Mở bài
Trí tuệ nhân tạo (AI) đang tạo ra những bước đột phá vượt bậc trong ngành y tế toàn cầu, từ chẩn đoán bệnh chính xác hơn đến phát triển thuốc mới và cá nhân hóa phương pháp điều trị. Chủ đề “How Is Artificial Intelligence Transforming The Healthcare Industry?” không chỉ là xu hướng công nghệ hiện đại mà còn là một trong những đề tài phổ biến xuất hiện trong IELTS Reading với tần suất ngày càng tăng, đặc biệt trong các đề thi gần đây từ năm 2020 trở lại đây.
Bài viết này được thiết kế như một đề thi IELTS Reading hoàn chỉnh, giúp bạn:
- Làm quen với đề thi đầy đủ 3 passages từ dễ đến khó (Easy → Medium → Hard)
- Thực hành đa dạng dạng câu hỏi giống thi thật 100%
- Kiểm tra đáp án chi tiết kèm giải thích từng bước
- Nắm vững từ vựng chuyên ngành và kỹ thuật làm bài hiệu quả
Đề thi này phù hợp cho học viên từ band 5.0 trở lên, giúp bạn làm quen với cấu trúc đề thi thực tế và nâng cao kỹ năng đọc hiểu học thuật một cách bài bản.
1. Hướng Dẫn Làm Bài IELTS Reading
Tổng Quan Về IELTS Reading Test
IELTS Reading Test là phần thi quan trọng trong kỳ thi IELTS Academic, được thiết kế để đánh giá khả năng đọc hiểu tiếng Anh học thuật của thí sinh. Hiểu rõ cấu trúc và phân bổ thời gian hợp lý là chìa khóa để đạt band điểm cao.
Cấu trúc chuẩn:
- Thời gian: 60 phút cho 3 passages (không có thời gian chuyển đáp án bổ sung)
- Tổng số câu hỏi: 40 câu
- Độ dài văn bản: 2000-2750 từ tổng cộng
Phân bổ thời gian khuyến nghị:
- Passage 1 (Easy): 15-17 phút – Đây là passage ngắn nhất và dễ nhất, bạn cần làm nhanh để dành thời gian cho các passage khó hơn
- Passage 2 (Medium): 18-20 phút – Độ khó trung bình, yêu cầu đọc kỹ và paraphrase
- Passage 3 (Hard): 23-25 phút – Passage khó nhất với từ vựng học thuật và cấu trúc phức tạp
Lưu ý quan trọng: Bạn cần ghi đáp án trực tiếp vào answer sheet trong 60 phút, không có thời gian bổ sung như phần Listening.
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 – Câu hỏi trắc nghiệm nhiều lựa chọn
- True/False/Not Given – Xác định thông tin đúng/sai/không được đề cập
- Matching Information – Nối thông tin với đoạn văn tương ứng
- Yes/No/Not Given – Xác định quan điểm tác giả
- Matching Headings – Chọn tiêu đề phù hợp cho các đoạn văn
- Summary Completion – Hoàn thành đoạn tóm tắt
- Matching Features – Nối đặc điểm với danh mục
Mỗi dạng câu hỏi yêu cầu kỹ năng đọc hiểu và chiến lược làm bài khác nhau. Trong phần giải thích đáp án, bạn sẽ học được cách tiếp cận hiệu quả cho từng dạng.
Trí tuệ nhân tạo đang cách mạng hóa ngành y tế với công nghệ tiên tiến và ứng dụng thực tế
2. IELTS Reading Practice Test
PASSAGE 1 – The Rise of AI in Medical Diagnostics
Độ khó: Easy (Band 5.0-6.5)
Thời gian đề xuất: 15-17 phút
Artificial intelligence is rapidly changing the way doctors diagnose diseases and treat patients. In recent years, AI-powered systems have demonstrated remarkable abilities to analyze medical images, identify patterns, and provide accurate diagnoses that sometimes exceed human capabilities. This technological revolution is making healthcare more accessible, efficient, and reliable for millions of people around the world.
One of the most significant applications of AI in healthcare is in medical imaging. Traditional methods of examining X-rays, CT scans, and MRIs require highly trained radiologists who spend years developing their expertise. However, these specialists can sometimes miss subtle signs of disease, especially when they are tired or dealing with large volumes of images. AI systems, on the other hand, never get tired and can process thousands of images in the time it takes a human to examine just one. For example, machine learning algorithms can now detect early signs of lung cancer in chest X-rays with an accuracy rate of over 94%, which is comparable to or better than experienced radiologists.
Deep learning, a subset of artificial intelligence, has been particularly revolutionary in dermatology. Skin cancer is one of the most common forms of cancer worldwide, and early detection is crucial for successful treatment. AI systems trained on thousands of images of various skin conditions can now distinguish between benign moles and malignant melanomas with impressive precision. A study conducted at Stanford University showed that an AI algorithm could identify skin cancer as accurately as board-certified dermatologists. This technology is especially valuable in remote areas where access to specialist doctors is limited.
The benefits of AI diagnostics extend beyond accuracy. Speed is another crucial advantage. In emergency situations, every minute counts. AI systems can analyze medical data almost instantaneously, providing doctors with vital information that helps them make life-saving decisions quickly. For instance, in cases of stroke, AI can rapidly analyze brain scans to determine the type and location of the stroke, enabling doctors to choose the most appropriate treatment immediately. This rapid diagnosis can mean the difference between a full recovery and permanent disability.
Another important aspect is consistency. Human doctors may have different interpretations of the same medical image depending on their experience, training, or even their physical condition at the time of examination. AI systems, however, apply the same analytical standards to every case, ensuring consistent results. This consistency is particularly valuable in screening programs where large populations need to be examined for specific diseases. For example, AI is being used in breast cancer screening programs in several countries, helping to reduce false positives and ensuring that patients who need further investigation are identified accurately.
Despite these advantages, it’s important to note that AI is not intended to replace doctors but rather to assist them. The technology works best when combined with human expertise and judgment. Doctors can use AI as a “second opinion” tool, helping them to verify their diagnoses or spot potential issues they might have missed. This collaborative approach between human intelligence and artificial intelligence is creating a new era of medical practice that is more accurate, efficient, and patient-centered than ever before.
Cost-effectiveness is another benefit that cannot be ignored. Training specialist doctors takes many years and involves substantial investment. There is also a global shortage of medical specialists, particularly in developing countries. AI systems, once developed and validated, can be deployed widely at relatively low cost, making high-quality diagnostic services available to communities that previously had no access to them. This democratization of healthcare has the potential to save countless lives and reduce health inequalities around the world.
Looking ahead, researchers are working on even more advanced AI applications. Some systems are being developed to predict diseases before symptoms appear by analyzing genetic data, lifestyle factors, and environmental influences. Others are learning to personalize treatment recommendations based on individual patient characteristics. As these technologies continue to mature, they promise to make healthcare not just more accurate and efficient, but also more predictive and preventive.
Questions 1-13
Questions 1-5: Multiple Choice
Choose the correct letter, A, B, C, or D.
1. According to the passage, what is one advantage AI systems have over human radiologists?
A) They are cheaper to train
B) They never experience fatigue
C) They can work in remote locations
D) They have better eyesight
2. The accuracy rate of AI in detecting lung cancer in X-rays is:
A) Exactly 94%
B) Less than human radiologists
C) More than 94%
D) Around 94% or higher
3. According to the Stanford University study, AI algorithms can identify skin cancer:
A) Better than any human doctor
B) Only in specific types of cancer
C) As accurately as qualified dermatologists
D) Faster but less accurately than doctors
4. In stroke cases, AI helps doctors by:
A) Performing surgery automatically
B) Quickly analyzing brain scans
C) Replacing emergency room doctors
D) Predicting future strokes
5. The passage suggests that AI in healthcare is best used:
A) Instead of human doctors
B) Only in emergency situations
C) Together with human expertise
D) Exclusively for cancer detection
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 systems can examine more medical images in a given time than human specialists.
7. Skin cancer is the most dangerous type of cancer worldwide.
8. AI diagnostic systems are currently available in all hospitals globally.
9. There is a worldwide shortage of medical specialists.
Questions 10-13: Sentence Completion
Complete the sentences below.
Choose NO MORE THAN TWO WORDS from the passage for each answer.
10. AI provides __ results because it applies the same standards to every case.
11. In breast cancer screening, AI helps to reduce __ and identify patients needing more tests.
12. The combination of human and artificial intelligence is creating a more __ approach to medical practice.
13. Future AI systems may be able to predict diseases by analyzing genetic data and __ factors.
PASSAGE 2 – AI-Driven Drug Discovery and Development
Độ khó: Medium (Band 6.0-7.5)
Thời gian đề xuất: 18-20 phút
The pharmaceutical industry is experiencing a paradigm shift thanks to artificial intelligence, which is revolutionizing the traditionally lengthy and expensive process of drug discovery and development. Historically, bringing a new drug to market has been an arduous journey that typically spans 10 to 15 years and costs upwards of $2.6 billion. This protracted timeline involves multiple stages: target identification, compound screening, preclinical testing, and several phases of clinical trials. However, AI is now compressing these timeframes and reducing costs while simultaneously improving success rates.
At the heart of this transformation lies AI’s ability to process vast datasets and identify patterns that would be impossible for human researchers to discern. Machine learning algorithms can analyze millions of molecular structures, predict their biological activities, and determine which compounds are most likely to become effective drugs. This computational approach to drug discovery represents a fundamental departure from traditional trial-and-error methods, where scientists would synthesize and test thousands of compounds in the laboratory, often spending years on projects that ultimately failed.
One of the most compelling examples of AI’s impact is in the field of oncology. Cancer treatment has always been challenging due to the disease’s genetic complexity and its ability to develop resistance to drugs. AI systems can now analyze genomic data from thousands of tumors, identify mutations that drive cancer growth, and suggest existing drugs that might be repurposed to target these specific mutations. This approach, known as drug repurposing, can dramatically shorten development time because the drugs have already undergone safety testing. Several pharmaceutical companies have successfully identified new applications for existing medications using AI, potentially bringing treatments to patients years earlier than would have been possible through conventional development pathways.
The technology is also proving invaluable in predicting drug toxicity and side effects before compounds enter expensive clinical trials. Traditional methods of toxicity testing rely heavily on animal models, which are not only ethically controversial but also imperfect predictors of how drugs will behave in humans. AI models trained on historical data from thousands of drug trials can predict with increasing accuracy whether a compound is likely to cause adverse reactions. This predictive capability allows researchers to eliminate problematic candidates early in the development process, saving both time and resources while reducing the need for animal testing.
Personalized medicine represents another frontier where AI is making significant strides. The recognition that patients respond differently to the same medication due to genetic variations, environmental factors, and lifestyle differences has led to a growing emphasis on tailored treatments. AI systems can integrate diverse data sources—including genomic sequences, medical histories, lifestyle information, and even data from wearable devices—to predict how individual patients will respond to specific drugs. This holistic approach enables doctors to prescribe the most effective medication at the optimal dose for each patient, minimizing side effects and maximizing therapeutic benefits.
The COVID-19 pandemic provided a dramatic demonstration of AI’s potential in accelerated drug development. When the virus emerged in late 2019, AI systems were quickly deployed to screen existing drugs for potential antiviral properties. Within weeks, researchers using AI had identified several promising candidates for repurposing, including drugs originally developed for other viral infections and even some cancer medications. While not all of these candidates ultimately proved effective against COVID-19, the speed at which they were identified would have been unthinkable using traditional methods. Furthermore, AI played a crucial role in vaccine development by helping scientists understand the virus’s structure and predict which viral proteins would trigger the strongest immune response.
However, the integration of AI into drug development is not without challenges. Regulatory agencies like the FDA and EMA are still developing frameworks to evaluate AI-designed drugs, and questions remain about data privacy, algorithmic transparency, and liability when AI systems make errors. There are also concerns about data quality: AI systems are only as good as the data they are trained on, and biased or incomplete datasets can lead to flawed predictions. The pharmaceutical industry must therefore invest in high-quality data collection and ensure that training datasets are representative of diverse populations to avoid perpetuating health disparities.
Despite these challenges, the trajectory is clear: AI is becoming an indispensable tool in pharmaceutical research. Venture capital investment in AI drug discovery companies has surged in recent years, with billions of dollars flowing into startups that promise to transform how medicines are developed. Major pharmaceutical companies are forming partnerships with AI firms or building their own in-house capabilities. Some experts predict that within the next decade, the majority of new drugs will have been discovered or developed with substantial AI involvement. This shift promises not only to make drug development faster and cheaper but also to bring more effective, safer, and more personalized treatments to patients worldwide.
Questions 14-26
Questions 14-18: Yes/No/Not Given
Do the following statements agree with the views of the writer in the passage?
Write:
- YES if the statement agrees with the views of the writer
- NO if the statement contradicts the views of the writer
- NOT GIVEN if it is impossible to say what the writer thinks about this
14. Traditional drug development methods are more reliable than AI-based approaches.
15. AI can identify patterns in molecular structures that humans cannot detect.
16. Drug repurposing using AI can reduce the time needed to bring treatments to patients.
17. Animal testing provides perfect predictions of drug behavior in humans.
18. All drugs identified by AI during the COVID-19 pandemic were effective.
Questions 19-22: Matching Information
Match the following statements (19-22) with the correct paragraph (A-H).
You may use any letter more than once.
A – Paragraph 1
B – Paragraph 2
C – Paragraph 3
D – Paragraph 4
E – Paragraph 5
F – Paragraph 6
G – Paragraph 7
H – Paragraph 8
19. A description of how AI helps predict individual patient responses to medication
20. An explanation of the challenges facing AI integration in drug development
21. Information about the traditional cost and duration of drug development
22. An example of AI’s application during a global health crisis
Questions 23-26: Summary Completion
Complete the summary below.
Choose NO MORE THAN TWO WORDS from the passage for each answer.
AI is transforming pharmaceutical research by analyzing millions of 23. __ to predict which compounds could become effective drugs. In oncology, AI examines 24. __ from tumors to identify mutations and suggest drugs for repurposing. The technology can also predict 25. __ before expensive trials begin, using data from previous drug studies. For personalized medicine, AI integrates information from multiple sources including genomic data and 26. __ to determine optimal treatments for individual patients.
PASSAGE 3 – The Ethical and Societal Implications of AI in Healthcare Systems
Độ khó: Hard (Band 7.0-9.0)
Thời gian đề xuất: 23-25 phút
The proliferation of artificial intelligence technologies within healthcare ecosystems has precipitated an unprecedented convergence of technological capability and ethical complexity. While the demonstrable benefits of AI in diagnostic accuracy, treatment optimization, and operational efficiency are increasingly well-documented, the attendant ethical dilemmas and societal ramifications demand rigorous scrutiny. This discourse extends beyond mere technical implementation to encompass fundamental questions about algorithmic accountability, epistemic justice, autonomy, and the very nature of the patient-practitioner relationship in an era of machine-augmented medicine.
A central concern revolves around the concept of algorithmic bias and its potential to exacerbate existing health disparities. Machine learning systems are inherently dependent upon the data used for their training, and when these datasets systematically underrepresent certain demographic groups—whether defined by race, ethnicity, gender, socioeconomic status, or geographic location—the resulting algorithms may perpetuate or even amplify discriminatory patterns. A seminal study published in 2019 revealed that a widely deployed healthcare algorithm in the United States systematically disadvantaged Black patients by incorrectly assessing their medical needs. The algorithm, which influenced care decisions for millions of patients, used healthcare costs as a proxy for health needs, failing to account for systemic inequalities in healthcare access that result in Black patients receiving less expensive care even when experiencing comparable or greater illness severity. This case exemplifies how ostensibly neutral technical systems can encode and institutionalize social inequities when their design fails to address structural disparities.
The opacity of many AI systems—particularly those employing deep neural networks—presents another formidable challenge to ethical implementation. These “black box” algorithms can make highly accurate predictions yet remain fundamentally inscrutable, offering little insight into the reasoning processes underlying their decisions. This interpretability deficit creates a tension with the principle of informed consent, a cornerstone of medical ethics that requires patients to understand the basis of diagnostic or therapeutic recommendations before agreeing to them. How can a patient provide meaningful consent to a treatment recommended by an algorithm whose decision-making logic even its creators cannot fully explain? Moreover, when AI systems make errors—as they invariably will—the lack of transparency complicates accountability. Should responsibility lie with the algorithm’s developers, the healthcare institution that deployed it, the clinician who relied upon its output, or the regulatory agencies that approved its use? These questions lack straightforward answers and represent a significant impediment to establishing robust governance frameworks.
Data privacy constitutes yet another dimension of the ethical landscape. AI systems require enormous quantities of patient data for training and continual refinement, raising profound concerns about confidentiality and surveillance. The aggregation of electronic health records, genomic information, behavioral data from wearable devices, and even social media activity creates comprehensive digital profiles that, if compromised, could expose individuals to discrimination in employment, insurance, or social contexts. The commodification of health data by technology companies seeking to develop proprietary algorithms further muddies ethical waters, particularly when patients may be insufficiently informed about how their data will be used or lack meaningful alternatives to data sharing if they wish to access certain healthcare services. The European Union’s General Data Protection Regulation (GDPR) represents an attempt to address these concerns through stringent data protection requirements, including the “right to explanation” for automated decisions, yet such regulations remain unevenly implemented globally and their practical efficacy in the healthcare domain remains subject to debate.
The automation of medical decision-making also raises philosophical questions about professional autonomy and the nature of medical expertise. As AI systems become more sophisticated and demonstrably superior to human clinicians in certain specialized tasks, there is a conceivable trajectory toward deskilling of medical professionals or erosion of clinical judgment. If an algorithm consistently outperforms human radiologists in detecting tumors, does this obviate the need for extensive radiological training? Should doctors be legally obligated to follow AI recommendations even when they contradict their own clinical intuition? These questions touch upon the epistemological status of medical knowledge itself and challenge traditional conceptions of medical professionalism that emphasize experiential wisdom and contextual judgment alongside technical competence.
Furthermore, the integration of AI into healthcare systems reflects and reinforces broader societal power dynamics. The development of medical AI is predominantly concentrated in wealthy nations and large technology corporations, raising concerns about technological colonialism whereby solutions designed for well-resourced contexts are imposed upon low-resource settings without adequate consideration of local needs, values, or infrastructural constraints. The asymmetry in AI capabilities between developed and developing nations threatens to widen the global health divide, with cutting-edge AI-assisted care becoming another marker of privilege rather than a tool for equalizing health outcomes. Moreover, the commercial imperatives driving much AI development may misalign with public health priorities, channeling resources toward profitable conditions affecting wealthy populations while neglecting diseases that disproportionately burden poorer communities.
The transformative potential of AI in healthcare cannot be fully realized without concurrent efforts to address these multifaceted ethical challenges. This requires interdisciplinary collaboration among technologists, clinicians, ethicists, policymakers, patient advocates, and affected communities to develop governance frameworks that promote beneficial innovation while safeguarding against harm. Algorithmic audits and impact assessments should become standard practice, ensuring that AI systems are regularly evaluated for bias, accuracy, and unintended consequences. Transparency mechanisms—including algorithmic explainability tools and public registries of AI systems used in clinical settings—can enhance accountability and build trust. Participatory design processes that involve diverse stakeholders, particularly marginalized groups most vulnerable to algorithmic harm, are essential for developing AI systems that reflect pluralistic values and serve equitable ends.
Ultimately, the question is not whether AI should be integrated into healthcare—that process is already well underway—but rather how this integration can be guided by ethical principles and oriented toward social justice. The technical sophistication of AI systems must be matched by equivalent sophistication in ethical reasoning and institutional governance. Only through such a holistic approach can we harness AI’s considerable promise for improving health outcomes while mitigating risks and ensuring that the benefits of this technological revolution are broadly shared rather than narrowly concentrated. The healthcare systems of the future will undoubtedly be deeply shaped by artificial intelligence; our collective responsibility is to ensure they remain fundamentally human in their values and commitments.
Các thách thức đạo đức và xã hội trong việc ứng dụng trí tuệ nhân tạo cho hệ thống chăm sóc sức khỏe
Questions 27-40
Questions 27-31: Multiple Choice
Choose the correct letter, A, B, C, or D.
27. According to the passage, algorithmic bias in healthcare AI primarily results from:
A) Intentional discrimination by developers
B) Inadequate representation in training datasets
C) Insufficient computing power
D) Regulatory failures
28. The 2019 study mentioned in the passage demonstrated that:
A) Black patients received more expensive care than necessary
B) Healthcare costs accurately reflected health needs
C) An algorithm underestimated Black patients’ medical needs
D) Most healthcare algorithms are free from bias
29. The “black box” problem in AI systems refers to:
A) The physical appearance of AI hardware
B) The lack of transparency in decision-making processes
C) The absence of qualified operators
D) The high cost of AI technology
30. According to the passage, the GDPR attempts to address data privacy concerns by:
A) Banning all AI use in healthcare
B) Requiring patients to pay for data protection
C) Establishing strict data protection requirements
D) Limiting healthcare data collection entirely
31. The passage suggests that AI development in healthcare is mainly concentrated in:
A) Developing nations with large populations
B) International health organizations
C) Wealthy countries and large technology companies
D) University research centers exclusively
Questions 32-36: Matching Features
Match each ethical concern (32-36) with the correct consequence or implication (A-H) mentioned in the passage.
List of Ethical Concerns:
32. Algorithmic bias
33. Lack of algorithmic transparency
34. Data privacy issues
35. Automation of medical decisions
36. Concentration of AI development
List of Consequences/Implications:
A) May lead to deskilling of medical professionals
B) Creates comprehensive digital profiles vulnerable to misuse
C) Prevents meaningful informed consent
D) Exacerbates existing health disparities
E) Increases healthcare costs
F) Reduces the need for clinical trials
G) Widens the global health divide
H) Eliminates human error completely
Questions 37-40: Short-answer Questions
Answer the questions below.
Choose NO MORE THAN THREE WORDS from the passage for each answer.
37. What type of networks make AI systems particularly difficult to interpret?
38. What principle of medical ethics requires patients to understand recommendations before agreeing?
39. What type of design processes should involve diverse stakeholders to develop equitable AI systems?
40. According to the passage, what must match the technical sophistication of AI systems?
3. Answer Keys – Đáp Án
PASSAGE 1: Questions 1-13
- B
- D
- C
- B
- C
- TRUE
- NOT GIVEN
- NOT GIVEN
- TRUE
- consistent
- false positives
- patient-centered
- lifestyle
PASSAGE 2: Questions 14-26
- NO
- YES
- YES
- NO
- NO
- E
- G
- A
- F
- molecular structures
- genomic data
- drug toxicity / side effects
- wearable devices
PASSAGE 3: Questions 27-40
- B
- C
- B
- C
- C
- D
- C
- B
- A
- G
- deep neural networks
- informed consent
- participatory design processes
- ethical reasoning
4. Giải Thích Đáp Án Chi Tiết
Passage 1 – Giải Thích
Câu 1: B
- Dạng câu hỏi: Multiple Choice
- Từ khóa: advantage, AI systems, human radiologists
- Vị trí trong bài: Đoạn 2, dòng 4-6
- Giải thích: Câu trong bài viết rõ ràng: “However, these specialists can sometimes miss subtle signs of disease, especially when they are tired… AI systems, on the other hand, never get tired”. Đây là paraphrase của “never experience fatigue”. Các đáp án khác không được đề cập như những lợi thế so sánh trực tiếp.
Câu 2: D
- Dạng câu hỏi: Multiple Choice
- Từ khóa: accuracy rate, lung cancer, X-rays
- Vị trí trong bài: Đoạn 2, dòng cuối
- Giải thích: Bài viết nói “with an accuracy rate of over 94%, which is comparable to or better than experienced radiologists”. Cụm “over 94%” được paraphrase thành “around 94% or higher”, bao gồm cả khả năng lớn hơn.
Câu 3: C
- Dạng câu hỏi: Multiple Choice
- Từ khóa: Stanford University study, identify skin cancer
- Vị trí trong bài: Đoạn 3, dòng 5-6
- Giải thích: “An AI algorithm could identify skin cancer as accurately as board-certified dermatologists” được paraphrase thành “as accurately as qualified dermatologists”. “Board-certified” và “qualified” có nghĩa tương đương.
Câu 4: B
- Dạng câu hỏi: Multiple Choice
- Từ khóa: stroke cases, AI helps doctors
- Vị trí trong bài: Đoạn 4, dòng 4-6
- Giải thích: Bài viết nói “AI can rapidly analyze brain scans to determine the type and location of the stroke”. Đây chính xác là đáp án B. AI không thực hiện phẫu thuật (A), không thay thế bác sĩ (C), và không dự đoán đột quỵ tương lai (D).
Câu 5: C
- Dạng câu hỏi: Multiple Choice
- Từ khóa: AI in healthcare, best used
- Vị trí trong bài: Đoạn 6, dòng 1-3
- Giải thích: Câu chủ đề của đoạn 6 nói rõ: “AI is not intended to replace doctors but rather to assist them. The technology works best when combined with human expertise and judgment”. Đây là paraphrase của “together with human expertise”.
Câu 6: TRUE
- Dạng câu hỏi: True/False/Not Given
- Từ khóa: AI systems, examine more medical images, given time
- Vị trí trong bài: Đoạn 2, dòng 6-7
- Giải thích: Bài viết khẳng định “AI systems… can process thousands of images in the time it takes a human to examine just one”. Điều này rõ ràng ủng hộ nhận định rằng AI có thể xem xét nhiều hình ảnh hơn trong cùng một khoảng thời gian.
Câu 7: NOT GIVEN
- Dạng câu hỏi: True/False/Not Given
- Từ khóa: skin cancer, most dangerous type
- Vị trí trong bài: Đoạn 3
- Giải thích: Bài viết chỉ nói “Skin cancer is one of the most common forms of cancer worldwide” (phổ biến nhất), không đề cập đến việc nó có phải là loại nguy hiểm nhất hay không. “Common” (phổ biến) khác với “dangerous” (nguy hiểm).
Câu 8: NOT GIVEN
- Dạng câu hỏi: True/False/Not Given
- Từ khóa: AI diagnostic systems, available, all hospitals globally
- Vị trí trong bài: Toàn bộ passage
- Giải thích: Bài viết không đề cập đến việc AI có sẵn ở tất cả bệnh viện hay không. Nó chỉ nói về khả năng và lợi ích của AI, chứ không nói về mức độ phổ biến toàn cầu.
Câu 9: TRUE
- Dạng câu hỏi: True/False/Not Given
- Từ khóa: worldwide shortage, medical specialists
- Vị trí trong bài: Đoạn 7, dòng 3
- Giải thích: Bài viết nói rõ “There is also a global shortage of medical specialists”. “Global” và “worldwide” là từ đồng nghĩa, câu này khớp hoàn toàn với thông tin trong bài.
Câu 10: consistent
- Dạng câu hỏi: Sentence Completion
- Từ khóa: AI provides, results, same standards
- Vị trí trong bài: Đoạn 5, dòng 3-4
- Giải thích: Câu trong bài: “AI systems… apply the same analytical standards to every case, ensuring consistent results”. Từ cần điền là “consistent”.
Câu 11: false positives
- Dạng câu hỏi: Sentence Completion
- Từ khóa: breast cancer screening, AI helps reduce
- Vị trí trong bài: Đoạn 5, dòng cuối
- Giải thích: “AI is being used in breast cancer screening programs… helping to reduce false positives”. Cụm từ chính xác là “false positives”.
Câu 12: patient-centered
- Dạng câu hỏi: Sentence Completion
- Từ khóa: combination human and artificial intelligence, creating
- Vị trí trong bài: Đoạn 6, dòng 4-5
- Giải thích: “This collaborative approach between human intelligence and artificial intelligence is creating a new era of medical practice that is more accurate, efficient, and patient-centered”. Từ cuối cùng trong chuỗi tính từ là “patient-centered”.
Câu 13: lifestyle
- Dạng câu hỏi: Sentence Completion
- Từ khóa: predict diseases, genetic data
- Vị trí trong bài: Đoạn 8, dòng 2-3
- Giải thích: “Some systems are being developed to predict diseases before symptoms appear by analyzing genetic data, lifestyle factors, and environmental influences”. Từ cần điền là “lifestyle”.
Passage 2 – Giải Thích
Câu 14: NO
- Dạng câu hỏi: Yes/No/Not Given
- Từ khóa: traditional drug development, more reliable, AI-based approaches
- Vị trí trong bài: Đoạn 1-2
- Giải thích: Tác giả mô tả phương pháp truyền thống là “trial-and-error methods” và AI là “fundamental departure” giúp cải thiện tỷ lệ thành công. Điều này mâu thuẫn với quan điểm rằng phương pháp truyền thống đáng tin cậy hơn.
Câu 15: YES
- Dạng câu hỏi: Yes/No/Not Given
- Từ khóa: AI, identify patterns, humans cannot detect
- Vị trí trong bài: Đoạn 2, dòng 1-2
- Giải thích: “AI’s ability to process vast datasets and identify patterns that would be impossible for human researchers to discern” – tác giả rõ ràng đồng ý với nhận định này.
Câu 16: YES
- Dạng câu hỏi: Yes/No/Not Given
- Từ khóa: drug repurposing, AI, reduce time
- Vị trí trong bài: Đoạn 3, dòng 5-7
- Giải thích: “This approach, known as drug repurposing, can dramatically shorten development time” và “potentially bringing treatments to patients years earlier”. Tác giả ủng hộ quan điểm này.
Câu 17: NO
- Dạng câu hỏi: Yes/No/Not Given
- Từ khóa: animal testing, perfect predictions, drug behavior
- Vị trí trong bài: Đoạn 4, dòng 3-4
- Giải thích: Bài viết nói “animal models… are not only ethically controversial but also imperfect predictors of how drugs will behave in humans”. Từ “imperfect” mâu thuẫn trực tiếp với “perfect”.
Câu 18: NO
- Dạng câu hỏi: Yes/No/Not Given
- Từ khóa: all drugs, AI, COVID-19, effective
- Vị trí trong bài: Đoạn 6, dòng 5-6
- Giải thích: “While not all of these candidates ultimately proved effective against COVID-19” – tác giả nói rõ không phải tất cả đều hiệu quả.
Câu 19: E
- Dạng câu hỏi: Matching Information
- Từ khóa: predict individual patient responses, medication
- Vị trí trong bài: Đoạn 5 (Paragraph E)
- Giải thích: Đoạn 5 nói về “Personalized medicine” và “AI systems can integrate diverse data sources… to predict how individual patients will respond to specific drugs”.
Câu 20: G
- Dạng câu hỏi: Matching Information
- Từ khóa: challenges, AI integration, drug development
- Vị trí trong bài: Đoạn 7 (Paragraph G)
- Giải thích: Đoạn 7 bắt đầu với “However, the integration of AI into drug development is not without challenges” và liệt kê các thách thức về quy định, quyền riêng tư dữ liệu, và chất lượng dữ liệu.
Câu 21: A
- Dạng câu hỏi: Matching Information
- Từ khóa: traditional cost, duration, drug development
- Vị trí trong bài: Đoạn 1 (Paragraph A)
- Giải thích: Đoạn 1 đề cập “bringing a new drug to market has been an arduous journey that typically spans 10 to 15 years and costs upwards of $2.6 billion”.
Câu 22: F
- Dạng câu hỏi: Matching Information
- Từ khóa: AI application, global health crisis
- Vị trí trong bài: Đoạn 6 (Paragraph F)
- Giải thích: Đoạn 6 nói về “The COVID-19 pandemic provided a dramatic demonstration of AI’s potential” với ví dụ cụ thể về ứng dụng AI.
Câu 23: molecular structures
- Dạng câu hỏi: Summary Completion
- Từ khóa: analyzing millions
- Vị trí trong bài: Đoạn 2, dòng 2-3
- Giải thích: “Machine learning algorithms can analyze millions of molecular structures, predict their biological activities”.
Câu 24: genomic data
- Dạng câu hỏi: Summary Completion
- Từ khóa: oncology, examines, from tumors
- Vị trí trong bài: Đoạn 3, dòng 3-4
- Giải thích: “AI systems can now analyze genomic data from thousands of tumors, identify mutations”.
Câu 25: drug toxicity / side effects
- Dạng câu hỏi: Summary Completion
- Từ khóa: predict, before expensive trials
- Vị trí trong bài: Đoạn 4, dòng 1
- Giải thích: “The technology is also proving invaluable in predicting drug toxicity and side effects before compounds enter expensive clinical trials”. Cả hai từ đều chấp nhận được.
Câu 26: wearable devices
- Dạng câu hỏi: Summary Completion
- Từ khóa: personalized medicine, integrates information, genomic data
- Vị trí trong bài: Đoạn 5, dòng 3-4
- Giải thích: “AI systems can integrate diverse data sources—including genomic sequences, medical histories, lifestyle information, and even data from wearable devices”.
Passage 3 – Giải Thích
Câu 27: B
- Dạng câu hỏi: Multiple Choice
- Từ khóa: algorithmic bias, primarily results from
- Vị trí trong bài: Đoạn 2, dòng 2-4
- Giải thích: “Machine learning systems are inherently dependent upon the data used for their training, and when these datasets systematically underrepresent certain demographic groups… the resulting algorithms may perpetuate or even amplify discriminatory patterns”. Đáp án B “Inadequate representation in training datasets” là paraphrase chính xác.
Câu 28: C
- Dạng câu hỏi: Multiple Choice
- Từ khóa: 2019 study, demonstrated
- Vị trí trong bài: Đoạn 2, dòng 5-8
- Giải thích: “A seminal study… revealed that a widely deployed healthcare algorithm… systematically disadvantaged Black patients by incorrectly assessing their medical needs”. “Underestimated medical needs” là paraphrase của “incorrectly assessing their medical needs” trong ngữ cảnh bị thiệt thòi.
Câu 29: B
- Dạng câu hỏi: Multiple Choice
- Từ khóa: black box problem, refers to
- Vị trí trong bài: Đoạn 3, dòng 1-3
- Giải thích: “The opacity of many AI systems… These ‘black box’ algorithms can make highly accurate predictions yet remain fundamentally inscrutable, offering little insight into the reasoning processes”. “Lack of transparency in decision-making processes” là định nghĩa chính xác của “opacity” và “inscrutable”.
Câu 30: C
- Dạng câu hỏi: Multiple Choice
- Từ khóa: GDPR, attempts to address
- Vị trí trong bài: Đoạn 4, dòng cuối
- Giải thích: “The European Union’s General Data Protection Regulation (GDPR) represents an attempt to address these concerns through stringent data protection requirements”. Đáp án C là paraphrase trực tiếp.
Câu 31: C
- Dạng câu hỏi: Multiple Choice
- Từ khóa: AI development, healthcare, mainly concentrated
- Vị trí trong bài: Đoạn 6, dòng 2
- Giải thích: “The development of medical AI is predominantly concentrated in wealthy nations and large technology corporations”. Đáp án C là paraphrase chính xác.
Câu 32: D
- Dạng câu hỏi: Matching Features
- Từ khóa: algorithmic bias
- Vị trí trong bài: Đoạn 2
- Giải thích: Đoạn 2 nói rõ algorithmic bias có thể “exacerbate existing health disparities” và “perpetuate or even amplify discriminatory patterns”.
Câu 33: C
- Dạng câu hỏi: Matching Features
- Từ khóa: lack of algorithmic transparency
- Vị trí trong bài: Đoạn 3
- Giải thích: “This interpretability deficit creates a tension with the principle of informed consent… How can a patient provide meaningful consent to a treatment recommended by an algorithm whose decision-making logic even its creators cannot fully explain?”
Câu 34: B
- Dạng câu hỏi: Matching Features
- Từ khóa: data privacy issues
- Vị trí trong bài: Đoạn 4
- Giải thích: “The aggregation of electronic health records, genomic information, behavioral data… creates comprehensive digital profiles that, if compromised, could expose individuals to discrimination”.
Câu 35: A
- Dạng câu hỏi: Matching Features
- Từ khóa: automation of medical decisions
- Vị trí trong bài: Đoạn 5
- Giải thích: “As AI systems become more sophisticated… there is a conceivable trajectory toward deskilling of medical professionals or erosion of clinical judgment”.
Câu 36: G
- Dạng câu hỏi: Matching Features
- Từ khóa: concentration of AI development
- Vị trí trong bài: Đoạn 6
- Giải thích: “The asymmetry in AI capabilities between developed and developing nations threatens to widen the global health divide”.
Câu 37: deep neural networks
- Dạng câu hỏi: Short-answer Questions
- Từ khóa: networks, difficult to interpret
- Vị trí trong bài: Đoạn 3, dòng 1
- Giải thích: “The opacity of many AI systems—particularly those employing deep neural networks”.
Câu 38: informed consent
- Dạng câu hỏi: Short-answer Questions
- Từ khóa: principle, medical ethics, understand recommendations
- Vị trí trong bài: Đoạn 3, dòng 4-5
- Giải thích: “This interpretability deficit creates a tension with the principle of informed consent, a cornerstone of medical ethics that requires patients to understand the basis of diagnostic or therapeutic recommendations”.
Câu 39: participatory design processes
- Dạng câu hỏi: Short-answer Questions
- Từ khóa: design processes, diverse stakeholders, equitable AI
- Vị trí trong bài: Đoạn 7, dòng 6-8
- Giải thích: “Participatory design processes that involve diverse stakeholders… are essential for developing AI systems that reflect pluralistic values and serve equitable ends”.
Câu 40: ethical reasoning
- Dạng câu hỏi: Short-answer Questions
- Từ khóa: match, technical sophistication
- Vị trí trong bài: Đoạn 8, dòng 3-4
- Giải thích: “The technical sophistication of AI systems must be matched by equivalent sophistication in ethical reasoning and institutional governance”.
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 |
|---|---|---|---|---|---|
| diagnose | v | /ˌdaɪəɡˈnəʊz/ | chẩn đoán | doctors diagnose diseases | diagnose diseases, diagnose accurately |
| AI-powered | adj | /ˌeɪ.aɪ ˈpaʊəd/ | được vận hành bởi AI | AI-powered systems | AI-powered systems/tools |
| accurate | adj | /ˈækjərət/ | chính xác | provide accurate diagnoses | accurate diagnosis, highly accurate |
| accessible | adj | /əkˈsesəbl/ | có thể tiếp cận | making healthcare more accessible | accessible healthcare, easily accessible |
| medical imaging | n | /ˈmedɪkl ˈɪmɪdʒɪŋ/ | chụp hình ảnh y khoa | significant applications in medical imaging | medical imaging technology |
| subtle signs | n | /ˈsʌtl saɪnz/ | dấu hiệu tinh tế | miss subtle signs of disease | subtle signs, detect subtle changes |
| machine learning | n | /məˈʃiːn ˈlɜːnɪŋ/ | học máy | machine learning algorithms | machine learning algorithms/models |
| accuracy rate | n | /ˈækjərəsi reɪt/ | tỷ lệ chính xác | accuracy rate of over 94% | high accuracy rate, improve accuracy rate |
| dermatology | n | /ˌdɜːməˈtɒlədʒi/ | khoa da liễu | revolutionary in dermatology | dermatology department/specialist |
| early detection | n | /ˈɜːli dɪˈtekʃn/ | phát hiện sớm | early detection is crucial | early detection of cancer |
| consistency | n | /kənˈsɪstənsi/ | tính nhất quán | another important aspect is consistency | consistency in results, maintain consistency |
| cost-effectiveness | n | /kɒst ɪˈfektɪvnəs/ | hiệu quả chi phí | cost-effectiveness is another benefit | improve cost-effectiveness |
Passage 2 – Essential Vocabulary
| Từ vựng | Loại từ | Phiên âm | Nghĩa tiếng Việt | Ví dụ từ bài | Collocation |
|---|---|---|---|---|---|
| pharmaceutical | adj | /ˌfɑːməˈsjuːtɪkl/ | dược phẩm | pharmaceutical industry | pharmaceutical industry/company |
| paradigm shift | n | /ˈpærədaɪm ʃɪft/ | sự thay đổi mô hình | experiencing a paradigm shift | undergo a paradigm shift |
| revolutionizing | v | /ˌrevəˈluːʃənaɪzɪŋ/ | cách mạng hóa | revolutionizing the process | revolutionize healthcare/industry |
| arduous | adj | /ˈɑːdjuəs/ | gian khổ, khó khăn | an arduous journey | arduous journey/task |
| compound screening | n | /ˈkɒmpaʊnd ˈskriːnɪŋ/ | sàng lọc hợp chất | compound screening stage | drug compound screening |
| clinical trials | n | /ˈklɪnɪkl ˈtraɪəlz/ | thử nghiệm lâm sàng | several phases of clinical trials | conduct clinical trials |
| molecular structures | n | /məˈlekjələ ˈstrʌktʃəz/ | cấu trúc phân tử | analyze molecular structures | molecular structures and properties |
| oncology | n | /ɒŋˈkɒlədʒi/ | ung thư học | in the field of oncology | oncology department/research |
| drug repurposing | n | /drʌɡ ˌriːˈpɜːpəsɪŋ/ | tái sử dụng thuốc | approach known as drug repurposing | drug repurposing strategy |
| toxicity testing | n | /tɒkˈsɪsəti ˈtestɪŋ/ | kiểm tra độc tính | traditional methods of toxicity testing | toxicity testing methods |
| personalized medicine | n | /ˈpɜːsənəlaɪzd ˈmedsn/ | y học cá nhân hóa | personalized medicine represents another frontier | personalized medicine approach |
| genomic sequences | n | /dʒiˈnəʊmɪk ˈsiːkwənsɪz/ | chuỗi gen | integrate genomic sequences | analyze genomic sequences |
| adverse reactions | n | /ədˈvɜːs riˈækʃnz/ | phản ứng phụ | predict adverse reactions | adverse reactions to drugs |
| regulatory agencies | n | /ˈreɡjələtəri ˈeɪdʒənsiz/ | cơ quan quản lý | regulatory agencies like the FDA | regulatory agencies approval |
| algorithmic transparency | n | /ˌælɡəˈrɪðmɪk trænsˈpærənsi/ | tính minh bạch thuật toán | questions about algorithmic transparency | ensure algorithmic transparency |
Passage 3 – Essential Vocabulary
| Từ vựng | Loại từ | Phiên âm | Nghĩa tiếng Việt | Ví dụ từ bài | Collocation |
|---|---|---|---|---|---|
| proliferation | n | /prəˌlɪfəˈreɪʃn/ | sự lan rộng, gia tăng | proliferation of AI technologies | proliferation of technology/weapons |
| unprecedented | adj | /ʌnˈpresɪdentɪd/ | chưa từng có | unprecedented convergence | unprecedented situation/growth |
| ethical dilemmas | n | /ˈeθɪkl dɪˈleməz/ | tình huống khó xử về đạo đức | attendant ethical dilemmas | face ethical dilemmas |
| algorithmic accountability | n | /ˌælɡəˈrɪðmɪk əˌkaʊntəˈbɪləti/ | trách nhiệm giải trình thuật toán | questions about algorithmic accountability | ensure algorithmic accountability |
| epistemic justice | n | /ˌepɪˈstiːmɪk ˈdʒʌstɪs/ | công bằng tri thức | epistemic justice concerns | epistemic justice framework |
| algorithmic bias | n | /ˌælɡəˈrɪðmɪk ˈbaɪəs/ | thiên kiến thuật toán | concept of algorithmic bias | algorithmic bias in AI |
| exacerbate | v | /ɪɡˈzæsəbeɪt/ | làm trầm trọng thêm | exacerbate existing disparities | exacerbate the problem/situation |
| underrepresent | v | /ˌʌndəˌreprɪˈzent/ | thiếu đại diện | datasets underrepresent certain groups | underrepresent minorities |
| perpetuate | v | /pəˈpetʃueɪt/ | duy trì, kéo dài | perpetuate discriminatory patterns | perpetuate inequality/stereotypes |
| seminal study | n | /ˈsemɪnl ˈstʌdi/ | nghiên cứu có tính nền tảng | a seminal study published | seminal study/work |
| opacity | n | /əʊˈpæsəti/ | sự mờ đục, không minh bạch | opacity of AI systems | opacity of algorithms |
| inscrutable | adj | /ɪnˈskruːtəbl/ | khó hiểu, bí ẩn | remain fundamentally inscrutable | inscrutable expression/system |
| interpretability deficit | n | /ɪnˌtɜːprɪtəˈbɪləti ˈdefɪsɪt/ | thiếu hụt khả năng giải thích | interpretability deficit creates tension | address interpretability deficit |
| informed consent | n | /ɪnˈfɔːmd kənˈsent/ | sự đồng ý có hiểu biết | principle of informed consent | obtain informed consent |
| commodification | n | /kəˌmɒdɪfɪˈkeɪʃn/ | sự thương mại hóa | commodification of health data | commodification of data/resources |
| deskilling | n | /diːˈskɪlɪŋ/ | mất kỹ năng nghề nghiệp | trajectory toward deskilling | deskilling of workers/professionals |
| epistemological status | n | /ɪˌpɪstɪməˈlɒdʒɪkl ˈsteɪtəs/ | địa vị tri thức luận | epistemological status of knowledge | epistemological status/framework |
| technological colonialism | n | /ˌteknəˈlɒdʒɪkl kəˈləʊniəlɪzəm/ | chủ nghĩa thực dân công nghệ | concerns about technological colonialism | technological colonialism impact |
Học viên đang luyện tập IELTS Reading với chủ đề trí tuệ nhân tạo trong y tế
Kết bài
Chủ đề “How is artificial intelligence transforming the healthcare industry?” không chỉ phản ánh xu hướng công nghệ đương đại mà còn thường xuyên xuất hiện trong các đề thi IELTS Reading gần đây. Thông qua bộ đề thi mẫu hoàn chỉnh này, bạn đã được trải nghiệm 3 passages với độ khó tăng dần từ Easy đến Hard, bao gồm tổng cộng 40 câu hỏi đa dạng dạng giống thi thật 100%.
Passage 1 giới thiệu những ứng dụng cơ bản của AI trong chẩn đoán y khoa với ngôn ngữ dễ tiếp cận, giúp bạn làm quen với chủ đề và xây dựng tự tin. Passage 2 đi sâu vào phát triển thuốc và y học cá nhân hóa với từ vựng học thuật phong phú hơn, thử thách kỹ năng paraphrase và suy luận của bạn. Passage 3 phân tích các vấn đề đạo đức phức tạp với cấu trúc ngữ pháp tinh vi, yêu cầu khả năng đọc hiểu ở mức độ cao nhất.
Phần đáp án chi tiết không chỉ cung cấp câu trả lời đúng mà còn giải thích rõ ràng vị trí thông tin, kỹ thuật paraphrase, và chiến lược tiếp cận từng dạng câu hỏi. Danh sách từ vựng được phân loại theo passage với phiên âm, nghĩa, ví dụ và collocations sẽ giúp bạn mở rộng vốn từ học thuật một cách có hệ thống.
Hãy sử dụng đề thi này như một công cụ tự đánh giá và luyện tập thực chiến. Đặt thời gian 60 phút để làm bài trong điều kiện thi thật, sau đó đối chiếu đáp án và phân tích kỹ những câu sai để cải thiện. Với sự luyện tập kiên trì và phương pháp đúng đắn, bạn hoàn toàn có thể chinh phục band điểm IELTS Reading mục tiêu của mình. Chúc bạn ôn thi hiệu quả và đạt kết quả cao!
