IELTS Reading: The Role of Artificial Intelligence in Healthcare – Đề Thi Mẫu Có Đáp Án Chi Tiết

Mở Bài

Trí tuệ nhân tạo (AI) trong y tế đang là một trong những chủ đề nóng hổi nhất trong kỳ thi IELTS Reading hiện nay. Chủ đề này kết hợp giữa công nghệ, y học và những vấn đề xã hội đương đại, xuất hiện thường xuyên trong các đề thi IELTS gần đây, đặc biệt từ Cambridge IELTS 15 trở đi. Với sự phát triển vượt bậc của công nghệ AI trong ngành chăm sóc sức khỏe, từ chẩn đoán bệnh, phát triển thuốc đến robot phẫu thuật, đây là chủ đề có tính thời sự cao và thường được giám khảo IELTS ưa chuộng.

Trong bài viết này, bạn sẽ nhận được một bộ đề thi IELTS Reading hoàn chỉnh với 3 passages được thiết kế theo đúng chuẩn Cambridge, tăng dần từ mức độ Easy (Band 5.0-6.5) đến Medium (Band 6.0-7.5) và Hard (Band 7.0-9.0). Mỗi passage đi kèm với các dạng câu hỏi đa dạng giống thi thật, đáp án chi tiết kèm giải thích cụ thể, cùng bảng từ vựng quan trọng giúp bạn nâng cao 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 điểm 7.0+ trong phần Reading.

1. Hướng Dẫn Làm Bài IELTS Reading

Tổng Quan Về IELTS Reading Test

IELTS Reading Test là phần thi kéo dài 60 phút với 3 passages và tổng cộng 40 câu hỏi. Mỗi câu trả lời đúng được tính 1 điểm, và không bị trừ điểm khi trả lời sai. Độ khó của các passages tăng dần, với Passage 1 thường là bài đọc dễ nhất và Passage 3 là khó nhất.

Phân bổ thời gian khuyến nghị:

• Passage 1: 15-17 phút (bài dễ nhất, cần giành thời gian cho bài khó hơn)
• Passage 2: 18-20 phút (bài trung bình, cần cân nhắc kỹ hơn)
• Passage 3: 23-25 phút (bài khó nhất, cần nhiều thời gian phân tích)

Lưu ý quan trọng: Hãy dành 2-3 phút cuối để chuyển đáp án lên answer sheet và kiểm tra lại chính tả, đặc biệt với các câu điền từ.

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 – Câu hỏi trắc nghiệm (Passages 1 & 3)
2. True/False/Not Given – Xác định thông tin đúng/sai/không có (Passage 1)
3. Matching Information – Nối thông tin với đoạn văn (Passage 1)
4. Yes/No/Not Given – Xác định quan điểm tác giả (Passage 2)
5. Matching Headings – Nối tiêu đề với đoạn văn (Passage 2)
6. Summary Completion – Hoàn thành đoạn tóm tắt (Passage 2)
7. Short-answer Questions – Câu hỏi trả lời ngắn (Passage 3)

2. IELTS Reading Practice Test

PASSAGE 1 – AI Revolution in Medical Diagnosis

Độ khó: Easy (Band 5.0-6.5)

Thời gian đề xuất: 15-17 phút

The integration of artificial intelligence (AI) into healthcare has brought about unprecedented changes in how medical professionals diagnose and treat diseases. Over the past decade, AI systems have evolved from simple rule-based algorithms to sophisticated machine learning models capable of analyzing vast amounts of medical data with remarkable accuracy. This technological revolution is particularly evident in the field of medical diagnosis, where AI tools are now assisting doctors in identifying conditions that might otherwise go undetected until much later stages.

One of the most significant applications of AI in healthcare is in medical imaging analysis. Traditional methods of examining X-rays, CT scans, and MRI images rely heavily on the expertise and experience of radiologists, who must carefully review each image to identify abnormalities. However, human eyes can sometimes miss subtle signs of disease, especially when dealing with hundreds of images daily. AI-powered systems, trained on millions of medical images, can now detect patterns and anomalies that may be invisible to the human eye. For instance, in breast cancer screening, AI algorithms have demonstrated the ability to identify potentially malignant tumors with accuracy rates comparable to, and sometimes exceeding, those of experienced radiologists.

The technology works by using deep learning neural networks that have been trained on extensive databases of medical images. These networks learn to recognize the visual characteristics of various conditions by analyzing thousands of examples. When presented with a new image, the AI system can quickly compare it against its learned patterns and highlight areas of concern. This process, which might take a human expert several minutes, can be completed by AI in mere seconds. Moreover, AI systems do not suffer from fatigue or cognitive biases that can affect human decision-making, particularly during long shifts or when examining repetitive cases.

Cardiovascular disease detection represents another area where AI is making substantial contributions. Heart conditions are among the leading causes of death globally, and early detection is crucial for successful treatment. AI algorithms can now analyze electrocardiograms (ECGs) to identify irregular heart rhythms and other cardiac abnormalities with high precision. Some advanced systems can even predict the likelihood of future cardiac events by analyzing patterns in a patient’s historical health data, including blood pressure readings, cholesterol levels, and lifestyle factors. This predictive capability enables healthcare providers to implement preventive measures before serious complications arise.

In dermatology, AI applications are helping to identify skin cancers at their earliest, most treatable stages. Smartphone apps equipped with AI technology allow patients to take photographs of suspicious moles or skin lesions and receive instant preliminary assessments. While these tools are not meant to replace professional dermatological examinations, they serve as valuable screening tools that encourage individuals to seek medical attention when necessary. Studies have shown that some AI dermatology systems can match the diagnostic accuracy of board-certified dermatologists in identifying melanoma, the deadliest form of skin cancer.

Despite these impressive capabilities, it is important to understand that AI in medical diagnosis is designed to augment, not replace, human doctors. The technology functions best when used as a decision support tool, providing additional information that helps clinicians make more informed choices. Doctors bring critical elements to the diagnostic process that AI currently cannot replicate, including the ability to communicate with patients, understand context, consider multiple factors simultaneously, and apply clinical judgment based on years of training and experience. The most effective healthcare delivery models combine the analytical power of AI with the irreplaceable human elements of empathy, intuition, and professional expertise.

The implementation of AI diagnostic tools also raises important questions about medical liability and responsibility. When an AI system contributes to a diagnosis, who bears responsibility if that diagnosis proves incorrect? Healthcare institutions and regulatory bodies are still working to establish clear guidelines and legal frameworks for AI-assisted medicine. Additionally, there are concerns about data privacy and security, as AI systems require access to sensitive patient information for training and operation. Ensuring that this data is protected and used ethically is paramount to maintaining public trust in AI healthcare technologies.

Looking ahead, the role of AI in medical diagnosis is expected to expand significantly. Researchers are developing AI systems capable of analyzing multiple types of medical data simultaneously, including imaging results, laboratory tests, genetic information, and patient histories, to provide comprehensive diagnostic assessments. This holistic approach could revolutionize personalized medicine, enabling treatments tailored to each individual’s unique biological characteristics and health profile. As AI technology continues to advance and more medical professionals become comfortable working alongside these systems, the quality and accessibility of healthcare are likely to improve substantially, potentially saving countless lives through earlier detection and more accurate diagnosis of serious conditions.

Trí tuệ nhân tạo trong chẩn đoán bệnh y khoa hiện đại với công nghệ máy học

Questions 1-13

Questions 1-5: Multiple Choice

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

1. According to the passage, AI systems in medical diagnosis have developed from
   A. complex machine learning to simple algorithms
   B. basic rule-based systems to advanced learning models
   C. human expertise to computer analysis
   D. manual examination to automated screening

2. What advantage do AI systems have over human radiologists?
   A. They can work longer hours without breaks
   B. They have more medical qualifications
   C. They do not experience tiredness or bias
   D. They can communicate better with patients

3. In cardiovascular disease detection, AI can
   A. replace the need for doctors entirely
   B. only analyze current health conditions
   C. predict future cardiac problems using historical data
   D. perform surgical procedures automatically

4. The passage suggests that AI dermatology apps are primarily intended to
   A. replace visits to dermatologists
   B. provide definitive diagnoses of skin conditions
   C. encourage people to seek medical help when needed
   D. treat melanoma directly

5. According to the text, the most effective use of AI in healthcare involves
   A. replacing human doctors with machines
   B. combining AI analysis with human medical expertise
   C. using only AI for diagnostic decisions
   D. limiting AI to simple medical tasks

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 analyze medical images faster than human experts.

7. All hospitals around the world have implemented AI diagnostic systems.

8. AI technology in dermatology can identify melanoma with accuracy similar to qualified dermatologists.

9. Patients prefer AI diagnosis over consultation with human doctors.

Questions 10-13: Matching Information

Which paragraph contains the following information?

Write the correct letter, A-H.

NB: You may use any letter more than once.

10. A discussion of legal and ethical concerns regarding AI in medicine

11. An explanation of how AI neural networks learn to recognize medical conditions

12. Examples of AI applications in detecting heart problems

13. A description of future developments in AI medical diagnosis

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PASSAGE 2 – The Economic and Operational Impact of AI in Healthcare Systems

Độ khó: Medium (Band 6.0-7.5)

Thời gian đề xuất: 18-20 phút

The adoption of artificial intelligence technologies in healthcare extends far beyond clinical diagnosis, fundamentally reshaping the operational infrastructure and economic dynamics of medical institutions worldwide. Healthcare systems, particularly in developed nations, face mounting pressure from aging populations, rising treatment costs, and chronic staff shortages. AI presents a potential solution to these multifaceted challenges, though its implementation involves complex considerations regarding cost-benefit analysis, workforce transformation, and systemic integration. Understanding these broader implications is essential for healthcare administrators, policymakers, and medical professionals navigating this technological transition.

A. Resource Optimization Through AI

Modern hospitals generate enormous quantities of data daily, from patient admissions and treatment protocols to supply chain management and staffing schedules. Traditionally, managing this information has required substantial human resources and often resulted in inefficiencies. AI-powered hospital management systems can analyze these complex datasets in real-time, identifying patterns and optimizing resource allocation. For instance, AI algorithms can predict patient admission rates based on seasonal patterns, local disease outbreaks, and historical trends, enabling hospitals to adjust staffing levels proactively. Similarly, predictive maintenance systems using AI can monitor medical equipment, forecasting potential failures before they occur and scheduling repairs during off-peak hours, thereby minimizing disruption to patient care and reducing costly emergency repairs.

B. Financial Implications and Cost Structures

The economic case for AI in healthcare involves substantial upfront investments against projected long-term savings and improved outcomes. Initial costs include purchasing or developing AI systems, integrating them with existing electronic health record (EHR) platforms, training staff, and maintaining ongoing technical support. A typical AI diagnostic system might cost a healthcare institution between $500,000 and $2 million for implementation. However, proponents argue these expenses are offset by reduced diagnostic errors, decreased hospital readmission rates, more efficient use of expensive medical equipment, and shortened patient stay durations. Recent studies suggest that widespread AI adoption could reduce healthcare costs by 5-10% annually while improving patient outcomes, though these projections vary significantly depending on the specific applications and institutional contexts. Tương tự như telemedicine for mental health services, AI technologies promise to transform healthcare delivery while reducing overall system costs.

C. Workforce Transformation and Professional Development

Perhaps the most contentious aspect of AI implementation concerns its impact on healthcare employment. Contrary to apocalyptic predictions of mass unemployment, evidence suggests AI is more likely to transform rather than eliminate healthcare jobs. Routine administrative tasks, such as scheduling appointments, processing insurance claims, and transcribing medical notes, are increasingly being automated, freeing healthcare professionals to focus on more complex, patient-centered activities. However, this shift necessitates significant professional retraining and skill development. Medical professionals must become proficient in working alongside AI systems, interpreting their outputs critically, and understanding their limitations. Medical schools and continuing education programs are beginning to incorporate AI literacy into their curricula, recognizing that future healthcare workers will need to be comfortable with these technologies. Some institutions are creating new roles, such as clinical AI specialists, who bridge the gap between technical developers and medical practitioners.

D. Telemedicine and Remote Healthcare Delivery

AI has become an indispensable component of the rapidly expanding telemedicine sector, which gained unprecedented momentum during the global health crisis of 2020-2021. AI-powered chatbots and virtual health assistants provide initial patient assessments, triaging cases based on symptom severity and directing individuals to appropriate care levels. These systems can handle thousands of simultaneous consultations, dramatically extending the reach of limited healthcare resources, particularly in rural or underserved areas. Advanced remote monitoring systems employ AI to continuously analyze data from wearable devices, alerting healthcare providers to concerning changes in patients’ vital signs or activity patterns. This technology enables more effective management of chronic conditions like diabetes, heart disease, and respiratory disorders, potentially reducing emergency hospital visits and allowing patients to receive care in their homes.

E. Drug Discovery and Development Acceleration

The pharmaceutical industry has embraced AI as a transformative tool for accelerating drug discovery and development, processes that traditionally take over a decade and cost billions of dollars. AI algorithms can rapidly screen millions of molecular compounds to identify promising drug candidates, predict how these compounds will interact with biological systems, and even suggest modifications to improve efficacy or reduce side effects. Several AI-discovered drugs are currently in clinical trials, and at least one has received regulatory approval. This acceleration could significantly reduce the time and cost required to bring new treatments to market, making pharmaceutical development more economically viable and potentially lowering drug prices. Machine learning models can also help identify existing drugs that might be effective against new diseases, a process called drug repurposing, which proved valuable during the search for COVID-19 treatments.

F. Integration Challenges and Technical Limitations

Despite its promise, AI integration into healthcare systems faces substantial obstacles. Legacy IT infrastructure in many hospitals was not designed to accommodate sophisticated AI applications, requiring costly upgrades or complete replacements. Interoperability issues between different systems and institutions hinder the seamless data sharing that AI requires to function optimally. There are also significant concerns about algorithmic bias; if AI systems are trained predominantly on data from certain demographic groups, they may perform poorly or even harmfully when applied to underrepresented populations. A notable example involved an AI system that underestimated health needs of Black patients because it was trained on healthcare cost data, which reflected existing disparities in access rather than actual health needs. Addressing these biases requires diverse training datasets and ongoing monitoring of AI system performance across different patient populations. Additionally, when examining what are the implications of AI in ethical decision-making, we find that healthcare institutions must carefully consider moral frameworks when implementing these technologies.

G. Regulatory Frameworks and Quality Assurance

Healthcare is one of the most heavily regulated industries, and AI applications must navigate complex approval processes before clinical deployment. Regulatory bodies like the FDA (Food and Drug Administration) in the United States and the EMA (European Medicines Agency) in Europe are still developing appropriate frameworks for evaluating AI medical devices. Traditional medical device regulation assumes relatively static products, but AI systems that continuously learn and evolve present novel challenges. How should regulators evaluate a diagnostic tool that improves its performance over time? What standards should govern software updates that might change an AI system’s behavior? These questions are still being debated, and the regulatory uncertainty can slow AI adoption and innovation. Meanwhile, healthcare institutions must establish internal quality assurance protocols to monitor AI system performance, validate their decisions, and ensure they continue to meet clinical standards over time.

H. Future Healthcare Ecosystems

Looking toward the future, experts envision healthcare ecosystems where AI is seamlessly integrated throughout the patient journey, from preventive care and early screening through diagnosis, treatment selection, and post-treatment monitoring. Such systems would be characterized by predictive and preventive approaches rather than reactive treatment models, identifying health risks before they manifest as serious conditions. Personalized medicine, tailored to individual genetic profiles and lifestyle factors, would become standard rather than exceptional. However, realizing this vision requires not only technological advancement but also significant changes in healthcare policy, insurance models, and professional culture. The transition period will likely be lengthy and complex, requiring sustained collaboration between technologists, healthcare providers, regulators, and patients themselves to ensure that AI’s integration into healthcare genuinely serves the goal of improving health outcomes for all populations equitably and sustainably.

Hệ thống quản lý bệnh viện sử dụng trí tuệ nhân tạo tối ưu hóa quy trình

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. The primary motivation for adopting AI in healthcare is to address operational and economic challenges.

15. AI will completely replace human healthcare workers within the next decade.

16. The initial investment costs for AI systems are too high for most healthcare institutions to afford.

17. AI-powered drug discovery has already produced medications approved for clinical use.

18. Current regulatory frameworks are perfectly adequate for evaluating AI medical technologies.

Questions 19-23: Matching Headings

The passage has eight paragraphs, A-H.

Choose the correct heading for paragraphs C-G from the list of headings below.

Write the correct number, i-x.

List of Headings:

i. The role of AI in medication development
ii. Economic considerations in AI adoption
iii. Problems with implementing AI in existing systems
iv. Changes to healthcare jobs and required skills
v. Government funding for AI research
vi. AI applications in emergency medicine
vii. Remote healthcare enabled by AI technology
viii. International competition in AI development
ix. Standards and approval processes for AI systems
x. Public opinion about AI in healthcare

19. Paragraph C
20. Paragraph D
21. Paragraph E
22. Paragraph F
23. Paragraph G

Questions 24-26: Summary Completion

Complete the summary below.

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

AI hospital management systems can analyze large amounts of data to improve how resources are used. These systems can predict the number of patient admissions by examining patterns and past information, which helps hospitals adjust their 24. __ appropriately. AI can also monitor medical equipment and predict when 25. __ might occur, allowing repairs to be scheduled during quieter periods. This approach reduces expensive 26. __ and minimizes interruption to patient care.

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PASSAGE 3 – Ethical Dimensions and Societal Implications of AI in Medical Decision-Making

Độ khó: Hard (Band 7.0-9.0)

Thời gian đề xuất: 23-25 phút

The proliferation of artificial intelligence within healthcare contexts has precipitated profound ethical quandaries that extend well beyond mere technical considerations, engaging fundamental questions about the nature of medical practice, the physician-patient relationship, and the very epistemology of medical knowledge. While much discourse surrounding AI in healthcare focuses on its diagnostic accuracy and operational efficiency, these technological capabilities exist within complex ethical landscapes characterized by competing values, asymmetric power relationships, and profound uncertainties. A comprehensive examination of these ethical dimensions reveals tensions between beneficence and autonomy, between individual rights and collective welfare, and between innovation and precaution that resist simplistic resolution and demand nuanced, context-sensitive approaches.

The principle of informed consent, foundational to medical ethics since at least the mid-twentieth century, encounters particular complications in the context of AI-assisted medicine. Traditional models of informed consent presume that physicians can explain treatment options to patients in comprehensible terms, enabling patients to make autonomous decisions aligned with their values and preferences. However, many contemporary AI systems, particularly those employing deep learning architectures, function as “black boxes” whose internal decision-making processes remain opaque even to their designers. When an AI algorithm recommends a particular treatment based on pattern recognition across millions of patient cases, neither the physician nor the patient may fully understand the specific factors that generated that recommendation. This epistemological opacity raises critical questions: Can consent truly be “informed” when neither party comprehends the reasoning behind a recommendation? Does the physician’s fiduciary responsibility to the patient extend to understanding and being able to explain AI-generated recommendations, or is it sufficient to know that such systems generally perform well statistically? Some bioethicists argue that this opacity necessitates development of “explainable AI” systems that can articulate their reasoning in human-comprehensible terms, while others contend that demanding complete transparency may be unrealistic and that we must develop new consent frameworks appropriate to AI-assisted medicine.

The question of algorithmic bias represents another critical ethical challenge with profound implications for health equity. Machine learning systems learn patterns from the data on which they are trained, and when that training data reflects existing social inequalities and historical discrimination, AI systems may perpetuate or even amplify these biases. A particularly troubling example emerged from research examining an algorithm widely used in the United States to identify patients who might benefit from additional healthcare support. Researchers discovered that the algorithm systematically underestimated the health needs of Black patients compared to equally sick White patients. This occurred because the algorithm used healthcare costs as a proxy for health needs, and since Black patients in the United States typically receive less expensive care due to systemic inequalities in healthcare access, the algorithm incorrectly interpreted lower costs as indicating better health. This case exemplifies how AI systems can encode and operationalize existing social injustices, potentially exacerbating health disparities under the guise of objective, data-driven decision-making. The economic impacts of automation on service industries demonstrate similar patterns of how technological systems can perpetuate existing inequalities when not carefully designed with equity considerations in mind.

Addressing algorithmic bias requires more than technical solutions; it demands critical examination of which data we collect, how we define health outcomes, and whose interests are prioritized in algorithm design. Some scholars argue for “participatory design” approaches that involve diverse stakeholders, including members of historically marginalized communities, in developing AI healthcare systems. Others emphasize the importance of “algorithmic auditing”—systematic evaluation of AI system performance across different demographic groups to identify disparate impacts. However, these approaches face practical challenges: acquiring sufficiently diverse training datasets while respecting privacy; defining appropriate fairness metrics when different conceptions of fairness may conflict; and establishing accountability mechanisms when algorithmic bias is identified. Moreover, the global nature of AI development means that systems developed in one cultural context may be deployed in very different settings, potentially producing unexpected biases or inappropriate recommendations. When considering the importance of vocational training in education, we recognize that healthcare workers need specialized preparation to identify and mitigate these algorithmic biases effectively.

The integration of AI into medical practice also reconfigures professional authority and epistemic power within healthcare. Historically, physicians’ authority derived from their specialized knowledge, acquired through extensive training and experience, which positioned them as experts capable of interpreting complex medical information and applying it to individual cases. AI systems potentially democratize medical knowledge, making sophisticated diagnostic capabilities available to non-specialists and even directly to patients through consumer applications. This democratization might be viewed positively as empowering patients and reducing healthcare access barriers. However, it also raises concerns about de-professionalization, the erosion of professional judgment, and the potential for algorithmic authority to displace human expertise inappropriately. If an AI system’s recommendation contradicts an experienced physician’s clinical judgment, which should prevail? Some healthcare systems have implemented protocols requiring physicians to document justifications when overriding AI recommendations, effectively placing the burden of proof on human judgment. Critics argue this inverts the traditional relationship, treating AI as the default authority and human expertise as requiring special justification, potentially leading to “automation bias”—the tendency to favor automated decision-making even when it may be incorrect.

Furthermore, the commodification of health data necessary for AI system development raises profound ethical concerns about privacy, ownership, and exploitation. AI algorithms require vast datasets for training and validation, typically drawn from patient medical records, genetic information, and increasingly, data from wearable devices and smartphone applications. While individual data points may be de-identified to protect privacy, sophisticated techniques can sometimes re-identify individuals, particularly when multiple datasets are combined. Moreover, questions arise about who owns this health data and who should benefit from its use. When pharmaceutical companies develop profitable AI-discovered drugs using data from public health systems, should those public systems share in the profits? When technology companies build valuable AI platforms trained on data from thousands of patients who provided no explicit consent for such use, what obligations do these companies owe to those patients or their communities? Some scholars advocate for data trusts or data cooperatives that would give patients collective control over their health information and enable them to negotiate terms for its use, though implementing such governance structures faces significant legal and practical obstacles. The parallels with impact of the internet on privacy and security demonstrate how healthcare data concerns reflect broader societal challenges regarding personal information in the digital age.

The temporal dimension of AI ethics deserves particular attention, as the long-term societal implications of widespread AI adoption in healthcare remain highly uncertain. Healthcare AI systems are being deployed at scale even as fundamental questions about their long-term effects remain unanswered. Will AI-assisted medicine improve health outcomes equitably across all populations, or will it primarily benefit those already privileged with access to advanced healthcare? Will AI’s efficiency gains be used to reduce healthcare costs and expand access, or will they primarily increase profits for healthcare corporations and technology companies? Will AI augment human medical expertise, creating synergies between human and machine capabilities, or will economic pressures lead to replacing higher-paid professionals with cheaper AI alternatives, potentially degrading care quality? These questions cannot be answered definitively in advance; they will depend on policy choices, regulatory frameworks, professional standards, and market dynamics that are still taking shape. This epistemic uncertainty suggests the need for adaptive governance approaches that can respond to emerging evidence about AI’s actual impacts rather than presuming particular outcomes.

The development of appropriate governance frameworks for AI in healthcare requires balancing multiple, sometimes conflicting objectives: promoting beneficial innovation while preventing harm; protecting individual privacy while enabling research that requires data sharing; maintaining professional standards while adapting to technological change; ensuring equitable access while allowing for market-driven development. Different societies may weigh these considerations differently based on their particular cultural values, healthcare systems, and political contexts. Some nations have adopted precautionary approaches, implementing strict regulations before deploying AI healthcare systems widely, while others favor permissive frameworks that allow rapid innovation with lighter regulatory touch. International coordination is complicated by these divergent approaches, yet many ethical challenges—algorithmic bias, data privacy, accountability—transcend national boundaries and arguably demand coordinated responses.

Ultimately, the ethical integration of AI into healthcare depends not merely on technological refinement but on sustained societal deliberation about the kind of healthcare system we wish to create. AI is not a neutral tool that can be simply inserted into existing healthcare practices; rather, its adoption will transform those practices in ways that reflect values, priorities, and power relationships. The crucial question is not whether AI should play a role in healthcare—that question has effectively been settled—but rather how that role should be shaped to promote human welfare, respect individual dignity, advance social justice, and preserve what is valuable in the human dimensions of medical care. Addressing this question adequately requires ongoing dialogue among diverse stakeholders, including not only healthcare professionals and technology developers but also patients, ethicists, policymakers, and the broader public. The decisions made today about AI governance in healthcare will shape medical practice for decades to come, making this a critical moment for thoughtful ethical engagement with these transformative technologies.

Đạo đức y khoa và trí tuệ nhân tạo trong 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, the main challenge with informed consent in AI-assisted medicine is that
    A. patients refuse to accept AI recommendations
    B. doctors are unwilling to use AI systems
    C. neither doctors nor patients may understand how AI reaches its conclusions
    D. AI systems always make incorrect recommendations

28. The example of algorithmic bias in healthcare costs demonstrates that
    A. Black patients receive better healthcare than White patients
    B. AI systems can perpetuate existing social inequalities
    C. healthcare costs are always accurate indicators of health needs
    D. all AI algorithms are intentionally discriminatory

29. The concept of “automation bias” refers to
    A. the tendency to discriminate against certain groups
    B. the preference for automated decisions over human judgment
    C. the cost of implementing automated systems
    D. the technical limitations of AI algorithms

30. According to the passage, data trusts or data cooperatives would
    A. eliminate all privacy concerns in healthcare
    B. give patients collective control over their health information
    C. prevent companies from developing AI systems
    D. make healthcare data freely available to everyone

31. The author’s perspective on AI governance in healthcare can best be described as
    A. strongly opposing all AI applications in medicine
    B. advocating for immediate unrestricted AI deployment
    C. calling for thoughtful, adaptive approaches considering multiple stakeholders
    D. suggesting that technology companies should have complete control

Questions 32-36: Matching Features

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

Write the correct letter, A-H.

Concepts:
32. Explainable AI
33. Participatory design
34. Algorithmic auditing
35. De-professionalization
36. Epistemic opacity

Descriptions:
A. The inability to understand how AI systems make decisions
B. The erosion of professional authority and judgment
C. AI systems that can articulate their reasoning clearly
D. Systematic evaluation of AI performance across different groups
E. The complete replacement of human workers
F. Involving diverse stakeholders in AI system development
G. The prohibition of AI in healthcare settings
H. Automatic approval of all AI recommendations

Questions 37-40: Short-answer Questions

Answer the questions below.

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

37. What term describes the relationship where physicians have special responsibilities toward patients?

38. What is used as an unreliable indicator of health needs in the biased algorithm example?

39. What type of governance approach is suggested for responding to emerging evidence about AI’s impacts?

40. According to the passage, what must be preserved in medical care alongside technological advancement?

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3. Answer Keys – Đáp Án

PASSAGE 1: Questions 1-13

1. B
2. C
3. C
4. C
5. B
6. TRUE
7. NOT GIVEN
8. TRUE
9. NOT GIVEN
10. G
11. C
12. D
13. H

PASSAGE 2: Questions 14-26

14. YES
15. NO
16. NOT GIVEN
17. YES
18. NO
19. iv
20. vii
21. i
22. iii
23. ix
24. staffing levels
25. failures / potential failures
26. emergency repairs

PASSAGE 3: Questions 27-40

27. C
28. B
29. B
30. B
31. C
32. C
33. F
34. D
35. B
36. A
37. fiduciary responsibility
38. healthcare costs
39. adaptive governance
40. human dimensions

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4. Giải Thích Đáp Án Chi Tiết

Passage 1 – Giải Thích

Câu 1: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: AI systems, developed from
• Vị trí trong bài: Đoạn 1, dòng 2-3
• Giải thích: Bài viết nói rõ “AI systems have evolved from simple rule-based algorithms to sophisticated machine learning models.” Điều này được paraphrase trong đáp án B là “basic rule-based systems to advanced learning models.” Các đáp án khác không phản ánh đúng sự phát triển theo thứ tự được mô tả.

Câu 2: C

• Dạng câu hỏi: Multiple Choice
• Từ khóa: advantage, AI systems, human radiologists
• Vị trí trong bài: Đoạn 3, dòng 8-10
• Giải thích: Đoạn văn chỉ rõ “AI systems do not suffer from fatigue or cognitive biases that can affect human decision-making.” Đây chính là đáp án C được paraphrase thành “do not experience tiredness or bias.”

Câu 3: C

• Dạng câu hỏi: Multiple Choice
• Từ khóa: cardiovascular disease detection, AI can
• Vị trí trong bài: Đoạn 4, dòng 4-7
• Giải thích: Bài viết nêu “Some advanced systems can even predict the likelihood of future cardiac events by analyzing patterns in a patient’s historical health data.” Đáp án C diễn đạt chính xác khả năng này.

Câu 6: TRUE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: AI systems, analyze medical images faster
• Vị trí trong bài: Đoạn 3, dòng 6-7
• Giải thích: Văn bản nói “This process, which might take a human expert several minutes, can be completed by AI in mere seconds,” xác nhận AI nhanh hơn con người.

Câu 7: NOT GIVEN

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: all hospitals, worldwide, implemented AI
• Vị trí trong bài: Không có thông tin
• Giải thích: Bài viết không đề cập đến việc tất cả bệnh viện đã triển khai AI. Không có thông tin để xác nhận hay phủ nhận điều này.

Câu 10: G

• Dạng câu hỏi: Matching Information
• Từ khóa: legal, ethical concerns
• Vị trí trong bài: Đoạn 7 (đoạn G)
• Giải thích: Đoạn này thảo luận về “medical liability,” “legal frameworks,” và “data privacy” – các vấn đề pháp lý và đạo đức liên quan đến AI trong y tế.

Câu 11: C

• Dạng câu hỏi: Matching Information
• Từ khóa: AI neural networks learn, recognize medical conditions
• Vị trí trong bài: Đoạn 3 (đoạn C)
• Giải thích: Đoạn này giải thích chi tiết cách “deep learning neural networks” học từ cơ sở dữ liệu và nhận diện các đặc điểm thị giác của bệnh tật.

Passage 2 – Giải Thích

Câu 14: YES

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: primary motivation, operational and economic challenges
• Vị trí trong bài: Đoạn 1, dòng 1-3
• Giải thích: Đoạn mở đầu nêu rõ healthcare systems “face mounting pressure from aging populations, rising treatment costs, and chronic staff shortages” và “AI presents a potential solution to these multifaceted challenges.” Điều này thể hiện quan điểm của tác giả về động lực chính.

Câu 15: NO

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: completely replace, human healthcare workers
• Vị trí trong bài: Đoạn C, dòng 1-2
• Giải thích: Bài viết nói rõ “Contrary to apocalyptic predictions of mass unemployment, evidence suggests AI is more likely to transform rather than eliminate healthcare jobs,” trái ngược với phát biểu trong câu hỏi.

Câu 17: YES

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: AI-discovered drugs, approved
• Vị trí trong bài: Đoạn E, dòng 4-5
• Giải thích: Đoạn văn nêu “Several AI-discovered drugs are currently in clinical trials, and at least one has received regulatory approval,” khẳng định có thuốc do AI phát hiện đã được phê duyệt.

Câu 19: iv (Paragraph C)

• Dạng câu hỏi: Matching Headings
• Từ khóa: workforce transformation, professional development
• Giải thích: Đoạn C tập trung vào tác động của AI đến việc làm trong y tế và nhu cầu đào tạo lại, tương ứng với tiêu đề “Changes to healthcare jobs and required skills.”

Câu 20: vii (Paragraph D)

• Dạng câu hỏi: Matching Headings
• Từ khóa: telemedicine, remote healthcare
• Giải thích: Đoạn D thảo luận về vai trò của AI trong y tế từ xa và telemedicine, phù hợp với tiêu đề “Remote healthcare enabled by AI technology.”

Câu 24: staffing levels

• Dạng câu hỏi: Summary Completion
• Từ khóa: adjust, appropriately
• Vị trí trong bài: Đoạn A, dòng 5
• Giải thích: Câu trong bài viết “enabling hospitals to adjust staffing levels proactively” cung cấp cụm từ chính xác.

Câu 25: failures / potential failures

• Dạng câu hỏi: Summary Completion
• Từ khóa: predict, might occur
• Vị trí trong bài: Đoạn A, dòng 6-7
• Giải thích: Bài viết nói “forecasting potential failures before they occur,” với “failures” là từ phù hợp nhất.

Passage 3 – Giải Thích

Câu 27: C

• Dạng câu hỏi: Multiple Choice
• Từ khóa: informed consent, main challenge
• Vị trí trong bài: Đoạn 2, dòng 4-7
• Giải thích: Đoạn văn nêu rõ “neither the physician nor the patient may fully understand the specific factors that generated that recommendation,” đây chính là thách thức chính được mô tả.

Câu 28: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: algorithmic bias, healthcare costs, demonstrates
• Vị trí trong bài: Đoạn 3, dòng 6-12
• Giải thích: Ví dụ này được đưa ra để minh họa cách “AI systems can encode and operationalize existing social injustices, potentially exacerbating health disparities,” tương ứng với đáp án B.

Câu 29: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: automation bias, refers to
• Vị trí trong bài: Đoạn 5, dòng 9-10
• Giải thích: Thuật ngữ được định nghĩa là “the tendency to favor automated decision-making even when it may be incorrect,” khớp với đáp án B.

Câu 32: C (Explainable AI)

• Dạng câu hỏi: Matching Features
• Vị trí trong bài: Đoạn 2, dòng 10-11
• Giải thích: Bài viết nói về “explainable AI systems that can articulate their reasoning in human-comprehensible terms,” tương ứng với mô tả C.

Câu 35: B (De-professionalization)

• Dạng câu hỏi: Matching Features
• Vị trí trong bài: Đoạn 5, dòng 4-5
• Giải thích: Đoạn văn đề cập “concerns about de-professionalization, the erosion of professional judgment,” khớp với mô tả B.

Câu 37: fiduciary responsibility

• Dạng câu hỏi: Short-answer Questions
• Từ khóa: relationship, special responsibilities, physicians, patients
• Vị trí trong bài: Đoạn 2, dòng 8
• Giải thích: Cụm từ “fiduciary responsibility to the patient” xuất hiện chính xác trong văn bản.

Câu 40: human dimensions

• Dạng câu hỏi: Short-answer Questions
• Từ khóa: preserved, medical care, technological advancement
• Vị trí trong bài: Đoạn cuối, dòng 5-6
• Giải thích: Câu cuối cùng nói về “preserve what is valuable in the human dimensions of medical care.”

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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
unprecedented	adj	/ʌnˈpresɪdentɪd/	chưa từng có	unprecedented changes in how medical professionals diagnose	unprecedented growth/success/level
algorithm	n	/ˈælɡərɪðəm/	thuật toán	rule-based algorithms to sophisticated machine learning models	complex algorithm, search algorithm
radiologist	n	/ˌreɪdiˈɒlədʒɪst/	bác sĩ chuyên khoa X quang	rely heavily on the expertise of radiologists	experienced radiologist, consultant radiologist
malignant	adj	/məˈlɪɡnənt/	ác tính (khối u)	identify potentially malignant tumors	malignant tumor/growth/cells
cognitive bias	n	/ˈkɒɡnətɪv ˈbaɪəs/	thiên kiến nhận thức	do not suffer from cognitive biases	unconscious bias, confirmation bias
cardiovascular	adj	/ˌkɑːdiəʊˈvæskjələ(r)/	thuộc tim mạch	cardiovascular disease detection	cardiovascular system/health/risk
electrocardiogram	n	/ɪˌlektrəʊˈkɑːdiəɡræm/	điện tâm đồ (ECG)	analyze electrocardiograms to identify irregular heart rhythms	abnormal electrocardiogram
predictive	adj	/prɪˈdɪktɪv/	có tính dự đoán	this predictive capability enables healthcare providers	predictive analytics/model/value
dermatology	n	/ˌdɜːməˈtɒlədʒi/	da liễu học	in dermatology, AI applications are helping	clinical dermatology
melanoma	n	/ˌmeləˈnəʊmə/	ung thư da dạng hắc tố	identifying melanoma, the deadliest form of skin cancer	malignant melanoma, melanoma risk
augment	v	/ɔːɡˈment/	tăng cường, bổ sung	designed to augment, not replace, human doctors	augment income/capacity/workforce
empathy	n	/ˈempəθi/	sự đồng cảm	human elements of empathy, intuition	show empathy, feel empathy for

Passage 2 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
operational infrastructure	n	/ˌɒpəˈreɪʃənl ˈɪnfrəstrʌktʃə(r)/	cơ sở hạ tầng vận hành	reshaping the operational infrastructure of medical institutions	critical infrastructure, IT infrastructure
chronic shortage	n	/ˈkrɒnɪk ˈʃɔːtɪdʒ/	tình trạng thiếu hụt mãn tính	chronic staff shortages	chronic shortage of housing/workers
cost-benefit analysis	n	/kɒst ˈbenɪfɪt əˈnæləsɪs/	phân tích chi phí – lợi ích	complex considerations regarding cost-benefit analysis	conduct a cost-benefit analysis
predictive maintenance	n	/prɪˈdɪktɪv ˈmeɪntənəns/	bảo trì dự báo	predictive maintenance systems using AI	predictive maintenance strategy
electronic health record	n	/ɪˌlektrɒnɪk helθ ˈrekɔːd/	hồ sơ sức khỏe điện tử	integrating with existing EHR platforms	access electronic health records
telemedicine	n	/ˌteliˈmedɪsn/	y tế từ xa	indispensable component of telemedicine sector	telemedicine consultation/services
drug repurposing	n	/drʌɡ ˌriːˈpɜːpəsɪŋ/	tái sử dụng thuốc	a process called drug repurposing	drug repurposing strategy
legacy IT infrastructure	n	/ˈleɡəsi aɪˈtiː ˈɪnfrəstrʌktʃə(r)/	cơ sở hạ tầng IT cũ	legacy IT infrastructure in many hospitals	upgrade legacy systems
interoperability	n	/ˌɪntərˌɒpərəˈbɪləti/	khả năng tương tác	interoperability issues between different systems	ensure interoperability, system interoperability
algorithmic bias	n	/ˌælɡəˈrɪðmɪk ˈbaɪəs/	thiên kiến thuật toán	significant concerns about algorithmic bias	address algorithmic bias, reduce bias
quality assurance	n	/ˈkwɒləti əˈʃʊərəns/	đảm bảo chất lượng	establish internal quality assurance protocols	quality assurance procedures/standards
personalized medicine	n	/ˈpɜːsənəlaɪzd ˈmedsn/	y học cá nhân hóa	personalized medicine would become standard	era of personalized medicine
preventive approach	n	/prɪˈventɪv əˈprəʊtʃ/	phương pháp phòng ngừa	characterized by predictive and preventive approaches	preventive healthcare/measures

Passage 3 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
epistemology	n	/ɪˌpɪstɪˈmɒlədʒi/	nhận thức luận	the very epistemology of medical knowledge	theory of epistemology
asymmetric power	n	/ˌæsɪˈmetrɪk ˈpaʊə(r)/	quyền lực bất cân xứng	characterized by asymmetric power relationships	asymmetric power dynamics
beneficence	n	/bɪˈnefɪsns/	sự làm lành	tensions between beneficence and autonomy	principle of beneficence
informed consent	n	/ɪnˈfɔːmd kənˈsent/	sự đồng ý sau khi được thông báo	principle of informed consent	obtain informed consent, give informed consent
fiduciary responsibility	n	/fɪˈdjuːʃəri rɪˌspɒnsəˈbɪləti/	trách nhiệm tín thác	physician’s fiduciary responsibility to the patient	fiduciary duty/obligation
epistemological opacity	n	/ɪˌpɪstɪməˈlɒdʒɪkl əʊˈpæsəti/	sự mờ mịt về nhận thức	this epistemological opacity raises critical questions	transparency vs opacity
bioethicist	n	/ˌbaɪəʊˈeθɪsɪst/	nhà đạo đức sinh học	some bioethicists argue that	clinical bioethicist
explainable AI	n	/ɪkˈspleɪnəbl eɪˈaɪ/	AI có thể giải thích được	development of explainable AI systems	explainable AI methods/approaches
health equity	n	/helθ ˈekwəti/	công bằng y tế	profound implications for health equity	promote health equity, achieve equity
proxy	n	/ˈprɒksi/	đại diện, chỉ số thay thế	algorithm used healthcare costs as a proxy	serve as a proxy for
participatory design	n	/pɑːˈtɪsɪpətəri dɪˈzaɪn/	thiết kế có sự tham gia	participatory design approaches	participatory design process/methodology
algorithmic auditing	n	/ˌælɡəˈrɪðmɪk ˈɔːdɪtɪŋ/	kiểm toán thuật toán	importance of algorithmic auditing	conduct algorithmic auditing
de-professionalization	n	/diː prəˌfeʃnəlaɪˈzeɪʃn/	phi chuyên nghiệp hóa	concerns about de-professionalization	process of de-professionalization
automation bias	n	/ˌɔːtəˈmeɪʃn ˈbaɪəs/	thiên kiến tự động hóa	potentially leading to automation bias	overcome automation bias
commodification	n	/kəˌmɒdɪfɪˈkeɪʃn/	sự thương mại hóa	commodification of health data	commodification of information/data
de-identified	adj	/diː aɪˈdentɪfaɪd/	được ẩn danh hóa	individual data points may be de-identified	de-identified patient data
adaptive governance	n	/əˈdæptɪv ˈɡʌvənəns/	quản trị thích ứng	need for adaptive governance approaches	adaptive governance framework/system
precautionary approach	n	/prɪˈkɔːʃənəri əˈprəʊtʃ/	cách tiếp cận thận trọng	adopted precautionary approaches	precautionary principle/measures

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Kết Bài

Chủ đề “The Role Of Artificial Intelligence In Healthcare” không chỉ là một trong những đề tài nóng hổi nhất trong kỳ thi IELTS Reading hiện nay mà còn phản ánh xu hướng phát triển công nghệ đang định hình lại ngành y tế toàn cầu. Qua bộ đề thi mẫu này, bạn đã được trải nghiệm đầy đủ 3 passages với độ khó tăng dần từ Easy đến Hard, bao gồm tổng cộng 40 câu hỏi đa dạng giống như trong bài thi IELTS thực tế.

Passage 1 giới thiệu những ứng dụng cơ bản của AI trong chẩn đoán y tế, phù hợp cho học viên band 5.0-6.5. Passage 2 đi sâu vào tác động kinh tế và vận hành của AI trong hệ thống y tế, yêu cầu khả năng đọc hiểu ở mức band 6.0-7.5. Passage 3 thách thức học viên với các vấn đề đạo đức và xã hội phức tạp, phù hợp với những ai nhắm đến band 7.0-9.0. Đáp án chi tiết kèm giải thích cụ thể đã giúp bạn hiểu rõ cách xác định thông tin trong bài và phương pháp làm bài cho từng dạng câu hỏi.

Bảng từ vựng tổng hợp hơn 40 từ và cụm từ học thuật quan trọng sẽ là công cụ hữu ích giúp bạn mở rộng vốn từ vựng chuyên ngành, đặc biệt trong lĩnh vực công nghệ và y tế. Hãy luyện tập thường xuyên với các đề thi tương tự, phân tích kỹ cách paraphrase giữa câu hỏi và passage, và rèn luyện kỹ năng quản lý thời gian để đạt kết quả tốt nhất trong kỳ thi IELTS Reading. Chúc bạn ôn tập hiệu quả và đạt được band điểm mục tiêu!