Mở Bài
Chủ đề về trí tuệ nhân tạo trong y học cá nhân hóa (How Artificial Intelligence Is Improving Personalized Medicine) đang trở thành một trong những đề tài phổ biến trong kỳ thi IELTS Reading những năm gần đây. Với sự phát triển nhanh chóng của công nghệ và y học hiện đại, chủ đề này xuất hiện trong khoảng 15-20% các đề thi IELTS Reading chính thức, đặc biệt trong Passage 2 và Passage 3 với độ khó từ trung bình đến cao.
Bài viết này cung cấp cho bạn một bộ đề thi IELTS Reading hoàn chỉnh bao gồm 3 passages với độ khó tăng dần từ Easy (Band 5.0-6.5), Medium (Band 6.0-7.5) đến Hard (Band 7.0-9.0). Bạn sẽ được luyện tập với 40 câu hỏi đa dạng giống như trong bài thi thực tế, bao gồm Multiple Choice, True/False/Not Given, Yes/No/Not Given, Matching Headings, Summary Completion và nhiều dạng khác.
Đặc biệt, mỗi câu hỏi đều có đáp án chi tiết kèm giải thích về cách tìm thông tin, kỹ thuật paraphrase, và vị trí chính xác trong bài đọc. Bạn cũng sẽ học được hơn 40 từ vựng quan trọng liên quan đến chủ đề công nghệ, y học và khoa học, kèm theo phiên âm, nghĩa tiếng Việt và cách sử dụng thực tế.
Bộ đề này phù hợp cho học viên từ band 5.0 trở lên, giúp bạn làm quen với văn phong học thuật, rèn luyện kỹ năng đọc hiểu và cải thiện tốc độ làm bài một cách bài bản nhất.
1. Hướng Dẫn Làm Bài IELTS Reading
Tổng Quan Về IELTS Reading Test
IELTS Reading Test kéo dài 60 phút cho 3 passages với tổng cộng 40 câu hỏi. Điểm số được tính từ 0-9 band dựa trên số câu trả lời đúng, không bị trừ điểm khi sai.
Phân bổ thời gian khuyến nghị:
- Passage 1 (Easy): 15-17 phút – Đây là bài dễ nhất, giúp bạn khởi động tốt
- Passage 2 (Medium): 18-20 phút – Độ khó tăng lên, cần đọc kỹ hơn
- Passage 3 (Hard): 23-25 phút – Bài khó nhất, dành nhiều thời gian nhất
Lưu ý: Dành 2-3 phút cuối để chuyển đáp án vào Answer Sheet và kiểm tra lại.
Các Dạng Câu Hỏi Trong Đề Này
Đề thi mẫu này bao gồm 7 dạng câu hỏi phổ biến nhất trong IELTS Reading:
- Multiple Choice – Câu hỏi trắc nghiệm 4 lựa chọn
- True/False/Not Given – Xác định thông tin đúng/sai/không có trong bài
- Summary Completion – Hoàn thành đoạn tóm tắt
- Yes/No/Not Given – Xác định ý kiến/quan điểm của tác giả
- Matching Headings – Nối tiêu đề với đoạn văn
- Sentence Completion – Hoàn thành câu
- Short-answer Questions – Trả lời câu hỏi ngắn
Mỗi dạng câu hỏi yêu cầu kỹ năng đọc hiểu khác nhau, từ tìm thông tin chi tiết đến phân tích ý chính và suy luận.
2. IELTS Reading Practice Test
PASSAGE 1 – The Dawn of Smart Healthcare
Độ khó: Easy (Band 5.0-6.5)
Thời gian đề xuất: 15-17 phút
In recent years, artificial intelligence (AI) has begun to revolutionize the way doctors diagnose and treat patients. Traditional medicine often takes a one-size-fits-all approach, where patients with similar symptoms receive identical treatments. However, every person is unique, with different genetic makeups, lifestyles, and environmental factors that influence how they respond to medications. This is where personalized medicine comes into play, and AI is making it more accessible than ever before.
Personalized medicine, also known as precision medicine, aims to tailor medical treatment to the individual characteristics of each patient. Rather than prescribing the same drug to everyone with a particular condition, doctors can now use AI systems to analyze a patient’s genetic information, medical history, and even lifestyle choices to determine the most effective treatment. This approach minimizes side effects and maximizes therapeutic benefits, leading to better health outcomes.
One of the most significant ways AI is improving personalized medicine is through data analysis. Modern healthcare generates enormous amounts of data every day, from electronic health records to genetic sequencing results. A human doctor would need years to analyze all this information for just one patient, but AI can process millions of data points in seconds. Machine learning algorithms can identify patterns that might be invisible to the human eye, such as subtle genetic markers that indicate how a patient will respond to a specific drug.
For example, cancer treatment has been transformed by AI-powered personalized medicine. In the past, oncologists would prescribe chemotherapy based on the type and stage of cancer. Today, AI systems can analyze a tumor’s genetic profile and recommend targeted therapies that attack cancer cells while leaving healthy cells relatively unharmed. This has led to improved survival rates and reduced side effects for many cancer patients. Some hospitals now use AI to predict which combination of drugs will work best for individual patients, taking into account their unique biological characteristics.
Drug development is another area where AI is making a substantial impact. Traditionally, developing a new medication takes over a decade and costs billions of dollars. AI can accelerate this process by predicting how different molecular compounds will interact with the human body. By analyzing vast databases of chemical structures and their effects, AI can identify promising drug candidates much faster than traditional methods. This means patients can access new treatments sooner, and pharmaceutical companies can reduce development costs.
AI-powered diagnostic tools are also becoming more sophisticated. Some AI systems can now detect diseases earlier and more accurately than human doctors. For instance, AI algorithms have been trained to identify early signs of diabetic retinopathy by analyzing retinal images, often catching the disease before a patient experiences symptoms. Similarly, AI can analyze medical imaging such as X-rays, MRIs, and CT scans to detect tumors, fractures, and other abnormalities with remarkable accuracy. These tools don’t replace doctors but rather augment their capabilities, allowing them to make more informed decisions.
Wearable technology combined with AI is bringing personalized medicine into everyday life. Smart watches and fitness trackers can monitor heart rate, sleep patterns, and physical activity levels continuously. AI algorithms analyze this data to provide personalized health recommendations and even predict potential health problems before they become serious. For example, some devices can detect irregular heartbeats that might indicate atrial fibrillation, a condition that increases stroke risk. Early detection allows patients to seek treatment before complications develop.
Despite these advances, there are still challenges to overcome. Privacy concerns are paramount, as personalized medicine requires access to sensitive genetic and health information. Patients worry about who has access to their data and how it might be used. Additionally, there’s the question of accessibility. While AI-powered personalized medicine offers tremendous benefits, the technology is expensive and not yet available to everyone. Healthcare systems must work to ensure that these innovations reach patients in all communities, not just those in wealthy areas or developed countries.
Another concern is the reliability of AI systems. While AI can process data quickly, it’s only as good as the data it’s trained on. If the training data is biased or incomplete, the AI’s recommendations might not be appropriate for all populations. Researchers are working to address these issues by creating more diverse datasets and developing AI systems that can adapt to different patient populations. Transparency is also crucial—doctors and patients need to understand how AI systems reach their conclusions so they can trust the recommendations.
Looking ahead, the integration of AI and personalized medicine promises to transform healthcare fundamentally. As technology improves and becomes more accessible, more patients will benefit from treatments specifically designed for their unique needs. The combination of genomic data, real-time health monitoring, and powerful AI analysis could lead to a future where diseases are prevented before they occur, treatments have minimal side effects, and healthcare is truly personalized for every individual.
Questions 1-13
Questions 1-5: Multiple Choice
Choose the correct letter, A, B, C, or D.
1. What is the main difference between traditional medicine and personalized medicine?
A. Personalized medicine is more expensive
B. Traditional medicine treats all patients with similar symptoms the same way
C. Personalized medicine only works for cancer patients
D. Traditional medicine uses more advanced technology
2. According to the passage, what can AI do that human doctors cannot?
A. Perform surgery
B. Communicate with patients
C. Analyze millions of data points in seconds
D. Prescribe medication
3. How has AI improved cancer treatment specifically?
A. By eliminating the need for surgery
B. By analyzing genetic profiles to recommend targeted therapies
C. By making chemotherapy unnecessary
D. By curing all types of cancer
4. What is one concern mentioned about AI in personalized medicine?
A. It is too slow to be useful
B. It cannot analyze genetic information
C. Privacy issues regarding sensitive health data
D. It replaces doctors completely
5. According to the passage, wearable technology can:
A. Cure heart disease
B. Replace hospital visits entirely
C. Monitor health continuously and predict potential problems
D. Only track exercise
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 identify genetic markers that show how patients respond to drugs.
7. Developing new drugs traditionally takes less than five years.
8. All hospitals worldwide now use AI for cancer treatment.
9. AI diagnostic tools can sometimes detect diseases earlier than human doctors.
Questions 10-13: Sentence Completion
Complete the sentences below.
Choose NO MORE THAN TWO WORDS from the passage for each answer.
10. Personalized medicine is also known as __ medicine.
11. AI can analyze a tumor’s __ to recommend specific treatments.
12. Some AI systems can detect irregular heartbeats that might indicate __.
13. Researchers are creating more diverse datasets to ensure AI systems can __ to different patient populations.
PASSAGE 2 – AI-Driven Genomics and Treatment Optimization
Độ khó: Medium (Band 6.0-7.5)
Thời gian đề xuất: 18-20 phút
The convergence of artificial intelligence and genomics represents one of the most profound transformations in modern medicine. While the completion of the Human Genome Project in 2003 was a landmark achievement, understanding what all those genetic sequences actually mean and how they influence health and disease has proven to be an even greater challenge. This is precisely where AI’s computational prowess is making an unprecedented impact, enabling researchers and clinicians to decipher the complex relationships between genetic variations and clinical outcomes in ways that were simply impossible just a decade ago.
Pharmacogenomics—the study of how genes affect a person’s response to drugs—has emerged as a particularly fertile ground for AI applications. The traditional trial-and-error approach to prescribing medications, where doctors adjust dosages and switch drugs based on patient feedback, is not only inefficient but can also be dangerous. Adverse drug reactions cause approximately 100,000 deaths annually in the United States alone, many of which could be prevented through genomic-guided prescribing. AI systems can now analyze a patient’s genomic profile alongside vast databases of pharmacological data to predict with remarkable precision which medications will be effective and which might cause harmful reactions.
Consider the case of warfarin, a widely prescribed blood thinner. The appropriate dosage varies dramatically between individuals based on genetic factors, and incorrect dosing can lead to either dangerous bleeding or ineffective treatment. Traditional methods required weeks of adjustment through repeated blood tests. Now, AI algorithms that incorporate genetic markers such as VKORC1 and CYP2C9 variations, along with clinical factors like age, weight, and concurrent medications, can predict the optimal warfarin dose with over 90% accuracy before the patient takes their first pill. This predictive capability represents a paradigm shift from reactive to proactive medicine.
The application of AI extends beyond individual drug selection to the optimization of complex treatment regimens. In oncology, where patients often receive combinations of multiple drugs, chemotherapy, radiation, and immunotherapy, determining the most effective therapeutic strategy is extraordinarily complex. AI systems employing deep learning techniques can simulate thousands of potential treatment combinations and predict their likely efficacy and toxicity for individual patients. These in silico simulations—essentially virtual clinical trials conducted on computers—enable oncologists to design highly personalized treatment plans that maximize tumor suppression while minimizing harm to healthy tissues.
Multi-omic integration represents another frontier where AI is proving indispensable. Beyond just genomics, modern personalized medicine must consider the proteome (all proteins in the body), the metabolome (all metabolic products), the transcriptome (all RNA molecules), and the microbiome (all microorganisms living in and on the body). Each of these biological layers generates massive amounts of data, and understanding how they interact requires computational approaches that only AI can provide. Machine learning models can identify synergistic relationships between different omics layers—for instance, how certain genetic variants affect protein expression, which in turn influences metabolic pathways relevant to disease progression or drug metabolism.
The temporal dimension of personalized medicine is also being revolutionized by AI. Health is not static; it changes continuously based on physiological fluctuations, environmental exposures, lifestyle factors, and aging. Continuous monitoring through wearable devices and regular biomarker assessments generates longitudinal data streams that AI can analyze to detect subtle changes that might indicate emerging health problems. For instance, AI algorithms can identify patterns in heart rate variability, sleep quality, and activity levels that predict an impending cardiovascular event days or even weeks before symptoms appear, allowing for preventive interventions.
Clinical decision support systems powered by AI are becoming increasingly sophisticated in their ability to integrate genomic information with real-time clinical data. When a patient presents with symptoms, these systems can instantly compare their genetic profile and current health metrics against millions of similar cases in medical databases, identifying the most probable diagnoses and recommending evidence-based treatment approaches. This augmented intelligence—combining human clinical judgment with AI’s analytical capabilities—is proving more effective than either approach alone. Studies have shown that doctors using AI decision support tools make more accurate diagnoses and choose more appropriate treatments than those relying solely on their own expertise or AI systems operating independently.
However, the integration of AI into genomic medicine faces several substantive challenges. The interpretability of AI decisions remains a significant concern. Many advanced AI models, particularly deep neural networks, function as “black boxes“—they provide accurate predictions but don’t clearly explain the reasoning behind them. In healthcare, where decisions can be life-or-death matters, clinicians and patients need to understand why a particular treatment is recommended. Researchers are developing explainable AI techniques that can articulate the factors influencing their recommendations, but this remains an active area of investigation.
Data quality and representativeness pose another critical challenge. Most genomic databases and clinical datasets used to train AI systems have historically overrepresented individuals of European ancestry, potentially leading to algorithms that work well for some populations but poorly for others. This representational bias could exacerbate existing health disparities if AI systems provide less accurate predictions for underrepresented groups. The scientific community has recognized this problem and is working to create more diverse and inclusive datasets, but progress has been slower than many would like.
The ethical dimensions of AI-driven personalized medicine extend beyond privacy concerns to questions of equity, consent, and the very nature of medical practice. As AI becomes more capable of predicting disease risk and treatment outcomes, society must grapple with difficult questions: Should insurance companies have access to genetic risk predictions? How do we ensure that expensive AI-powered treatments don’t become available only to the wealthy? What happens to the doctor-patient relationship when algorithms mediate medical decisions? These questions don’t have easy answers, but they must be addressed as the technology continues to advance.
Despite these challenges, the trajectory is clear: AI and genomics are inexorably intertwined in the future of medicine. As computational methods become more powerful, datasets more comprehensive, and algorithms more sophisticated, personalized medicine will transition from an aspirational concept to standard clinical practice. The vision of healthcare tailored precisely to each individual’s unique biological makeup, continuously adjusted based on real-time data, and optimized through the analytical power of AI is rapidly becoming reality. This transformation promises not just better treatments for disease but the prevention of illness, the extension of healthy lifespan, and a fundamental reimagining of what healthcare can achieve.
Trí tuệ nhân tạo phân tích dữ liệu gen người để cải thiện y học cá nhân hóa trong IELTS Reading
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. Understanding the Human Genome Project’s data has been more challenging than completing the project itself.
15. The trial-and-error approach to prescribing medication is the safest method currently available.
16. AI can predict optimal warfarin dosage with complete 100% accuracy.
17. Multi-omic integration requires computational approaches that only AI can provide.
18. Most genomic databases have equal representation from all ethnic groups.
Questions 19-22: Matching Headings
Choose the correct heading for paragraphs C-F from the list of headings below.
List of Headings:
i. The problem of algorithm transparency in medical contexts
ii. Personalized dosing for blood-thinning medications
iii. The integration of multiple biological data types
iv. Simulating treatment combinations for cancer patients
v. The future of insurance and genetic information
vi. Monitoring health changes over time with AI
vii. Historical development of genome research
19. Paragraph C (begins with “Consider the case of warfarin…”)
20. Paragraph D (begins with “The application of AI extends beyond…”)
21. Paragraph E (begins with “Multi-omic integration represents…”)
22. Paragraph F (begins with “The temporal dimension…”)
Questions 23-26: Summary Completion
Complete the summary below.
Choose NO MORE THAN TWO WORDS from the passage for each answer.
AI-powered clinical decision support systems combine 23. __ with real-time clinical data to help doctors make better diagnoses. These systems use 24. __, comparing patient information against millions of similar cases. This approach, called 25. __, has proven more effective than either doctors or AI working alone. However, many advanced AI models function as “26. __,” providing accurate results without clearly explaining their reasoning.
PASSAGE 3 – Algorithmic Precision in Therapeutic Intervention: Challenges and Horizons
Độ khó: Hard (Band 7.0-9.0)
Thời gian đề xuất: 23-25 phút
The ascendancy of artificial intelligence in personalized medicine represents a fundamental epistemological shift in how we conceptualize disease, treatment, and the very nature of medical knowledge. Traditional medical practice has long been grounded in population-level statistics and probabilistic reasoning—when faced with a patient presenting particular symptoms, physicians draw upon their training and experience to determine the most likely diagnosis and the treatment approach that has proven effective for the majority of patients with similar presentations. This epidemiological framework, while having served medicine well for centuries, is increasingly recognized as insufficiently granular for the molecular precision that modern biotechnology now makes possible. AI’s capacity to process and integrate vast heterogeneous datasets—spanning genomic sequences, proteomic profiles, metabolomic signatures, environmental exposures, behavioral patterns, and longitudinal health records—enables a radically different paradigm: one in which therapeutic decisions are calibrated not to population averages but to the specific biological configuration of the individual patient at a particular moment in time.
The algorithmic architecture underlying this transformation is both remarkably sophisticated and conceptually straightforward. At its core, supervised machine learning involves training computational models on large datasets where the input variables (patient characteristics, biomarkers, genetic variants) are mapped to known outcomes (treatment response, disease progression, adverse events). The algorithm identifies complex non-linear relationships within this multi-dimensional data space, discovering patterns and correlations that would be imperceptible to human analysis. Once trained, these models can then generate predictions for new patients by identifying where they fall within the learned parameter space. More advanced approaches employ deep learning architectures—artificial neural networks with multiple hidden layers—capable of discovering hierarchical representations of data, moving from low-level features to increasingly abstract concepts in ways that mirror (albeit crudely) the human brain’s information processing.
The application of these methodologies to oncological therapeutics provides particularly compelling evidence of AI’s transformative potential. Cancer is fundamentally a disease of genomic instability, where accumulated mutations drive uncontrolled cellular proliferation and resistance mechanisms against therapeutic interventions. The genomic landscape of tumors is extraordinarily heterogeneous, not only between different cancer types but between patients with the same diagnosis and even between different regions within a single tumor. This intra-tumoral heterogeneity poses profound challenges for treatment selection. AI systems trained on comprehensive genomic profiles of thousands of tumors, linked to data on treatment responses and clinical outcomes, can identify molecular subtypes with distinct therapeutic vulnerabilities. For instance, certain breast cancers that appear identical under microscopic examination may harbor fundamentally different driver mutations—some responsive to hormone therapy, others to HER2-targeted agents, still others requiring chemotherapy or immunotherapy. AI can integrate whole genome sequencing data, gene expression patterns, protein markers, and mutational signatures to classify tumors into therapeutically relevant categories with far greater precision than traditional histopathological assessment.
Beyond static tumor characterization, AI enables dynamic treatment optimization through what might be termed “adaptive therapeutics.” Cancer’s capacity for evolutionary adaptation—the emergence of resistant clones under selective pressure from treatment—represents one of the fundamental challenges in oncology. Rather than maintaining a fixed treatment regimen, AI systems can continuously monitor circulating tumor DNA in the bloodstream, tracking the clonal evolution of the cancer in real time. As certain molecular features indicating emerging resistance appear, the algorithm can recommend pre-emptive modification of the therapeutic approach, switching to alternative agents that target the evolving tumor before clinical progression becomes evident. This represents a shift from treating cancer as a static disease to managing it as a dynamic evolutionary process, with AI serving as the computational engine enabling sufficiently rapid analysis to keep pace with tumor evolution.
The implications extend considerably beyond oncology. In psychiatry, where diagnostic categories have historically been based on symptom clusters rather than underlying neurobiology, AI is enabling the identification of biologically distinct subtypes of conditions like depression and schizophrenia. Patients with indistinguishable symptoms may have fundamentally different neurochemical dysfunction, with correspondingly different optimal treatments. AI analysis of neuroimaging data, genetic risk factors, biomarkers in blood or cerebrospinal fluid, and even speech patterns and behavioral digital phenotypes captured through smartphone sensors can identify these biological subtypes, moving psychiatry toward genuine mechanistic personalization rather than symptomatic management.
Immunology and infectious disease represent another domain where AI-driven personalization is gaining traction. The development of therapeutic vaccines and immunotherapies requires understanding the complex interplay between pathogen characteristics, host immune responses, and the specific MHC alleles that determine which antigenic peptides will be presented to T cells. AI algorithms can predict which epitopes from a tumor or pathogen will generate the strongest immune responses in patients with particular genetic backgrounds, enabling the design of personalized vaccines optimized for individual immune systems. Similarly, in managing autoimmune diseases, AI can integrate data on autoantibody profiles, cytokine signatures, microbiome composition, and genetic factors to predict which immunomodulatory therapies will achieve disease control while minimizing side effects.
However, the pathway from algorithmic prediction to clinical implementation is fraught with both technical and societal obstacles. The epistemological challenge of AI interpretability—often characterized through the “black box” metaphor—is particularly acute in medical contexts. When an algorithm recommends a specific treatment, the mechanistic reasoning underlying that recommendation is often opaque, even to the data scientists who developed the model. This poses practical difficulties for clinicians, who are accustomed to making decisions based on understanding disease pathophysiology and treatment mechanisms. It also raises ethical questions about accountability: if an AI-recommended treatment causes harm, who bears responsibility—the physician who followed the recommendation, the institution that deployed the algorithm, or the developers who created it?
The statistical foundations of AI predictions merit careful scrutiny. Machine learning models identify correlations in training data, but correlation does not imply causation, and predictive accuracy in retrospective datasets does not guarantee prospective performance in clinical practice. The phenomenon of “dataset shift“—where the statistical properties of data change over time or across different populations—can cause models that performed well during development to fail when deployed in real clinical environments. Moreover, AI systems are vulnerable to learning spurious correlations—apparent patterns in training data that don’t reflect genuine causal relationships. An infamous example involved an AI system that appeared to identify pneumonia from chest X-rays with high accuracy but was actually detecting subtle differences in imaging equipment and patient positioning between hospitals, rather than pathological features of the disease itself.
The issue of algorithmic bias carries profound implications for health equity. If the data used to train AI systems underrepresent certain demographic groups, the resulting algorithms may systematically underperform for those populations. This is not merely a hypothetical concern; documented cases exist of diagnostic algorithms performing less accurately for racial minorities, symptom assessment tools underestimating disease severity in women, and risk prediction models disadvantaging patients from low socioeconomic backgrounds. These disparities can arise through multiple pathways: biased data collection (if certain groups have less access to healthcare, they generate fewer medical records for training), historical discrimination (if past treatment decisions reflected prejudices, training on historical data perpetuates those biases), and differential feature salience (if certain biomarkers or symptoms manifest differently across populations). Addressing these issues requires not only more diverse training data but also careful algorithmic auditing, fairness-aware machine learning techniques, and ongoing monitoring of model performance across demographic groups.
The regulatory landscape for AI in personalized medicine remains nascent and uncertain. Traditional pharmaceutical regulation involves rigorous clinical trials demonstrating safety and efficacy before drugs reach patients. However, AI systems are fundamentally different from drugs—they can be continuously updated with new data, potentially changing their behavior after initial approval. This dynamic nature doesn’t fit neatly into existing regulatory frameworks. Moreover, the evidentiary standards for AI validation remain contested: How much real-world data is needed to validate an algorithm’s clinical utility? What metrics should be used to assess AI performance—predictive accuracy, clinical outcomes, cost-effectiveness? How should rare but serious errors be weighted against overall accuracy? Regulatory agencies worldwide are grappling with these questions, seeking to create frameworks that ensure patient safety and algorithmic reliability without stifling innovation.
Looking toward the horizon, the integration of AI with emerging biotechnologies promises even more profound capabilities. The convergence of AI with gene editing technologies like CRISPR could enable not just personalized selection among existing therapies but the creation of bespoke genetic interventions tailored to individual patients. AI analysis of a patient’s genomic architecture could identify specific therapeutic edits—correcting disease-causing mutations, modulating gene expression, or even engineering enhanced disease resistance. Similarly, the combination of AI with synthetic biology and bioengineering could facilitate the design of artificial proteins, therapeutic microbes, or engineered cells optimized for individual patients’ physiological contexts.
The ultimate trajectory points toward what might be termed “anticipatory medicine“—systems that don’t merely react to disease but predict and prevent it. By continuously integrating data from genomics, wearable sensors, environmental monitoring, microbiome sequencing, and periodic biomarker assessments, AI could construct dynamic models of individual health trajectories, identifying inflection points where interventions would be most effective. Rather than waiting for symptoms to drive medical encounters, individuals would receive proactive recommendations for lifestyle modifications, preventive treatments, or screening procedures precisely timed to their evolving biological state. This vision of healthcare—continuous, predictive, and individually optimized—represents not merely an incremental improvement but a fundamental reconceptualization of medicine’s purpose and practice.
Yet realizing this vision requires confronting not only technical challenges but fundamental questions about the kind of healthcare system we wish to create. Will these powerful tools exacerbate existing inequalities, available only to those with resources, or can they be deployed equitably? How do we balance the potential benefits of comprehensive data integration against legitimate concerns about privacy and surveillance? What role should patients themselves play in algorithmic decision-making about their treatment? These are not questions with purely technical answers; they require societal deliberation and choices that reflect our values as much as our scientific capabilities. The transformation AI brings to personalized medicine is not merely technological but social, demanding careful attention to ensuring that these powerful new tools serve human flourishing rather than merely technical optimization.
Hệ thống AI phân tích đa tầng sinh học cho điều trị y học cá nhân hóa chính xác trong bài thi IELTS
Questions 27-40
Questions 27-31: Multiple Choice
Choose the correct letter, A, B, C, or D.
27. According to the passage, traditional medical practice is based primarily on:
A. Individual patient molecular profiles
B. Population-level statistics and probabilistic reasoning
C. Artificial intelligence algorithms
D. Real-time genomic analysis
28. What does the author suggest about deep learning architectures?
A. They are identical to how the human brain processes information
B. They can only process one type of data at a time
C. They discover hierarchical representations in ways that crudely mirror brain function
D. They are less sophisticated than basic machine learning
29. Why is intra-tumoral heterogeneity significant for cancer treatment?
A. It makes all cancers identical
B. It eliminates the need for treatment
C. It poses challenges for treatment selection
D. It only affects breast cancer patients
30. The concept of “adaptive therapeutics” refers to:
A. Fixed treatment regimens that never change
B. Continuously monitoring cancer evolution and adjusting treatment accordingly
C. Using only traditional chemotherapy
D. Avoiding all forms of cancer treatment
31. According to the passage, algorithmic bias can arise through:
A. Only one specific pathway
B. Too much diverse data
C. Multiple pathways including biased data collection and historical discrimination
D. Excessive algorithmic transparency
Questions 32-36: Matching Features
Match each challenge (32-36) with the correct description (A-H).
Challenges:
32. Interpretability challenge
33. Dataset shift
34. Spurious correlations
35. Algorithmic bias
36. Regulatory uncertainty
Descriptions:
A. Statistical properties of data change over time or across populations
B. AI systems perform less accurately for underrepresented demographic groups
C. Algorithms detecting equipment differences rather than disease features
D. The mechanistic reasoning behind AI recommendations is often opaque
E. Patients refusing all AI-based treatments
F. Traditional regulatory frameworks don’t fit AI’s dynamic nature
G. All AI systems produce identical results
H. Doctors never using 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 does AI use to track cancer’s evolution in real time?
38. In psychiatry, AI can identify biologically distinct subtypes by analyzing neuroimaging data and what captured through smartphone sensors?
39. What technology combined with AI could enable bespoke genetic interventions for individual patients?
40. What type of medicine involves systems that predict and prevent disease rather than just reacting to it?
3. Answer Keys – Đáp Án
PASSAGE 1: Questions 1-13
- B
- C
- B
- C
- C
- TRUE
- FALSE
- NOT GIVEN
- TRUE
- precision
- genetic profile
- atrial fibrillation
- adapt
PASSAGE 2: Questions 14-26
- YES
- NO
- NO
- YES
- NO
- ii
- iv
- iii
- vi
- genomic information
- medical databases
- augmented intelligence
- black boxes
PASSAGE 3: Questions 27-40
- B
- C
- C
- B
- C
- D
- A
- C
- B
- F
- circulating tumor DNA
- behavioral digital phenotypes / digital phenotypes
- CRISPR / gene editing technologies
- anticipatory medicine
4. Giải Thích Đáp Án Chi Tiết
Passage 1 – Giải Thích
Câu 1: B
- Dạng câu hỏi: Multiple Choice
- Từ khóa: main difference, traditional medicine, personalized medicine
- Vị trí trong bài: Đoạn A, dòng 2-4
- Giải thích: Bài đọc nói rõ “Traditional medicine often takes a one-size-fits-all approach, where patients with similar symptoms receive identical treatments” trong khi personalized medicine điều chỉnh điều trị theo từng cá nhân. Đáp án B paraphrase ý này chính xác nhất.
Câu 2: C
- Dạng câu hỏi: Multiple Choice
- Từ khóa: AI do, human doctors cannot
- Vị trí trong bài: Đoạn C, dòng 3-5
- Giải thích: Bài viết nói “A human doctor would need years to analyze all this information for just one patient, but AI can process millions of data points in seconds.” Đây là khả năng mà bác sĩ không thể làm được.
Câu 3: B
- Dạng câu hỏi: Multiple Choice
- Từ khóa: AI improved cancer treatment
- Vị trí trong bài: Đoạn D, dòng 3-6
- Giải thích: “AI systems can analyze a tumor’s genetic profile and recommend targeted therapies that attack cancer cells while leaving healthy cells relatively unharmed” – đáp án B paraphrase chính xác ý này.
Câu 4: C
- Dạng câu hỏi: Multiple Choice
- Từ khóa: concern, AI, personalized medicine
- Vị trí trong bài: Đoạn H, dòng 1-2
- Giải thích: “Privacy concerns are paramount, as personalized medicine requires access to sensitive genetic and health information” – privacy/bảo mật là mối quan tâm chính được đề cập.
Câu 6: TRUE
- Dạng câu hỏi: True/False/Not Given
- Từ khóa: AI systems, identify genetic markers, patients respond to drugs
- Vị trí trong bài: Đoạn C, dòng 6-8
- Giải thích: “Machine learning algorithms can identify patterns that might be invisible to the human eye, such as subtle genetic markers that indicate how a patient will respond to a specific drug” – thông tin khớp hoàn toàn.
Câu 7: FALSE
- Dạng câu hỏi: True/False/Not Given
- Từ khóa: developing new drugs, less than five years
- Vị trí trong bài: Đoạn E, dòng 2
- Giải thích: “Traditionally, developing a new medication takes over a decade” – hơn 10 năm, không phải dưới 5 năm, nên là FALSE.
Câu 9: TRUE
- Dạng câu hỏi: True/False/Not Given
- Từ khóa: AI diagnostic tools, detect diseases earlier, human doctors
- Vị trí trong bài: Đoạn F, dòng 2-3
- Giải thích: “Some AI systems can now detect diseases earlier and more accurately than human doctors” – khớp chính xác với câu hỏi.
Câu 10: precision
- Dạng câu hỏi: Sentence Completion
- Từ khóa: Personalized medicine, also known as
- Vị trí trong bài: Đoạn B, dòng 1
- Giải thích: “Personalized medicine, also known as precision medicine” – lấy từ “precision” từ bài.
Câu 12: atrial fibrillation
- Dạng câu hỏi: Sentence Completion
- Từ khóa: irregular heartbeats, might indicate
- Vị trí trong bài: Đoạn G, dòng 5-6
- Giải thích: “some devices can detect irregular heartbeats that might indicate atrial fibrillation” – lấy cụm “atrial fibrillation”.
Passage 2 – Giải Thích
Câu 14: YES
- Dạng câu hỏi: Yes/No/Not Given
- Từ khóa: understanding Human Genome Project data, more challenging
- Vị trí trong bài: Đoạn A, dòng 2-3
- Giải thích: “While the completion of the Human Genome Project in 2003 was a landmark achievement, understanding what all those genetic sequences actually mean… has proven to be an even greater challenge” – tác giả đồng ý với quan điểm này.
Câu 15: NO
- Dạng câu hỏi: Yes/No/Not Given
- Từ khóa: trial-and-error approach, safest method
- Vị trí trong bài: Đoạn B, dòng 2-4
- Giải thích: Tác giả nói “trial-and-error approach… is not only inefficient but can also be dangerous” – rõ ràng không phải phương pháp an toàn nhất, nên là NO.
Câu 16: NO
- Dạng câu hỏi: Yes/No/Not Given
- Từ khóa: AI, predict warfarin dosage, 100% accuracy
- Vị trí trong bài: Đoạn C, dòng 5-6
- Giải thích: Bài nói “over 90% accuracy” chứ không phải 100%, nên là NO.
Câu 17: YES
- Dạng câu hỏi: Yes/No/Not Given
- Từ khóa: Multi-omic integration, computational approaches, only AI
- Vị trí trong bài: Đoạn E, dòng 3-5
- Giải thích: “understanding how they interact requires computational approaches that only AI can provide” – tác giả khẳng định rõ ràng.
Câu 19: ii (Personalized dosing for blood-thinning medications)
- Dạng câu hỏi: Matching Headings
- Vị trí trong bài: Đoạn C
- Giải thích: Đoạn này tập trung vào warfarin, một loại thuốc làm loãng máu, và cách AI dự đoán liều lượng phù hợp cho từng bệnh nhân.
Câu 20: iv (Simulating treatment combinations for cancer patients)
- Dạng câu hỏi: Matching Headings
- Vị trí trong bài: Đoạn D
- Giải thích: Đoạn này nói về “AI systems… can simulate thousands of potential treatment combinations” trong điều trị ung thư.
Câu 23: genomic information
- Dạng câu hỏi: Summary Completion
- Từ khóa: clinical decision support systems combine
- Vị trí trong bài: Đoạn G, dòng 1-2
- Giải thích: “Clinical decision support systems powered by AI… integrate genomic information with real-time clinical data.”
Câu 25: augmented intelligence
- Dạng câu hỏi: Summary Completion
- Từ khóa: approach called
- Vị trí trong bài: Đoạn G, dòng 5-6
- Giải thích: “This augmented intelligence—combining human clinical judgment with AI’s analytical capabilities.”
Passage 3 – Giải Thích
Câu 27: B
- Dạng câu hỏi: Multiple Choice
- Từ khóa: traditional medical practice, based primarily on
- Vị trí trong bài: Đoạn A, dòng 2-3
- Giải thích: “Traditional medical practice has long been grounded in population-level statistics and probabilistic reasoning” – đáp án B paraphrase chính xác.
Câu 28: C
- Dạng câu hỏi: Multiple Choice
- Từ khóa: deep learning architectures
- Vị trí trong bài: Đoạn B, dòng 7-9
- Giải thích: “moving from low-level features to increasingly abstract concepts in ways that mirror (albeit crudely) the human brain’s information processing” – từ “crudely” cho thấy đây là sự mô phỏng thô.
Câu 29: C
- Dạng câu hỏi: Multiple Choice
- Từ khóa: intra-tumoral heterogeneity, significant
- Vị trí trong bài: Đoạn C, dòng 4-5
- Giải thích: “This intra-tumoral heterogeneity poses profound challenges for treatment selection” – đáp án C khớp chính xác.
Câu 30: B
- Dạng câu hỏi: Multiple Choice
- Từ khóa: adaptive therapeutics
- Vị trí trong bài: Đoạn D, dòng 2-6
- Giải thích: Đoạn mô tả “continuously monitor circulating tumor DNA… tracking the clonal evolution… recommend pre-emptive modification” – điều chỉnh điều trị liên tục dựa trên sự tiến hóa của ung thư.
Câu 32: D (Interpretability challenge – The mechanistic reasoning behind AI recommendations is often opaque)
- Dạng câu hỏi: Matching Features
- Vị trí trong bài: Đoạn G, dòng 2-3
- Giải thích: “the mechanistic reasoning underlying that recommendation is often opaque” khớp với mô tả về interpretability challenge.
Câu 33: A (Dataset shift – Statistical properties of data change over time or across populations)
- Dạng câu hỏi: Matching Features
- Vị trí trong bài: Đoạn H, dòng 3-4
- Giải thích: “‘dataset shift’—where the statistical properties of data change over time or across different populations” – định nghĩa trực tiếp trong bài.
Câu 37: circulating tumor DNA
- Dạng câu hỏi: Short-answer Question
- Từ khóa: AI use, track cancer evolution, real time
- Vị trí trong bài: Đoạn D, dòng 3
- Giải thích: “AI systems can continuously monitor circulating tumor DNA in the bloodstream, tracking the clonal evolution of the cancer in real time.”
Câu 38: behavioral digital phenotypes / digital phenotypes
- Dạng câu hỏi: Short-answer Question
- Từ khóa: psychiatry, smartphone sensors
- Vị trí trong bài: Đoạn E, dòng 4-5
- Giải thích: “behavioral digital phenotypes captured through smartphone sensors” – có thể viết đầy đủ hoặc rút gọn thành “digital phenotypes”.
Câu 40: anticipatory medicine
- Dạng câu hỏi: Short-answer Question
- Từ khóa: systems predict and prevent disease
- Vị trí trong bài: Đoạn L, dòng 1
- Giải thích: “The ultimate trajectory points toward what might be termed ‘anticipatory medicine’—systems that don’t merely react to disease but predict and prevent it.”
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 |
|---|---|---|---|---|---|
| revolutionize | v | /ˌrevəˈluːʃənaɪz/ | cách mạng hóa, thay đổi hoàn toàn | AI has begun to revolutionize the way doctors diagnose patients | revolutionize the industry/field |
| genetic makeup | n | /dʒəˈnetɪk ˈmeɪkʌp/ | cấu trúc di truyền | different genetic makeups influence drug response | unique genetic makeup |
| precision medicine | n | /prɪˈsɪʒən ˈmedsɪn/ | y học chính xác | Precision medicine aims to tailor treatment to individuals | practice precision medicine |
| minimize | v | /ˈmɪnɪmaɪz/ | giảm thiểu | This approach minimizes side effects | minimize risk/damage |
| data points | n | /ˈdeɪtə pɔɪnts/ | điểm dữ liệu | AI can process millions of data points in seconds | collect data points |
| machine learning | n | /məˈʃiːn ˈlɜːnɪŋ/ | học máy | Machine learning algorithms identify patterns | apply machine learning |
| tumor | n | /ˈtjuːmə(r)/ | khối u | AI can analyze a tumor’s genetic profile | malignant tumor |
| survival rate | n | /səˈvaɪvl reɪt/ | tỷ lệ sống sót | This has led to improved survival rates | increase survival rates |
| accelerate | v | /əkˈseləreɪt/ | đẩy nhanh, tăng tốc | AI can accelerate the drug development process | accelerate the process |
| diagnostic tool | n | /ˌdaɪəɡˈnɒstɪk tuːl/ | công cụ chẩn đoán | AI-powered diagnostic tools are becoming sophisticated | advanced diagnostic tool |
| augment | v | /ɔːɡˈment/ | tăng cường, bổ sung | These tools augment doctors’ capabilities | augment human abilities |
| accessibility | n | /əkˌsesəˈbɪləti/ | khả năng tiếp cận | There’s the question of accessibility to new technology | improve accessibility |
Passage 2 – Essential Vocabulary
| Từ vựng | Loại từ | Phiên âm | Nghĩa tiếng Việt | Ví dụ từ bài | Collocation |
|---|---|---|---|---|---|
| convergence | n | /kənˈvɜːdʒəns/ | sự hội tụ, kết hợp | The convergence of AI and genomics | technological convergence |
| profound | adj | /prəˈfaʊnd/ | sâu sắc, to lớn | profound transformations in medicine | profound impact/effect |
| decipher | v | /dɪˈsaɪfə(r)/ | giải mã | enabling researchers to decipher complex relationships | decipher the code |
| pharmacogenomics | n | /ˌfɑːməkəʊdʒɪˈnɒmɪks/ | dược lý học di truyền | Pharmacogenomics has emerged as fertile ground for AI | field of pharmacogenomics |
| adverse | adj | /ˈædvɜːs/ | bất lợi, có hại | Adverse drug reactions cause deaths | adverse effects/reactions |
| dosage | n | /ˈdəʊsɪdʒ/ | liều lượng | The appropriate dosage varies between individuals | correct dosage |
| paradigm shift | n | /ˈpærədaɪm ʃɪft/ | sự thay đổi mô hình/tư duy | This represents a paradigm shift in medicine | undergo a paradigm shift |
| therapeutic | adj | /ˌθerəˈpjuːtɪk/ | thuộc về điều trị | determining the most effective therapeutic strategy | therapeutic benefits |
| oncology | n | /ɒŋˈkɒlədʒi/ | ung thư học | In oncology, treatment is complex | field of oncology |
| efficacy | n | /ˈefɪkəsi/ | hiệu quả (của thuốc/điều trị) | predict their likely efficacy and toxicity | proven efficacy |
| longitudinal | adj | /ˌlɒndʒɪˈtjuːdɪnl/ | theo chiều dọc, theo thời gian | generates longitudinal data streams | longitudinal study/data |
| interpretability | n | /ɪnˌtɜːprɪtəˈbɪləti/ | khả năng giải thích | The interpretability of AI decisions remains a concern | improve interpretability |
| bias | n | /ˈbaɪəs/ | sự thiên lệch, định kiến | representational bias in databases | unconscious bias |
| exacerbate | v | /ɪɡˈzæsəbeɪt/ | làm trầm trọng thêm | could exacerbate existing health disparities | exacerbate the problem |
| trajectory | n | /trəˈdʒektəri/ | quỹ đạo, xu hướng phát triển | the trajectory is clear for AI in medicine | upward trajectory |
Bảng từ vựng quan trọng về trí tuệ nhân tạo và y học cá nhân hóa cho IELTS Reading
Passage 3 – Essential Vocabulary
| Từ vựng | Loại từ | Phiên âm | Nghĩa tiếng Việt | Ví dụ từ bài | Collocation |
|---|---|---|---|---|---|
| ascendancy | n | /əˈsendənsi/ | sự lên ngôi, thống trị | The ascendancy of AI in medicine | gain ascendancy |
| epistemological | adj | /ɪˌpɪstəməˈlɒdʒɪkl/ | thuộc về nhận thức luận | represents an epistemological shift | epistemological framework |
| probabilistic | adj | /ˌprɒbəbɪˈlɪstɪk/ | theo xác suất | grounded in probabilistic reasoning | probabilistic model |
| granular | adj | /ˈɡrænjələ(r)/ | chi tiết, tỉ mỉ | insufficiently granular for molecular precision | granular analysis |
| heterogeneous | adj | /ˌhetərəˈdʒiːniəs/ | không đồng nhất, đa dạng | process vast heterogeneous datasets | heterogeneous population |
| calibrated | v | /ˈkælɪbreɪtɪd/ | được điều chỉnh chính xác | therapeutic decisions are calibrated to individuals | carefully calibrated |
| algorithmic | adj | /ˌælɡəˈrɪðmɪk/ | thuộc về thuật toán | The algorithmic architecture underlying this | algorithmic approach |
| supervised learning | n | /ˈsuːpəvaɪzd ˈlɜːnɪŋ/ | học có giám sát | supervised machine learning involves training models | apply supervised learning |
| non-linear | adj | /nɒn ˈlɪniə(r)/ | phi tuyến tính | identifies complex non-linear relationships | non-linear pattern |
| oncological | adj | /ˌɒŋkəˈlɒdʒɪkl/ | thuộc về ung thư học | application to oncological therapeutics | oncological treatment |
| genomic instability | n | /dʒɪˈnəʊmɪk ˌɪnstəˈbɪləti/ | sự bất ổn định gen | Cancer is a disease of genomic instability | chromosomal/genomic instability |
| heterogeneity | n | /ˌhetərəʊdʒəˈniːəti/ | tính không đồng nhất | intra-tumoral heterogeneity poses challenges | genetic heterogeneity |
| histopathological | adj | /ˌhɪstəʊˌpæθəˈlɒdʒɪkl/ | thuộc về mô bệnh học | than traditional histopathological assessment | histopathological examination |
| clonal evolution | n | /ˈkləʊnl ˌiːvəˈluːʃn/ | sự tiến hóa dòng tế bào | tracking the clonal evolution of cancer | study clonal evolution |
| pre-emptive | adj | /priˈemptɪv/ | phòng ngừa, tiên phát | recommend pre-emptive modification of treatment | pre-emptive action/strike |
| neurochemical | adj | /ˌnjʊərəʊˈkemɪkl/ | thuộc về hóa thần kinh | different neurochemical dysfunction | neurochemical changes |
| epitope | n | /ˈepɪtəʊp/ | biểu mô (phần kháng nguyên) | predict which epitopes will generate immune responses | antigenic epitope |
| opaque | adj | /əʊˈpeɪk/ | mờ đục, khó hiểu | the mechanistic reasoning is often opaque | remain opaque |
| spurious | adj | /ˈspjʊəriəs/ | giả, không thực | learning spurious correlations | spurious relationship |
| pathophysiology | n | /ˌpæθəʊˌfɪziˈɒlədʒi/ | sinh lý bệnh học | understanding disease pathophysiology | study pathophysiology |
| bespoke | adj | /bɪˈspəʊk/ | được thiết kế riêng | creation of bespoke genetic interventions | bespoke solution/service |
| anticipatory | adj | /ænˈtɪsɪpətri/ | dự đoán trước | points toward anticipatory medicine | anticipatory approach |
| reconceptualization | n | /ˌriːkənˌseptʃuəlaɪˈzeɪʃn/ | sự tái khái niệm hóa | represents a fundamental reconceptualization | require reconceptualization |
Kết Bài
Chủ đề “How artificial intelligence is improving personalized medicine” không chỉ phản ánh xu hướng phát triển mạnh mẽ của ngành y tế hiện đại mà còn là một trong những chủ đề phổ biến trong kỳ thi IELTS Reading những năm gần đây. Việc nắm vững kiến thức và từ vựng liên quan đến công nghệ AI trong y học sẽ giúp bạn tự tin hơn khi gặp các bài đọc tương tự trong kỳ thi chính thức.
Bộ đề thi mẫu này đã cung cấp cho bạn 3 passages hoàn chỉnh với độ khó tăng dần từ Easy (Band 5.0-6.5) qua Medium (Band 6.0-7.5) đến Hard (Band 7.0-9.0), giống như cấu trúc của bài thi IELTS Reading thực tế. Với tổng cộng 40 câu hỏi đa dạng bao gồm 7 dạng câu hỏi khác nhau – Multiple Choice, True/False/Not Given, Yes/No/Not Given, Matching Headings, Summary Completion, Sentence Completion và Short-answer Questions – bạn đã được luyện tập toàn diện các kỹ năng cần thiết.
Đáp án chi tiết kèm giải thích đã chỉ ra cách tìm thông tin trong bài, kỹ thuật paraphrase mà đề thi thường sử dụng, và vị trí chính xác của thông tin trong từng passage. Điều này giúp bạn không chỉ biết đáp án đúng mà còn hiểu tại sao đó là đáp án đúng và phát triển tư duy phân tích cần thiết cho IELTS Reading.
Hơn 40 từ vựng quan trọng được trình bày chi tiết với phiên âm, nghĩa tiếng Việt, ví dụ thực tế và collocations sẽ giúp bạn xây dựng vốn từ vựng học thuật vững chắc. Những từ vựng này không chỉ hữu ích cho phần Reading mà còn có thể áp dụng trong Writing Task 2 khi viết về các chủ đề liên quan đến công nghệ, y tế và khoa học.
Hãy nhớ rằng, thành công trong IELTS Reading không chỉ đến từ việc làm nhiều bài tập mà còn từ việc phân tích kỹ đáp án, hiểu rõ cách đề thi được xây dựng, và rèn luyện các kỹ thuật làm bài một cách bài bản. Đừng quên quản lý thời gian hiệu quả: 15-17 phút cho Passage 1, 18-20 phút cho Passage 2, và 23-25 phút cho Passage 3, để đảm bảo bạn có đủ thời gian hoàn thành cả 40 câu hỏi.
Chúc bạn ôn tập hiệu quả và đạt được band điểm mong muốn trong kỳ thi IELTS sắp tới!
