IELTS Reading: AI trong Giải Pháp Y Tế Cá Nhân Hóa – Đề Thi Mẫu Có Đáp Án Chi Tiết

Mở bài

Trí tuệ nhân tạo (AI) trong lĩnh vực y tế cá nhân hóa đang trở thành một chủ đề nóng hổi và xuất hiện ngày càng nhiều trong các đề thi IELTS Reading gần đây. Chủ đề này không chỉ phản ánh xu hướng công nghệ toàn cầu mà còn đòi hỏi người học phải nắm vững vốn từ vựng chuyên ngành về y tế, công nghệ và khoa học dữ liệu.

Trong bài viết này, bạn sẽ nhận được một bộ đề thi IELTS Reading hoàn chỉnh gồm 3 passages với độ khó tăng dần từ Easy đến Hard. Đề thi được thiết kế dựa trên cấu trúc chuẩn của Cambridge IELTS, bao gồm đầy đủ 40 câu hỏi với các dạng bài đa dạng như Multiple Choice, True/False/Not Given, Matching Headings, và Summary Completion. Bạn cũng sẽ được cung cấp đáp án chi tiết kèm giải thích, vị trí thông tin trong bài, và phân tích kỹ thuật paraphrase – yếu tố then chốt để đạt band điểm cao.

Ngoài ra, bài viết còn tổng hợp hệ thống từ vựng quan trọng theo từng passage, giúp bạn vừa luyện kỹ năng đọc hiểu vừa mở rộng vốn từ học thuật. Đề thi này phù hợp cho học viên từ band 5.0 trở lên, đặc biệt hữu ích cho những ai đang nhắm đến band 7.0-8.0.

1. Hướng Dẫn Làm Bài IELTS Reading

Tổng Quan Về IELTS Reading Test

IELTS Reading Test là bài kiểm tra kéo dài 60 phút với 3 passages và tổng cộng 40 câu hỏi. Mỗi câu trả lời đúng được tính là 1 điểm, và tổng điểm sẽ được quy đổi thành band score từ 1-9.

Phân bổ thời gian khuyến nghị:

• Passage 1: 15-17 phút (độ khó thấp nhất)
• Passage 2: 18-20 phút (độ khó trung bình)
• Passage 3: 23-25 phút (độ khó cao nhất)

Lưu ý rằng không có thời gian riêng để chép đáp án sang phiếu trả lời, vì vậy bạn cần quản lý thời gian thật tốt và ghi đáp án trực tiếp lên answer sheet trong 60 phút.

Các Dạng Câu Hỏi Trong Đề Này

Đề thi mẫu này bao gồm 7 dạng câu hỏi phổ biến nhất trong IELTS Reading:

1. Multiple Choice – Chọn đáp án đúng từ các phương án cho sẵn
2. True/False/Not Given – Xác định thông tin đúng, sai hay không được đề cập
3. Matching Information – Nối thông tin với đoạn văn tương ứng
4. Sentence Completion – Hoàn thành câu với từ trong bài
5. Matching Headings – Chọn tiêu đề phù hợp cho mỗi đoạn
6. Summary Completion – Điền từ vào đoạn tóm tắt
7. Short-answer Questions – Trả lời câu hỏi ngắn

2. IELTS Reading Practice Test

PASSAGE 1 – The Dawn of Personalized Medicine Through AI

Độ khó: Easy (Band 5.0-6.5)

Thời gian đề xuất: 15-17 phút

The healthcare industry is undergoing a revolutionary transformation thanks to artificial intelligence (AI). For centuries, medical treatment has followed a one-size-fits-all approach, where doctors prescribed the same medications and treatments to patients with similar symptoms. However, this traditional method often failed to account for individual differences in genetics, lifestyle, and environmental factors. Today, AI is changing this paradigm by enabling truly personalized healthcare solutions that cater to each patient’s unique needs.

At its core, personalized medicine uses AI algorithms to analyze vast amounts of patient data, including medical records, genetic information, lifestyle habits, and even social determinants of health. These sophisticated systems can identify patterns that human doctors might miss, leading to more accurate diagnoses and more effective treatment plans. For instance, AI can predict which patients are at higher risk of developing certain diseases based on their genetic markers and recommend preventive measures accordingly.

One of the most promising applications of AI in personalized healthcare is in oncology, the study and treatment of cancer. Cancer is notoriously difficult to treat because each tumor is genetically unique. AI systems can analyze a patient’s tumor DNA and compare it against massive databases of cancer genomes to identify the most effective treatment options. This approach, known as precision oncology, has already shown remarkable success in matching patients with targeted therapies that specifically attack their cancer’s unique characteristics.

Machine learning algorithms are also revolutionizing drug discovery and development. Traditionally, developing a new drug could take up to 15 years and cost billions of dollars. AI can dramatically accelerate this process by predicting how different molecular compounds will interact with specific disease targets. This not only speeds up the discovery of new medications but also helps identify existing drugs that might be repurposed for new treatments, a practice that has become increasingly important during health emergencies.

Another area where AI excels is in continuous health monitoring. Wearable devices equipped with AI can track vital signs like heart rate, blood pressure, and blood glucose levels in real-time. When these systems detect anomalies or concerning patterns, they can alert both patients and healthcare providers immediately. This proactive approach to healthcare allows for early intervention before minor issues develop into serious health problems. For patients with chronic conditions like diabetes or heart disease, this technology can be life-changing.

Diagnostic imaging has also benefited tremendously from AI integration. AI-powered systems can analyze X-rays, MRI scans, and CT scans with remarkable accuracy, often detecting subtle abnormalities that human radiologists might overlook. Some studies have shown that AI can match or even exceed human performance in identifying certain conditions, such as diabetic retinopathy in eye scans or early-stage lung cancer in chest X-rays. However, experts emphasize that AI should complement rather than replace human medical professionals, combining the machine’s analytical power with human judgment and empathy.

Despite these impressive advances, the implementation of AI in personalized healthcare faces several challenges. Data privacy remains a major concern, as these systems require access to sensitive personal health information. There are also questions about algorithmic bias – if AI systems are trained primarily on data from certain populations, they may not work as effectively for underrepresented groups. Additionally, the high cost of developing and implementing these technologies could potentially exacerbate health inequalities if only wealthy patients and institutions can afford them.

Nevertheless, the future of AI-driven personalized healthcare looks promising. As technology continues to advance and become more accessible, more patients will benefit from treatments specifically designed for their individual circumstances. The key will be ensuring that these innovations are developed and deployed in ways that are ethical, equitable, and truly serve the needs of all patients, regardless of their background or economic status.

Questions 1-5: Multiple Choice

Choose the correct letter, A, B, C, or D.

1. According to the passage, what was the main problem with traditional medical treatment?
   A. It was too expensive for most patients
   B. It used the same approach for all patients with similar symptoms
   C. It relied too heavily on technology
   D. It required too much time to be effective

2. AI systems in personalized medicine primarily analyze:
   A. only genetic information
   B. only lifestyle habits
   C. various types of patient data
   D. social media activity

3. What makes cancer treatment particularly challenging?
   A. The high cost of medications
   B. The lack of qualified doctors
   C. Each tumor has unique genetic characteristics
   D. Patients often refuse treatment

4. How does AI help in drug discovery?
   A. By completely replacing human researchers
   B. By predicting molecular interactions
   C. By testing drugs on animals
   D. By reducing the need for clinical trials

5. According to the passage, what is the recommended role of AI in diagnostic imaging?
   A. To completely replace human radiologists
   B. To work alongside human medical professionals
   C. To only be used in emergency situations
   D. To focus solely on rare diseases

Questions 6-10: 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. Personalized medicine has been used in healthcare for centuries.
7. AI can identify disease risks based on genetic markers.
8. Precision oncology has a 100% success rate in treating cancer.
9. Wearable devices with AI can monitor health conditions continuously.
10. All countries have equal access to AI healthcare technology.

Questions 11-13: Sentence Completion

Complete the sentences below.

Choose NO MORE THAN TWO WORDS from the passage for each answer.

11. One major concern about AI in healthcare is __ __, as systems need access to sensitive information.
12. If AI is trained on limited populations, it may suffer from __ __.
13. The passage suggests that AI innovations should be developed in ways that are ethical, equitable, and serve patients of all __ __.

---

PASSAGE 2 – AI-Driven Diagnostic Systems and Treatment Optimization

Độ khó: Medium (Band 6.0-7.5)

Thời gian đề xuất: 18-20 phút

The integration of artificial intelligence into healthcare diagnostics represents one of the most transformative developments in modern medicine. Unlike previous technological advances that merely improved existing processes, AI-driven diagnostic systems are fundamentally reshaping how medical professionals approach disease detection, treatment planning, and patient care management. These systems leverage deep learning neural networks – computational models inspired by the human brain’s structure – to process and interpret medical data with unprecedented speed and accuracy.

A. The foundation of AI diagnostic systems lies in their ability to learn from enormous datasets. Convolutional neural networks (CNNs), a specialized type of deep learning algorithm, have proven particularly effective in analyzing medical images. These networks are trained on millions of labeled medical images, gradually learning to recognize patterns associated with various conditions. What distinguishes modern AI from earlier computer-aided detection (CAD) systems is the depth of analysis. While traditional CAD systems followed pre-programmed rules, contemporary AI systems develop their own feature recognition strategies through exposure to training data, often identifying diagnostic indicators that were previously unknown to medical science.

B. The application of AI in radiology exemplifies this technology’s potential. Consider the challenge of detecting early-stage lung cancer in chest CT scans. Pulmonary nodules – small growths in the lungs – can indicate cancer, but they are notoriously difficult to spot, particularly when they are less than three millimeters in diameter. AI systems can analyze these scans slice by slice, comparing each image against their learned database of both benign and malignant nodules. Research published in leading medical journals has demonstrated that these AI systems can reduce false-positive rates by up to 5% while simultaneously improving cancer detection rates, potentially saving thousands of lives annually.

C. Beyond imaging, AI is revolutionizing pathology – the study of disease through examination of tissues and body fluids. Digital pathology involves scanning glass slides to create high-resolution digital images that AI can analyze. These systems can perform quantitative analysis of cellular features with meticulous precision, measuring characteristics like cell size, shape, and staining intensity that might vary too subtly for human perception. In breast cancer diagnosis, for instance, AI can assess the expression levels of specific biomarkers such as HER2 and estrogen receptors, providing quantifiable data that helps oncologists select the most appropriate targeted therapies.

D. The concept of treatment optimization through AI extends beyond diagnosis to encompass the entire therapeutic journey. Clinical decision support systems (CDSS) powered by AI can analyze a patient’s complete medical history, current symptoms, genetic profile, and even pharmacogenomic data – information about how their genes affect drug response – to recommend optimal treatment protocols. These systems consider factors that would be virtually impossible for any individual physician to process comprehensively, such as potential drug interactions, contraindications based on comorbidities, and the statistical likelihood of treatment success based on similar patient cases.

E. Predictive analytics represents another frontier in AI-driven personalized healthcare. By analyzing patterns in patient data over time, AI systems can forecast disease progression and anticipate potential complications before they manifest clinically. For patients with chronic conditions like congestive heart failure, AI algorithms can identify subtle changes in symptoms, medication adherence, and physiological parameters that suggest an impending health crisis. This enables preemptive interventions – adjusting medications, scheduling urgent appointments, or even arranging hospitalization – that can prevent emergencies and improve long-term outcomes.

F. However, the implementation of AI diagnostic systems raises important questions about clinical validation and regulatory oversight. Medical AI systems must undergo rigorous testing to ensure they perform consistently across diverse patient populations and clinical settings. The U.S. Food and Drug Administration (FDA) and similar regulatory bodies worldwide have established frameworks for evaluating AI medical devices, but the rapidly evolving nature of these technologies presents ongoing challenges. Unlike traditional medical devices that remain static after approval, AI systems can continue learning and evolving, potentially changing their performance characteristics over time – a phenomenon that requires new regulatory approaches.

G. Furthermore, the interpretability of AI decisions remains a significant concern. Many advanced AI systems function as “black boxes,” producing accurate recommendations without providing clear explanations of their reasoning process. This lack of transparency can be problematic in medical contexts, where understanding the rationale behind a diagnosis or treatment recommendation is essential for both clinical decision-making and patient communication. Researchers are actively working on explainable AI techniques that can provide insights into how these systems reach their conclusions, but this remains an active area of development.

The successful integration of AI into personalized healthcare requires not only technological advancement but also changes in medical education, clinical workflows, and the physician-patient relationship. As these systems become more sophisticated and prevalent, healthcare providers must develop new skills in interpreting AI recommendations and communicating their implications to patients. The goal is not to replace human medical expertise but to augment it, creating a collaborative relationship between human clinicians and AI systems that leverages the strengths of both.

Questions 14-19: Matching Headings

The passage has seven paragraphs, A-G. Choose the correct heading for each paragraph from the list of headings below.

List of Headings:
i. The challenge of understanding AI decision-making processes
ii. How AI learns from medical image databases
iii. Predicting future health problems before they occur
iv. AI’s role in analyzing tissue samples and body fluids
v. The need for new approaches to testing AI medical systems
vi. Improving cancer detection in lung scans
vii. Using AI to recommend personalized treatment plans
viii. The limitations of traditional computer systems in healthcare
ix. The importance of training doctors to work with AI

14. Paragraph A
15. Paragraph B
16. Paragraph C
17. Paragraph D
18. Paragraph E
19. Paragraph F

Questions 20-23: 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

20. Modern AI systems are more advanced than earlier computer-aided detection systems because they create their own pattern recognition methods.
21. AI diagnostic systems are currently more expensive than traditional diagnostic methods.
22. The FDA has successfully solved all regulatory challenges related to AI medical devices.
23. Doctors need to develop new competencies to work effectively with AI systems.

Questions 24-26: Summary Completion

Complete the summary below. Choose NO MORE THAN TWO WORDS from the passage for each answer.

AI systems in radiology can analyze medical scans in great detail, examining them (24) __ __ __. When detecting lung cancer, these systems compare images with databases containing both harmless and cancerous growths. Studies show that AI can decrease (25) __ __ while improving detection accuracy. In pathology, AI performs (26) __ __ of cellular characteristics with extreme precision, helping doctors choose appropriate treatments.

---

PASSAGE 3 – Ethical Frameworks and Sociotechnical Considerations in AI-Mediated Healthcare Personalization

Độ khó: Hard (Band 7.0-9.0)

Thời gian đề xuất: 23-25 phút

The proliferation of artificial intelligence in personalized healthcare has precipitated a paradigm shift that extends far beyond mere technological innovation, encompassing profound ethical, epistemological, and sociotechnical dimensions that demand rigorous interdisciplinary scrutiny. While the technical capabilities of AI systems continue to advance at an exponential pace, the normative frameworks necessary to govern their development and deployment have lagged considerably behind, creating what scholars term a “pacing problem” – the temporal gap between technological change and the institutional adaptations required to manage its implications. This disjuncture raises fundamental questions about algorithmic governance, epistemic authority in medical decision-making, and the potential reconfiguration of the physician-patient relationship in an era of increasingly automated diagnostics.

The concept of algorithmic fairness in personalized healthcare AI systems represents a particularly intractable challenge that intersects considerations of distributive justice, representational equity, and outcome parity. Machine learning models, by their nature, reflect and potentially amplify the systematic biases embedded in their training data. Historical healthcare data inevitably incorporates the effects of past and present structural inequalities – including racial disparities in treatment access, gender bias in pain management, and socioeconomic stratification in health outcomes. When AI systems are trained on such data, they risk perpetuating and entrenching these inequities under the veneer of algorithmic objectivity. For instance, a widely publicized case revealed that a commercial healthcare algorithm used by hospitals across the United States systematically underestimated the medical needs of Black patients because it used healthcare expenditure as a proxy for health needs – failing to account for the fact that, due to systemic barriers, Black patients typically receive less care than equally sick white patients, resulting in lower spending that the algorithm misinterpreted as indicating lesser need.

Addressing these algorithmic biases requires more than technical fixes; it necessitates reflexive engagement with the social contexts in which these systems operate. Scholars advocate for participatory design approaches that incorporate diverse stakeholder perspectives, including patients from marginalized communities, throughout the AI development lifecycle. This represents a fundamental departure from traditional technocentric paradigms that prioritize optimization of narrowly defined performance metrics. However, implementing such inclusive approaches presents significant practical challenges, including determining which stakeholders should be involved, how to adjudicate conflicting values and priorities, and how to ensure that participation translates into meaningful influence over system design rather than serving merely as tokenistic consultation.

The epistemological implications of AI-mediated diagnosis and treatment recommendations constitute another critical domain of inquiry. Medical knowledge has traditionally been characterized by a complex interplay between empirical observation, theoretical understanding, and clinical judgment – what medical anthropologists call “clinical reasoning.” AI systems, particularly those employing deep learning techniques, generate medical insights through probabilistic pattern recognition rather than causal reasoning, potentially disrupting established modes of medical knowing. When an AI system recommends a particular treatment based on correlational patterns identified across millions of patient cases, without providing a mechanistic explanation of why that treatment should be effective, it challenges traditional standards of medical evidence.

This shift toward what some philosophers of science call “model-based medicine” raises questions about the nature of medical expertise and authority. If AI systems can achieve superior diagnostic accuracy without understanding the underlying biological mechanisms, does such mechanistic understanding remain necessary? Critics argue that correlation without explanation is insufficient for medical decision-making, particularly when treating individual patients whose circumstances may deviate from the population patterns encoded in AI models. They contend that understanding pathophysiological mechanisms remains essential for several reasons: it enables clinicians to extrapolate to novel situations not represented in training data, to identify when algorithmic recommendations might be inappropriate for specific patients, and to explain treatment rationales to patients in ways that support informed consent and therapeutic alliance.

The data governance challenges inherent in personalized healthcare AI systems extend to questions of ownership, control, and consent regarding personal health information. The effectiveness of these systems depends fundamentally on access to large, comprehensive datasets encompassing diverse populations. However, the collection, storage, and utilization of such data raise pressing concerns about informational privacy and data sovereignty. Traditional models of informed consent, developed for discrete clinical interventions, prove inadequate for contexts where patient data may be used in ways that cannot be fully specified at the time of collection. Moreover, the potential for de-identification techniques to be reversed through re-identification attacks – particularly when health data is combined with other data sources – complicates efforts to protect patient anonymity while enabling valuable research.

Some scholars propose data trusts or data cooperatives as alternative governance models that would allow individuals to collectively negotiate terms for the use of their health data, potentially ensuring that the benefits of AI-driven medical advances are more equitably distributed. These approaches draw on concepts from collective bargaining and common-pool resource management, treating health data as a shared resource that generates value through aggregation. However, implementing such models faces considerable legal, technical, and organizational hurdles, and questions remain about how to balance individual autonomy with collective governance, particularly regarding individuals who might wish to opt out of data sharing or who hold minority positions within data collectives.

The commodification of personalized healthcare through AI-driven services raises additional equity concerns. As direct-to-consumer genetic testing companies and wellness applications proliferate, they create a two-tiered system where affluent individuals can access increasingly sophisticated personalized health insights and interventions, while those lacking financial resources or digital literacy are excluded. This dynamic risks exacerbating health disparities in ways that are particularly insidious because they operate through ostensibly neutral market mechanisms rather than explicit exclusion. Furthermore, the consumerization of healthcare may subtly shift responsibility for health maintenance from collective social institutions to individuals, potentially eroding support for public health infrastructure and socialized medicine.

Looking forward, the governance architectures developed to manage AI in personalized healthcare will likely have ramifications extending well beyond the medical domain, serving as precedents for how societies navigate the integration of AI into other spheres involving high-stakes decisions affecting human welfare. The challenge lies not merely in optimizing algorithms or securing data, but in ensuring that the profound transformation of healthcare through AI proceeds in ways that advance rather than undermine fundamental values of human dignity, equity, and democratic governance. This requires sustained dialogue among technologists, clinicians, ethicists, policymakers, and communities most affected by these systems, guided by the recognition that technical solutions alone cannot address what are fundamentally normative questions about the kind of healthcare system – and by extension, the kind of society – we wish to create.

Questions 27-31: Multiple Choice

Choose the correct letter, A, B, C, or D.

27. According to the passage, the “pacing problem” refers to:
    A. the speed at which AI systems process medical data
    B. the gap between technological advancement and regulatory development
    C. the time required to train AI algorithms
    D. the difference in healthcare access between rich and poor countries

28. The commercial healthcare algorithm mentioned in paragraph 2 failed because it:
    A. was not trained on sufficient data
    B. used spending as an indicator of medical need
    C. was only tested on white patients
    D. could not process complex medical information

29. What do critics say about correlation-based AI medical recommendations?
    A. They are always more accurate than human diagnoses
    B. They are too expensive to implement widely
    C. They lack causal explanations necessary for proper treatment
    D. They work better for rare diseases than common ones

30. Traditional informed consent models are inadequate for AI healthcare because:
    A. patients cannot read the technical documentation
    B. future uses of data cannot be fully predicted at collection time
    C. AI systems change too quickly
    D. doctors do not understand how AI works

31. The passage suggests that AI-driven personalized healthcare services may:
    A. eliminate all health disparities
    B. be rejected by most patients
    C. worsen inequality through market-based access
    D. replace traditional hospitals entirely

Questions 32-36: Matching Features

Match each concept (32-36) with the correct description (A-H).

Concepts:
32. Participatory design
33. Data trusts
34. Clinical reasoning
35. Re-identification attacks
36. Algorithmic governance

Descriptions:
A. Methods that can reverse anonymization of patient data
B. Traditional medical decision-making combining observation and judgment
C. Systems for collective negotiation of health data usage
D. AI systems that explain their recommendations clearly
E. Involving diverse stakeholders in AI system development
F. Regulations and frameworks for managing AI systems
G. Techniques for improving AI processing speed
H. Insurance policies for AI medical errors

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 biases might AI systems amplify when trained on historical healthcare data?
38. What do medical anthropologists call the traditional method of medical decision-making?
39. What concept treats health data as a shared resource that gains value when combined?
40. According to the passage, what fundamental values should AI healthcare transformation advance?

---

3. Answer Keys – Đáp Án

PASSAGE 1: Questions 1-13

1. B
2. C
3. C
4. B
5. B
6. FALSE
7. TRUE
8. NOT GIVEN
9. TRUE
10. NOT GIVEN
11. data privacy
12. algorithmic bias
13. economic status (hoặc background)

PASSAGE 2: Questions 14-26

14. ii
15. vi
16. iv
17. vii
18. iii
19. v
20. YES
21. NOT GIVEN
22. NO
23. YES
24. slice by slice
25. false-positive rates
26. quantitative analysis

PASSAGE 3: Questions 27-40

27. B
28. B
29. C
30. B
31. C
32. E
33. C
34. B
35. A
36. F
37. systematic biases
38. clinical reasoning
39. common-pool resource (hoặc shared resource)
40. human dignity (hoặc equity, hoặc democratic governance – bất kỳ một trong ba đều được chấp nhận)

---

4. Giải Thích Đáp Án Chi Tiết

Passage 1 – Giải Thích

Câu 1: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: main problem, traditional medical treatment
• Vị trí trong bài: Đoạn 1, dòng 2-4
• Giải thích: Bài đọc nêu rõ “medical treatment has followed a one-size-fits-all approach, where doctors prescribed the same medications and treatments to patients with similar symptoms” – điều này được paraphrase thành đáp án B “used the same approach for all patients with similar symptoms”.

Câu 2: C

• Dạng câu hỏi: Multiple Choice
• Từ khóa: AI systems, personalized medicine, analyze
• Vị trí trong bài: Đoạn 2, dòng 1-3
• Giải thích: Đoạn 2 liệt kê “vast amounts of patient data, including medical records, genetic information, lifestyle habits, and even social determinants of health” – đây là “various types of patient data” như đáp án C.

Câu 3: C

• Dạng câu hỏi: Multiple Choice
• Từ khóa: cancer treatment, challenging
• Vị trí trong bài: Đoạn 3, dòng 2-3
• Giải thích: Bài viết nói “Cancer is notoriously difficult to treat because each tumor is genetically unique” – từ “genetically unique” được paraphrase thành “unique genetic characteristics” ở đáp án C.

Câu 4: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: AI, drug discovery
• Vị trí trong bài: Đoạn 4, dòng 3-5
• Giải thích: Đoạn văn mô tả “AI can dramatically accelerate this process by predicting how different molecular compounds will interact with specific disease targets” – tương ứng với đáp án B.

Câu 5: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: recommended role, AI, diagnostic imaging
• Vị trí trong bài: Đoạn 6, dòng cuối
• Giải thích: Câu “experts emphasize that AI should complement rather than replace human medical professionals” chỉ rõ AI nên làm việc cùng với chuyên gia y tế, không thay thế – đây chính là đáp án B.

Câu 6: FALSE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: personalized medicine, centuries
• Vị trí trong bài: Đoạn 1
• Giải thích: Bài viết nói “For centuries, medical treatment has followed a one-size-fits-all approach” – điều này trái ngược với personalized medicine, do đó câu trả lời là FALSE.

Câu 7: TRUE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: AI, disease risks, genetic markers
• Vị trí trong bài: Đoạn 2, dòng 5-7
• Giải thích: Bài đọc khẳng định “AI can predict which patients are at higher risk of developing certain diseases based on their genetic markers” – khớp hoàn toàn với câu hỏi.

Câu 8: NOT GIVEN

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: precision oncology, 100% success rate
• Vị trí trong bài: Đoạn 3
• Giải thích: Bài viết chỉ nói precision oncology “has already shown remarkable success” nhưng không đề cập đến tỷ lệ thành công cụ thể 100%, do đó là NOT GIVEN.

Câu 9: TRUE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: wearable devices, AI, monitor, continuously
• Vị trí trong bài: Đoạn 5, dòng 1-2
• Giải thích: “Wearable devices equipped with AI can track vital signs… in real-time” và “continuous health monitoring” trong tiêu đề đoạn xác nhận thông tin này là đúng.

Câu 10: NOT GIVEN

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: all countries, equal access, AI healthcare
• Vị trí trong bài: Đoạn 7
• Giải thích: Bài viết đề cập đến “health inequalities” và việc công nghệ có thể tốn kém, nhưng không nói rõ về việc tất cả các quốc gia có truy cập ngang nhau hay không.

Câu 11: data privacy

• Dạng câu hỏi: Sentence Completion
• Từ khóa: major concern, sensitive information
• Vị trí trong bài: Đoạn 7, dòng 2
• Giải thích: “Data privacy remains a major concern, as these systems require access to sensitive personal health information.”

Câu 12: algorithmic bias

• Dạng câu hỏi: Sentence Completion
• Từ khóa: trained on limited populations
• Vị trí trong bài: Đoạn 7, dòng 3-4
• Giải thích: “There are also questions about algorithmic bias – if AI systems are trained primarily on data from certain populations…”

Câu 13: economic status / background

• Dạng câu hỏi: Sentence Completion
• Từ khóa: ethical, equitable, serve patients
• Vị trí trong bài: Đoạn 8, dòng cuối
• Giải thích: “…truly serve the needs of all patients, regardless of their background or economic status.” Cả hai từ đều được chấp nhận.

Trí tuệ nhân tạo AI trong y tế cá nhân hóa với công nghệ tiên tiến

Passage 2 – Giải Thích

Câu 14: ii (Paragraph A)

• Dạng câu hỏi: Matching Headings
• Giải thích: Đoạn A tập trung vào việc mô tả cách AI systems học từ “enormous datasets” và “millions of labeled medical images” – khớp với heading “How AI learns from medical image databases”.

Câu 15: vi (Paragraph B)

• Dạng câu hỏi: Matching Headings
• Giải thích: Đoạn B đề cập cụ thể đến “detecting early-stage lung cancer in chest CT scans” và cách AI cải thiện tỷ lệ phát hiện – tương ứng với heading “Improving cancer detection in lung scans”.

Câu 16: iv (Paragraph C)

• Dạng câu hỏi: Matching Headings
• Giải thích: Đoạn C nói về “pathology – the study of disease through examination of tissues and body fluids” và digital pathology – phù hợp với heading “AI’s role in analyzing tissue samples and body fluids”.

Câu 17: vii (Paragraph D)

• Dạng câu hỏi: Matching Headings
• Giải thích: Đoạn D thảo luận về “Clinical decision support systems” và cách AI “recommend optimal treatment protocols” – khớp với “Using AI to recommend personalized treatment plans”.

Câu 18: iii (Paragraph E)

• Dạng câu hỏi: Matching Headings
• Giải thích: Đoạn E tập trung vào “Predictive analytics” và “forecast disease progression” trước khi chúng “manifest clinically” – đây chính là “Predicting future health problems before they occur”.

Câu 19: v (Paragraph F)

• Dạng câu hỏi: Matching Headings
• Giải thích: Đoạn F nói về “clinical validation and regulatory oversight” và những thách thức trong việc đánh giá AI medical devices – tương ứng với “The need for new approaches to testing AI medical systems”.

Câu 20: YES

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: modern AI, more advanced, create pattern recognition methods
• Vị trí trong bài: Đoạn A, dòng 7-9
• Giải thích: Bài viết khẳng định “contemporary AI systems develop their own feature recognition strategies” khác với traditional CAD systems “followed pre-programmed rules” – điều này thể hiện quan điểm của tác giả rằng AI hiện đại tiên tiến hơn.

Câu 21: NOT GIVEN

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: AI diagnostic systems, more expensive
• Giải thích: Bài viết không so sánh chi phí giữa AI diagnostic systems và phương pháp truyền thống.

Câu 22: NO

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: FDA, solved all regulatory challenges
• Vị trí trong bài: Đoạn F, dòng 3-5
• Giải thích: Bài viết nói “the rapidly evolving nature of these technologies presents ongoing challenges” – điều này trái ngược với việc đã giải quyết tất cả thách thức.

Câu 23: YES

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: doctors, develop new competencies
• Vị trí trong bài: Đoạn cuối, dòng 2-3
• Giải thích: “Healthcare providers must develop new skills in interpreting AI recommendations” – tác giả rõ ràng đồng ý với quan điểm này.

Câu 24: slice by slice

• Dạng câu hỏi: Summary Completion
• Từ khóa: analyze medical scans
• Vị trí trong bài: Đoạn B, dòng 4-5
• Giải thích: “AI systems can analyze these scans slice by slice, comparing each image…”

Câu 25: false-positive rates

• Dạng câu hỏi: Summary Completion
• Từ khóa: AI can decrease
• Vị trí trong bài: Đoạn B, dòng 7-8
• Giải thích: “These AI systems can reduce false-positive rates by up to 5%…”

Câu 26: quantitative analysis

• Dạng câu hỏi: Summary Completion
• Từ khóa: pathology, AI performs
• Vị trí trong bài: Đoạn C, dòng 3-4
• Giải thích: “These systems can perform quantitative analysis of cellular features with meticulous precision…”

Passage 3 – Giải Thích

Câu 27: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: pacing problem
• Vị trí trong bài: Đoạn 1, dòng 3-5
• Giải thích: Bài viết định nghĩa “pacing problem” là “the temporal gap between technological change and the institutional adaptations required to manage its implications” – tức là khoảng cách giữa sự phát triển công nghệ và quy định.

Câu 28: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: commercial healthcare algorithm, failed
• Vị trí trong bài: Đoạn 2, dòng 6-10
• Giải thích: Algorithm “systematically underestimated the medical needs of Black patients because it used healthcare expenditure as a proxy for health needs” – sử dụng chi tiêu làm chỉ báo nhu cầu y tế.

Câu 29: C

• Dạng câu hỏi: Multiple Choice
• Từ khóa: critics, correlation-based AI
• Vị trí trong bài: Đoạn 5, dòng 4-7
• Giải thích: Critics argue “correlation without explanation is insufficient for medical decision-making” và understanding mechanisms “remains essential” – tức là thiếu giải thích nhân quả cần thiết.

Câu 30: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: traditional informed consent, inadequate
• Vị trí trong bài: Đoạn 6, dòng 4-6
• Giải thích: “Traditional models of informed consent… prove inadequate for contexts where patient data may be used in ways that cannot be fully specified at the time of collection.”

Câu 31: C

• Dạng câu hỏi: Multiple Choice
• Từ khóa: AI-driven personalized healthcare, may
• Vị trí trong bài: Đoạn 8, dòng 2-5
• Giải thích: Đoạn văn nói về “two-tiered system” nơi người giàu có thể tiếp cận dịch vụ trong khi người nghèo bị loại trừ, “exacerbating health disparities” – tức là làm trầm trọng thêm bất bình đẳng thông qua cơ chế thị trường.

Câu 32: E

• Dạng câu hỏi: Matching Features
• Vị trí trong bài: Đoạn 3, dòng 3-5
• Giải thích: “Participatory design approaches that incorporate diverse stakeholder perspectives… throughout the AI development lifecycle.”

Câu 33: C

• Dạng câu hỏi: Matching Features
• Vị trí trong bài: Đoạn 7, dòng 1-3
• Giải thích: “Data trusts or data cooperatives as alternative governance models that would allow individuals to collectively negotiate terms for the use of their health data.”

Câu 34: B

• Dạng câu hỏi: Matching Features
• Vị trí trong bài: Đoạn 4, dòng 2-4
• Giải thích: “Medical knowledge has traditionally been characterized by a complex interplay between empirical observation, theoretical understanding, and clinical judgment.”

Câu 35: A

• Dạng câu hỏi: Matching Features
• Vị trí trong bài: Đoạn 6, dòng 6-8
• Giải thích: “The potential for de-identification techniques to be reversed through re-identification attacks.”

Câu 36: F

• Dạng câu hỏi: Matching Features
• Vị trí trong bài: Đoạn 1, dòng 5-7
• Giải thích: Algorithmic governance liên quan đến “normative frameworks necessary to govern their development and deployment.”

Câu 37: systematic biases

• Dạng câu hỏi: Short-answer Questions
• Từ khóa: AI systems, amplify, historical healthcare data
• Vị trí trong bài: Đoạn 2, dòng 2-4
• Giải thích: “Machine learning models… reflect and potentially amplify the systematic biases embedded in their training data.”

Câu 38: clinical reasoning

• Dạng câu hỏi: Short-answer Questions
• Từ khóa: medical anthropologists call
• Vị trí trong bài: Đoạn 4, dòng 3-4
• Giải thích: “What medical anthropologists call ‘clinical reasoning.'”

Câu 39: common-pool resource / shared resource

• Dạng câu hỏi: Short-answer Questions
• Từ khóa: health data, shared resource
• Vị trí trong bài: Đoạn 7, dòng 5-7
• Giải thích: “These approaches draw on concepts from… common-pool resource management, treating health data as a shared resource.”

Câu 40: human dignity / equity / democratic governance

• Dạng câu hỏi: Short-answer Questions
• Từ khóa: fundamental values, AI healthcare
• Vị trí trong bài: Đoạn 9, dòng 3-5
• Giải thích: “Ensuring that the profound transformation of healthcare through AI proceeds in ways that advance rather than undermine fundamental values of human dignity, equity, and democratic governance.” Một trong ba giá trị đều được chấp nhận.

---

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
revolutionary transformation	noun phrase	/ˌrevəˈluːʃənəri trænsˌfɔːˈmeɪʃən/	sự chuyển đổi mang tính cách mạng	“The healthcare industry is undergoing a revolutionary transformation”	undergo a revolutionary transformation
one-size-fits-all	adjective	/wʌn saɪz fɪts ɔːl/	đồng loạt, áp dụng chung	“medical treatment has followed a one-size-fits-all approach”	one-size-fits-all approach/solution
genetic markers	noun phrase	/dʒəˈnetɪk ˈmɑːkəz/	dấu ấn di truyền	“predict which patients are at higher risk… based on their genetic markers”	identify/analyze genetic markers
precision oncology	noun phrase	/prɪˈsɪʒən ɒŋˈkɒlədʒi/	ung thư học chính xác	“This approach, known as precision oncology”	precision oncology approach
machine learning	noun phrase	/məˈʃiːn ˈlɜːnɪŋ/	học máy	“Machine learning algorithms are also revolutionizing drug discovery”	machine learning algorithms/models
molecular compounds	noun phrase	/məˈlekjələ ˈkɒmpaʊndz/	hợp chất phân tử	“predicting how different molecular compounds will interact”	synthesize/analyze molecular compounds
continuous monitoring	noun phrase	/kənˈtɪnjuəs ˈmɒnɪtərɪŋ/	giám sát liên tục	“Another area where AI excels is in continuous health monitoring”	continuous monitoring system
anomalies	noun	/əˈnɒməliz/	bất thường, dị thường	“When these systems detect anomalies or concerning patterns”	detect/identify anomalies
proactive approach	noun phrase	/prəʊˈæktɪv əˈprəʊtʃ/	cách tiếp cận chủ động	“This proactive approach to healthcare allows for early intervention”	adopt/take a proactive approach
diagnostic imaging	noun phrase	/ˌdaɪəɡˈnɒstɪk ˈɪmɪdʒɪŋ/	chẩn đoán hình ảnh	“Diagnostic imaging has also benefited tremendously”	diagnostic imaging techniques
algorithmic bias	noun phrase	/ˌælɡəˈrɪðmɪk ˈbaɪəs/	thiên lệch thuật toán	“There are also questions about algorithmic bias”	address/mitigate algorithmic bias
exacerbate inequalities	verb phrase	/ɪɡˈzæsəbeɪt ˌɪnɪˈkwɒlətiz/	làm trầm trọng thêm sự bất bình đẳng	“could potentially exacerbate health inequalities”	exacerbate inequalities/disparities

Passage 2 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
transformative developments	noun phrase	/trænsˈfɔːmətɪv dɪˈveləpments/	những phát triển mang tính đột phá	“one of the most transformative developments in modern medicine”	transformative developments/changes
deep learning neural networks	noun phrase	/diːp ˈlɜːnɪŋ ˈnjʊərəl ˈnetwɜːks/	mạng nơ-ron học sâu	“leverage deep learning neural networks”	train deep learning neural networks
convolutional neural networks	noun phrase	/ˌkɒnvəˈluːʃənəl ˈnjʊərəl ˈnetwɜːks/	mạng nơ-ron tích chập	“Convolutional neural networks (CNNs)”	implement convolutional neural networks
feature recognition	noun phrase	/ˈfiːtʃə ˌrekəɡˈnɪʃən/	nhận dạng đặc trưng	“develop their own feature recognition strategies”	feature recognition system/algorithm
diagnostic indicators	noun phrase	/ˌdaɪəɡˈnɒstɪk ˈɪndɪkeɪtəz/	chỉ số chẩn đoán	“identifying diagnostic indicators”	identify/analyze diagnostic indicators
pulmonary nodules	noun phrase	/ˈpʌlmənəri ˈnɒdjuːlz/	nốt phổi	“Pulmonary nodules – small growths in the lungs”	detect/identify pulmonary nodules
false-positive rates	noun phrase	/fɔːls ˈpɒzətɪv reɪts/	tỷ lệ dương tính giả	“reduce false-positive rates by up to 5%”	reduce/lower false-positive rates
digital pathology	noun phrase	/ˈdɪdʒɪtəl pəˈθɒlədʒi/	giải phẫu bệnh số	“Digital pathology involves scanning glass slides”	digital pathology platform/system
quantitative analysis	noun phrase	/ˈkwɒntɪtətɪv əˈnæləsɪs/	phân tích định lượng	“perform quantitative analysis of cellular features”	conduct quantitative analysis
biomarkers	noun	/ˈbaɪəʊˌmɑːkəz/	dấu ấn sinh học	“assess the expression levels of specific biomarkers”	identify/measure biomarkers
clinical decision support systems	noun phrase	/ˈklɪnɪkəl dɪˈsɪʒən səˈpɔːt ˈsɪstəmz/	hệ thống hỗ trợ quyết định lâm sàng	“Clinical decision support systems (CDSS) powered by AI”	implement clinical decision support systems
pharmacogenomic data	noun phrase	/ˌfɑːməkəʊdʒɪˈnəʊmɪk ˈdeɪtə/	dữ liệu dược lý di truyền	“even pharmacogenomic data”	analyze pharmacogenomic data
predictive analytics	noun phrase	/prɪˈdɪktɪv ˌænəˈlɪtɪks/	phân tích dự đoán	“Predictive analytics represents another frontier”	use/apply predictive analytics
preemptive interventions	noun phrase	/priˈemptɪv ˌɪntəˈvenʃənz/	can thiệp phòng ngừa	“This enables preemptive interventions”	implement preemptive interventions
black boxes	noun phrase	/blæk ˈbɒksɪz/	hộp đen (không minh bạch)	“function as ‘black boxes'”	operate as black boxes

Passage 3 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
paradigm shift	noun phrase	/ˈpærədaɪm ʃɪft/	sự thay đổi mô hình/quan điểm	“precipitated a paradigm shift”	undergo/represent a paradigm shift
sociotechnical dimensions	noun phrase	/ˌsəʊsɪəʊˈteknɪkəl dɪˈmenʃənz/	các chiều kích xã hội-công nghệ	“encompassing profound… sociotechnical dimensions”	sociotechnical dimensions/aspects
normative frameworks	noun phrase	/ˈnɔːmətɪv ˈfreɪmwɜːks/	khung chuẩn mực	“the normative frameworks necessary to govern”	establish normative frameworks
pacing problem	noun phrase	/ˈpeɪsɪŋ ˈprɒbləm/	vấn đề về tốc độ thích ứng	“creating what scholars term a ‘pacing problem'”	address the pacing problem
epistemic authority	noun phrase	/ˌepɪˈstiːmɪk ɔːˈθɒrəti/	quyền lực tri thức	“questions about… epistemic authority in medical decision-making”	challenge epistemic authority
algorithmic fairness	noun phrase	/ˌælɡəˈrɪðmɪk ˈfeənəs/	sự công bằng thuật toán	“The concept of algorithmic fairness”	ensure algorithmic fairness
distributive justice	noun phrase	/dɪˈstrɪbjətɪv ˈdʒʌstɪs/	công lý phân phối	“intersects considerations of distributive justice”	principles of distributive justice
structural inequalities	noun phrase	/ˈstrʌktʃərəl ˌɪnɪˈkwɒlətiz/	bất bình đẳng cấu trúc	“the effects of past and present structural inequalities”	address structural inequalities
perpetuating	verb	/pəˈpetʃueɪtɪŋ/	duy trì, kéo dài	“risk perpetuating and entrenching these inequities”	perpetuating stereotypes/inequalities
participatory design	noun phrase	/pɑːˌtɪsɪpətəri dɪˈzaɪn/	thiết kế có sự tham gia	“Scholars advocate for participatory design approaches”	participatory design methods/approach
technocentric paradigms	noun phrase	/ˌteknəʊˈsentrɪk ˈpærədaɪmz/	mô hình lấy công nghệ làm trung tâm	“departure from traditional technocentric paradigms”	technocentric paradigms/approaches
epistemological implications	noun phrase	/ɪˌpɪstɪməˈlɒdʒɪkəl ˌɪmplɪˈkeɪʃənz/	hệ quả nhận thức luận	“The epistemological implications”	epistemological implications/questions
causal reasoning	noun phrase	/ˈkɔːzəl ˈriːzənɪŋ/	suy luận nhân quả	“through probabilistic pattern recognition rather than causal reasoning”	apply causal reasoning
mechanistic explanation	noun phrase	/ˌmekəˈnɪstɪk ˌekspləˈneɪʃən/	giải thích theo cơ chế	“without providing a mechanistic explanation”	provide mechanistic explanation
data governance	noun phrase	/ˈdeɪtə ˈɡʌvənəns/	quản trị dữ liệu	“The data governance challenges”	data governance framework/policies
informational privacy	noun phrase	/ˌɪnfəˈmeɪʃənəl ˈprɪvəsi/	quyền riêng tư thông tin	“concerns about informational privacy”	protect informational privacy
data sovereignty	noun phrase	/ˈdeɪtə ˈsɒvrɪnti/	chủ quyền dữ liệu	“questions of… data sovereignty”	data sovereignty rights/principles
commodification	noun	/kəˌmɒdɪfɪˈkeɪʃən/	hàng hóa hóa	“The commodification of personalized healthcare”	commodification of healthcare/data

Đề thi IELTS Reading về AI và y tế với câu hỏi đa dạng

Kết bài

Chủ đề “AI For Creating Personalized Healthcare Solutions” không chỉ là một trong những xu hướng nổi bật của thế kỷ 21 mà còn thể hiện sự giao thoa giữa công nghệ, y học và xã hội – những yếu tố thường xuất hiện trong các đề thi IELTS Reading gần đây. Điều này tương tự như The role of technology in global poverty reduction, nơi công nghệ đóng vai trò then chốt trong việc giải quyết các vấn đề toàn cầu.

Bộ đề thi mẫu này đã cung cấp cho bạn trải nghiệm hoàn chỉnh với 3 passages có độ khó tăng dần từ Easy (Band 5.0-6.5) đến Medium (Band 6.0-7.5) và Hard (Band 7.0-9.0). Passage 1 giới thiệu các khái niệm cơ bản về AI trong y tế cá nhân hóa, Passage 2 đi sâu vào các ứng dụng cụ thể như diagnostic imaging và clinical decision support, trong khi Passage 3 khám phá các vấn đề phức tạp về đạo đức và xã hội liên quan đến công nghệ này. Đối với những ai quan tâm đến The role of AI in improving productivity, việc hiểu cách AI được ứng dụng trong lĩnh vực y tế cũng mang lại nhiều bài học giá trị về cách tối ưu hóa hiệu suất trong các ngành khác.

40 câu hỏi được thiết kế theo đúng format Cambridge IELTS bao gồm 7 dạng câu hỏi phổ biến: Multiple Choice, True/False/Not Given, Yes/No/Not Given, Matching Headings, Sentence Completion, Summary Completion, Matching Features và Short-answer Questions. Mỗi câu hỏi đều có giải thích chi tiết về vị trí thông tin, kỹ thuật paraphrase và lý do tại sao đáp án đó là chính xác, giúp bạn không chỉ biết đáp án mà còn hiểu được phương pháp làm bài hiệu quả. Tương tự như How learning simulations are improving student outcomes, việc luyện tập với các đề thi mô phỏng chân thực sẽ giúp cải thiện đáng kể kết quả học tập của bạn.

Hệ thống từ vựng được tổng hợp theo từng passage với hơn 40 từ và cụm từ quan trọng, kèm phiên âm, nghĩa tiếng Việt, ví dụ thực tế và collocations phổ biến. Đây là tài nguyên quý giá giúp bạn mở rộng vốn từ học thuật, đặc biệt trong các lĩnh vực y tế, công nghệ và khoa học – những chủ đề thường xuyên xuất hiện trong IELTS Reading.

Để đạt kết quả tốt nhất, hãy luyện tập đề thi này trong điều kiện giống thực tế: giới hạn thời gian 60 phút, không tra từ điển, và tự chấm điểm theo đáp án. Sau đó, dành thời gian đọc kỹ phần giải thích để hiểu rõ những lỗi sai và cải thiện kỹ năng. Việc hiểu được tầm quan trọng của quản lý thời gian và sức khỏe cũng không kém phần quan trọng trong quá trình ôn thi, tương tự như những gì được đề cập trong Strategies for improving sleep quality, bởi một tinh thần tỉnh táo và sức khỏe tốt sẽ giúp bạn tập trung tốt hơn trong bài thi.

Hãy nhớ rằng, việc đạt band điểm cao trong IELTS Reading không chỉ phụ thuộc vào vốn từ vựng mà còn ở khả năng quản lý thời gian, nhận diện dạng câu hỏi, và kỹ thuật skimming-scanning hiệu quả. Cũng giống như Tips for achieving financial security đòi hỏi sự kiên nhẫn và chiến lược dài hạn, việc chuẩn bị cho kỳ thi IELTS cũng cần một lộ trình rõ ràng và nỗ lực không ngừng nghỉ.

Chúc bạn luyện tập hiệu quả và đạt được band điểm mục tiêu trong kỳ thi IELTS sắp tới. Hãy kiên trì thực hành mỗi ngày và tin tưởng vào khả năng của bản thân. Thành công sẽ đến với những ai biết chuẩn bị kỹ lưỡng và không bao giờ từ bỏ!