss” và có độ chính xác “comparable to” (tương đương) bác sĩ, chứ không phải “always better” (luôn tốt hơn). Từ khóa “always” làm cho câu này FALSE.
Câu 7: TRUE
- Dạng câu hỏi: True/False/Not Given
- Từ khóa: Voice recognition software, doctors, save time, paperwork
- Vị trí trong bài: Đoạn 5, dòng 4-6
- Giải thích: Bài viết nói rõ “Voice recognition software allows doctors to dictate notes directly into electronic health records, saving valuable time”. Đây hoàn toàn trùng khớp với câu hỏi.
Câu 10: abnormalities
- Dạng câu hỏi: Sentence Completion
- Từ khóa: identify, medical images, overlooked by humans
- Vị trí trong bài: Đoạn 2, dòng 4-5
- Giải thích: Câu trong bài: “AI algorithms can detect abnormalities that human eyes might miss”. “Detect” được paraphrase thành “identify”, “miss” được paraphrase thành “overlooked”.
Câu 13: replacement
- Dạng câu hỏi: Sentence Completion
- Từ khóa: indispensable tool, rather than
- Vị trí trong bài: Đoạn 7, dòng 2-3
- Giải thích: Bài viết nói “AI will become an indispensable tool for healthcare professionals rather than a replacement for human expertise”. Đây là paraphrase trực tiếp.
Passage 2 – Giải Thích
Câu 14: iii (Paragraph B)
- Dạng câu hỏi: Matching Headings
- Giải thích: Đoạn B tập trung vào việc hoàn thành Human Genome Project và việc hiểu các biến thể di truyền. Tiêu đề “Understanding the human genetic code” (iii) phù hợp nhất với nội dung chính của đoạn về “providing the first complete map of human DNA” và “understanding what the billions of genetic variations mean”.
Câu 15: iv (Paragraph C)
- Dạng câu hỏi: Matching Headings
- Giải thích: Đoạn C nói về “personalized treatment protocols” đặc biệt trong oncology (ung thư học), với việc phân tích “tumor genetic profile” để đề xuất liệu pháp hiệu quả. Tiêu đề “Customized cancer treatment strategies” (iv) tóm tắt chính xác nội dung này.
Câu 19: YES
- Dạng câu hỏi: Yes/No/Not Given
- Vị trí trong bài: Đoạn 1, dòng 3-5
- Giải thích: Tác giả khẳng định “traditional medicine, which often employs a one-size-fits-all approach to treatment”. Câu hỏi paraphrase “same treatment to all patients with similar symptoms” = “one-size-fits-all approach”.
Câu 20: NO
- Dạng câu hỏi: Yes/No/Not Given
- Vị trí trong bài: Paragraph B, dòng 1-3
- Giải thích: Bài viết nói “the real challenge lay not in sequencing the genome but in understanding what the billions of genetic variations mean”. Điều này mâu thuẫn với việc “made it easy to understand all genetic variations immediately”.
Câu 22: NO
- Dạng câu hỏi: Yes/No/Not Given
- Vị trí trong bài: Paragraph D, dòng 5-8
- Giải thích: Bài viết nói rõ “Much of the existing medical data comes from studies conducted primarily on individuals of European descent”, ngược lại với “diverse ethnic populations” trong câu hỏi.
Câu 24: critical enabler
- Dạng câu hỏi: Summary Completion
- Vị trí trong bài: Đoạn 1, dòng 7-8
- Giải thích: Câu trong bài: “AI has emerged as the critical enabler of this personalized approach”. Từ “makes this personalized approach feasible” trong câu hỏi paraphrase “enabler”.
Passage 3 – Giải Thích
Câu 27: B
- Dạng câu hỏi: Multiple Choice
- Từ khóa: critics, primarily concerned
- Vị trí trong bài: Đoạn 1, dòng 4-7
- Giải thích: Bài viết nói “critics caution that without careful governance and equitable distribution, these technologies may inadvertently entrench existing disparities and create new forms of stratification”. Điều này tương ứng với đáp án B về làm trầm trọng thêm bất bình đẳng.
Câu 28: B
- Dạng câu hỏi: Multiple Choice
- Từ khóa: McKinsey Global Institute estimate
- Vị trí trong bài: Đoạn 2, dòng 3-5
- Giải thích: Câu trong bài: “McKinsey Global Institute estimates that AI applications could generate up to $100 billion annually in savings for the United States healthcare system alone”. “Alone” nhấn mạnh chỉ riêng Mỹ, tương ứng đáp án B.
Câu 29: B
- Dạng câu hỏi: Multiple Choice
- Từ khóa: irony
- Vị trí trong bài: Đoạn 3, dòng cuối
- Giải thích: “The irony is palpable: technologies capable of ameliorating healthcare inequities may instead exacerbate them if deployment follows existing patterns of resource distribution”. Công nghệ có thể cải thiện bất bình đẳng nhưng lại có thể làm trầm trọng thêm – đây chính là irony được mô tả trong đáp án B.
Câu 31: C
- Dạng câu hỏi: Multiple Choice
- Từ khóa: model drift, distribution shift, refers to
- Vị trí trong bài: Đoạn 5, dòng 4-8
- Giải thích: Bài viết giải thích: “their performance may deteriorate when applied to patient populations with different disease profiles, genetic backgrounds, or environmental exposures—a phenomenon known as model drift or distribution shift”. Đây chính xác là đáp án C.
Câu 32: C (Regulatory authorities)
- Dạng câu hỏi: Matching Features
- Vị trí trong bài: Đoạn 6, dòng 1-3
- Giải thích: “Regulatory harmonization across jurisdictions is complicated by divergent approaches” – việc thiết lập tiêu chuẩn nhất quán là thách thức của regulatory authorities (cơ quan quản lý).
Câu 37: automation bias
- Dạng câu hỏi: Short-answer Questions
- Vị trí trong bài: Đoạn 7, dòng 3-4
- Giải thích: “The risk of automation bias—whereby human decision-makers uncritically defer to algorithmic outputs”. Đây là định nghĩa chính xác của thuật ngữ được hỏi.
Câu 40: policy choices
- Dạng câu hỏi: Short-answer Questions
- Vị trí trong bài: Đoạn 8, dòng 1-2
- Giải thích: “The trajectory of AI in healthcare ultimately hinges not merely on technological sophistication but on deliberate policy choices”. Câu này chỉ rõ policy choices sẽ quyết định (determine) tiềm năng của AI.
5. Từ Vựng Quan Trọng Theo Passage
Passage 1 – Essential Vocabulary
| Từ vựng | Loại từ | Phiên âm | Nghĩa tiếng Việt | Ví dụ từ bài | Collocation |
|---|---|---|---|---|---|
| artificial intelligence | n | /ˌɑːtɪˈfɪʃl ɪnˈtelɪdʒəns/ | trí tuệ nhân tạo | The introduction of artificial intelligence into healthcare has marked one of the most significant transformations | AI technology, AI system, AI application |
| gradually adopted | v phr | /ˈɡrædʒuəli əˈdɒptɪd/ | dần dần áp dụng | Hospitals have gradually adopted various AI-powered tools | gradually adopt new methods, slowly adopted |
| detect abnormalities | v phr | /dɪˈtekt ˌæbnɔːˈmæləti/ | phát hiện bất thường | AI algorithms can detect abnormalities that human eyes might miss | detect early signs, detect patterns |
| comparable to | adj phr | /ˈkɒmpərəbl tuː/ | có thể so sánh với, tương đương | AI system could identify skin cancer with an accuracy level comparable to experienced dermatologists | comparable in quality, roughly comparable |
| substantial progress | n phr | /səbˈstænʃl ˈprəʊɡres/ | tiến bộ đáng kể | AI is making substantial progress in predicting patient outcomes | make substantial progress, achieve substantial results |
| predictive capability | n phr | /prɪˈdɪktɪv ˌkeɪpəˈbɪləti/ | khả năng dự đoán | This predictive capability allows doctors to intervene earlier | enhanced predictive capability, predictive power |
| implement preventive measures | v phr | /ˈɪmplɪment prɪˈventɪv ˈmeʒəz/ | thực hiện các biện pháp phòng ngừa | Doctors can implement preventive measures before conditions worsen | implement safety measures, implement strategies |
| deteriorating health | n phr | /dɪˈtɪəriəreɪtɪŋ helθ/ | sức khỏe xấu đi | AI systems can predict which patients are at highest risk of deteriorating health | deteriorating condition, rapidly deteriorating |
| accelerating this process | v phr | /əkˈseləreɪtɪŋ ðɪs ˈprəʊses/ | đẩy nhanh quá trình này | AI is accelerating this process by rapidly analyzing molecular structures | accelerate development, accelerate growth |
| breakthrough | n | /ˈbreɪkθruː/ | đột phá | This breakthrough has particular significance for rare diseases | major breakthrough, scientific breakthrough |
| automate processes | v phr | /ˈɔːtəmeɪt ˈprəʊsesɪz/ | tự động hóa quy trình | AI-powered systems can automate many of these processes | automate tasks, fully automated |
| robust security measures | n phr | /rəʊˈbʌst sɪˈkjʊərəti ˈmeʒəz/ | các biện pháp bảo mật mạnh mẽ | Organizations must implement robust security measures to protect patient data | robust system, robust framework |
Passage 2 – Essential Vocabulary
| Từ vựng | Loại từ | Phiên âm | Nghĩa tiếng Việt | Ví dụ từ bài | Collocation |
|---|---|---|---|---|---|
| fundamental transformation | n phr | /ˌfʌndəˈmentl ˌtrænsfəˈmeɪʃn/ | sự chuyển đổi căn bản | Healthcare is undergoing a fundamental transformation driven by AI | undergo transformation, radical transformation |
| one-size-fits-all approach | n phr | /wʌn saɪz fɪts ɔːl əˈprəʊtʃ/ | cách tiếp cận một khuôn mẫu | Traditional medicine employs a one-size-fits-all approach to treatment | adopt an approach, standardized approach |
| critical enabler | n phr | /ˈkrɪtɪkl ɪˈneɪblə/ | yếu tố hỗ trợ then chốt | AI has emerged as the critical enabler of this personalized approach | key enabler, major enabler |
| staggering volumes | n phr | /ˈstæɡərɪŋ ˈvɒljuːmz/ | khối lượng khổng lồ | AI is capable of processing the staggering volumes of biological data | staggering amount, staggering numbers |
| watershed moment | n phr | /ˈwɔːtəʃed ˈməʊmənt/ | thời điểm bước ngoặt | The Human Genome Project marked a watershed moment in medical science | watershed event, historic watershed |
| subtle patterns | n phr | /ˈsʌtl ˈpætənz/ | các mẫu tinh vi | Deep learning algorithms can identify subtle patterns within genomic data | detect subtle patterns, subtle differences |
| tangible applications | n phr | /ˈtændʒəbl ˌæplɪˈkeɪʃnz/ | các ứng dụng cụ thể | One of the most tangible applications is in oncology | tangible benefits, tangible results |
| tumor genetic profile | n phr | /ˈtjuːmə dʒəˈnetɪk ˈprəʊfaɪl/ | đặc điểm di truyền của khối u | AI systems can analyze a patient’s tumor genetic profile | genetic profile analysis, complete genetic profile |
| evidence-based treatment | n phr | /ˈevɪdəns beɪst ˈtriːtmənt/ | điều trị dựa trên bằng chứng | Watson suggests evidence-based treatment options | evidence-based practice, evidence-based medicine |
| predictive analytics | n phr | /prɪˈdɪktɪv ˌænəˈlɪtɪks/ | phân tích dự đoán | AI is revolutionizing disease prevention through predictive analytics | advanced predictive analytics, predictive modeling |
| proactive interventions | n phr | /prəʊˈæktɪv ˌɪntəˈvenʃnz/ | can thiệp chủ động | This capability enables proactive interventions rather than reactive treatments | proactive approach, proactive measures |
| formidable obstacles | n phr | /ˈfɔːmɪdəbl ˈɒbstəklz/ | những trở ngại ghê gớm | Implementation faces several formidable obstacles | overcome obstacles, formidable challenges |
| data standardization | n phr | /ˈdeɪtə ˌstændədaɪˈzeɪʃn/ | chuẩn hóa dữ liệu | The first obstacle is data standardization and interoperability | standardization process, lack of standardization |
| algorithmic bias | n phr | /ˌælɡəˈrɪðmɪk ˈbaɪəs/ | thiên lệch thuật toán | This algorithmic bias could exacerbate existing health disparities | reduce algorithmic bias, address bias |
| profound ethical questions | n phr | /prəˈfaʊnd ˈeθɪkl ˈkwestʃənz/ | các câu hỏi đạo đức sâu sắc | AI in precision medicine raises profound ethical questions | raise ethical questions, ethical dilemma |
Passage 3 – Essential Vocabulary
| Từ vựng | Loại từ | Phiên âm | Nghĩa tiếng Việt | Ví dụ từ bài | Collocation |
|---|---|---|---|---|---|
| paradigmatic transformation | n phr | /ˌpærədɪɡˈmætɪk ˌtrænsfəˈmeɪʃn/ | sự chuyển đổi mang tính mô hình | It constitutes a paradigmatic transformation that carries profound implications | paradigmatic shift, paradigmatic change |
| profound implications | n phr | /prəˈfaʊnd ˌɪmplɪˈkeɪʃnz/ | những hàm ý sâu sắc | This carries profound implications for healthcare accessibility | have profound implications, far-reaching implications |
| tout | v | /taʊt/ | quảng cáo rầm rộ, ca ngợi | While proponents tout AI’s potential to democratize healthcare | widely touted, touted as a solution |
| inadvertently entrench | v phr | /ˌɪnədˈvɜːtntli ɪnˈtrentʃ/ | vô tình củng cố, làm sâu sắc thêm | Technologies may inadvertently entrench existing disparities | inadvertently create, inadvertently cause |
| stratification | n | /ˌstrætɪfɪˈkeɪʃn/ | sự phân tầng | Create new forms of stratification based on access to algorithmic medicine | social stratification, economic stratification |
| multifaceted | adj | /ˌmʌltiˈfæsɪtɪd/ | nhiều mặt, đa diện | The economic ramifications are multifaceted and potentially contradictory | multifaceted problem, multifaceted approach |
| operational efficiencies | n phr | /ˌɒpəˈreɪʃənl ɪˈfɪʃnsiz/ | hiệu quả vận hành | AI could generate savings through operational efficiencies | improve operational efficiency, operational excellence |
| capital-intensive | adj | /ˈkæpɪtl ɪnˈtensɪv/ | tốn kém vốn | The capital-intensive nature of AI implementation | capital-intensive industry, highly capital-intensive |
| formidable barrier | n phr | /ˈfɔːmɪdəbl ˈbæriə/ | rào cản ghê gớm | This presents a formidable barrier to entry | overcome formidable barriers, formidable challenge |
| disproportionately concentrated | v phr | /ˌdɪsprəˈpɔːʃnətli ˈkɒnsntreɪtɪd/ | tập trung không cân đối | Resources are disproportionately concentrated in high-income countries | disproportionately affected, disproportionately high |
| two-tiered system | n phr | /tuː tɪəd ˈsɪstəm/ | hệ thống hai tầng lớp | This threatens to create a two-tiered global healthcare system | two-tier structure, two-tiered approach |
| ameliorating | v | /əˈmiːliəreɪtɪŋ/ | cải thiện, làm giảm bớt | Technologies capable of ameliorating healthcare inequities | ameliorate conditions, ameliorate suffering |
| exacerbate | v | /ɪɡˈzæsəbeɪt/ | làm trầm trọng thêm | May instead exacerbate existing disparities | exacerbate problems, severely exacerbate |
| augment rather than replace | v phr | /ɔːɡˈment ˈrɑːðə ðæn rɪˈpleɪs/ | tăng cường chứ không thay thế | AI will augment rather than replace human healthcare workers | augment capabilities, augment existing systems |
| nuanced human judgment | n phr | /ˈnjuːɑːnst ˈhjuːmən ˈdʒʌdʒmənt/ | phán đoán tinh tế của con người | Activities requiring nuanced human judgment | nuanced understanding, nuanced approach |
| computational literacy | n phr | /ˌkɒmpjuˈteɪʃənl ˈlɪtərəsi/ | kiến thức về máy tính | Medical education must incorporate computational literacy | digital literacy, data literacy |
| model drift | n phr | /ˈmɒdl drɪft/ | sự trôi dạt của mô hình | A phenomenon known as model drift or distribution shift | prevent model drift, detect model drift |
| technological colonialism | n phr | /ˌteknəˈlɒdʒɪkl kəˈləʊniəlɪzəm/ | chủ nghĩa thực dân công nghệ | This technological colonialism wherein AI systems developed in wealthy nations are exported | digital colonialism, neo-colonialism |
| nascent and fragmented | adj phr | /ˈnæsnt ænd ˈfræɡmentɪd/ | còn sơ khai và phân mảnh | Governance frameworks remain nascent and fragmented | nascent industry, nascent technology |
| epistemic authority | n phr | /ˌepɪˈstiːmɪk ɔːˈθɒrəti/ | quyền lực tri thức | Questions surrounding medical agency and epistemic authority | epistemic uncertainty, epistemic status |
| automation bias | n phr | /ˌɔːtəˈmeɪʃn ˈbaɪəs/ | thiên lệch tự động hóa | The risk of automation bias whereby humans uncritically defer to algorithms | overcome automation bias, automation bias effect |
| algorithm aversion | n phr | /ˈælɡərɪðəm əˈvɜːʃn/ | sự e ngại thuật toán | Algorithm aversion – the tendency to lose confidence in algorithms after observing errors | algorithm aversion phenomenon, overcome aversion |
| epistemic humility | n phr | /ˌepɪˈstiːmɪk hjuːˈmɪləti/ | sự khiêm tốn tri thức | Cultivating appropriate epistemic humility regarding both human and machine capabilities | intellectual humility, epistemic virtues |
Kết bài
Chủ đề về trí tuệ nhân tạo trong y tế không chỉ phổ biến trong các kỳ thi IELTS Reading hiện nay mà còn là một lĩnh vực đang phát triển mạnh mẽ trên toàn cầu. Qua bộ đề thi mẫu này với đầy đủ 3 passages từ dễ đến khó, bạn đã được trải nghiệm một bài thi IELTS Reading hoàn chỉnh giống như thi thật.
Ba passages đã đưa bạn qua hành trình từ những ứng dụng cơ bản của AI trong chẩn đoán và điều trị (Passage 1), đến y học chính xác với sự kết hợp giữa AI và di truyền học (Passage 2), và cuối cùng là những tác động kinh tế-xã hội sâu sắc của công nghệ này trên quy mô toàn cầu (Passage 3). Sự đa dạng về độ khó và dạng câu hỏi giúp bạn rèn luyện toàn diện các kỹ năng cần thiết cho bài thi thực tế.
Phần đáp án chi tiết với giải thích cụ thể về vị trí thông tin và cách paraphrase sẽ giúp bạn hiểu rõ phương pháp làm bài đúng, học cách xác định từ khóa và tránh những cạm bẫy thường gặp. Đặc biệt, kho từ vựng phong phú về công nghệ y tế sẽ là hành trang quý giá không chỉ cho IELTS Reading mà còn cho cả phần Writing và Speaking khi gặp các chủ đề liên quan.
Hãy luyện tập đề này nhiều lần, phân tích kỹ từng câu hỏi và ghi nhớ từ vựng chuyên ngành. Việc làm quen với các bài đọc học thuật phức tạp như thế này sẽ giúp bạn tự tin hơn khi bước vào phòng thi IELTS thực sự. Chúc bạn học tập hiệu quả và đạt band điểm mong muốn!
