| 1 |
According to the article, which factor most strongly motivates the increasing use of artificial intelligence in mental health care?
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2. Shortage of trained mental health professionals and unequal access to care |
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AI is used more in mental health care because there are not enough mental health workers. Many people, especially in poor countries, they can’t get help when they need it.
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Gap between demand and supply
- Many people need mental health services.
Using technology to improve the access
- Not enough professionals trained for that services.
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| 2 |
Which application of AI in mental health is primarily described as using passive data from smartphones and wearable devices?
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2. Digital phenotyping |
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Digital phenotyping uses data from smartphones and wearable devices, such as movement, sleep, and phone use. AI use this data to detect changes and mental health without asking users to answer.
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Research : Digital Phenotyping for Monitoring Mental Disorders
This review explains how digital phenotyping uses data from smartphone interactions to monitor mental health and predict symptom changes in mental disorders.
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| 3 |
What is a major methodological limitation of many AI-based mental health studies identified by the authors?
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3. 3. Small, homogeneous datasets that limit generalizability of qualitative research methods |
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Many AI mental health studies use small and similar datasets. This makes the results hard to apply to different people.
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Research in mental health AI shows that small and homogeneous samples reduce generalizability. AI models need large and diverse data to work well in settings.
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| 4 |
Why is data privacy a particularly critical concern in AI-driven mental health applications?
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3. Mental health data are highly sensitive and risk misuse or stigma |
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Mental health data are very personal. If the data are leaked or misused, they can cause stigma or discrimination.
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Ethical research shows that sensitive personal data need strong privacy protection, especially in mental health AI.
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| 5 |
According to the article, how should AI systems ideally function within mental health care?
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3. As supportive tools that augment clinical decision-making |
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AI should support mental health professionals, not replace them. AI can help analyze data, provide suggestions, but people are still responsible for diagnosis and treatment decisions. Human is important because mental health care involves emotions and personal contexts.
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the HITL theory, AI should work together with humans. In healthcare research, this approach ensures safety, accountability, and ethical decision-making by keeping clinicians involved in the final decisions.
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| 6 |
What is the primary source of algorithmic bias in AI mental health tools discussed in the article?
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3. Biased or unrepresentative training data |
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The article explains that AI mental health tools often learn from data that come from limited or similar groups of people. If the data do not represent different ages, cultures, or social backgrounds, the AI system may give unfair or inaccurate results.
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AI systems reflect the data used to train them. If the data are biased, the AI output will also be biased, which can affect fairness in healthcare.
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| 7 |
Which future direction is most strongly recommended by the authors for advancing AI in mental health?
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2. Increasing transparency and explainability of AI models |
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Doctors and patients need to understand how AI reaches its decisions in order to trust and use these systems safely in mental health care.
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Explainable AI (XAI) theory. XAI focuses on making AI decisions understandable to humans, which is especially important in healthcare where decisions can strongly affect patients’ lives.
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| 8 |
Why do the authors caution against overinterpreting AI-based mental health predictions as definitive diagnoses?
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3. Predictions may not capture clinical context and individual variability |
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The article warns that AI predictions should not be treated as final diagnoses. AI systems may miss personal history, life experiences, or cultural factors that doctors consider during diagnosis.
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diagnosis requires professional experience, patient interaction, and understanding of individual differences, not only data-based predictions.
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| 9 |
How might AI unintentionally widen mental health disparities, according to the article?
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2. By relying on data primarily from high-income populations |
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healthcare tools should work fairly for all populations. Research shows that unbalanced data can worsen existing health disparities.
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| 10 |
Which statement best reflects the authors’ overall conclusion regarding AI in mental health?
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3. AI offers significant potential but requires careful, ethical integration |
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AI has strong potential to improve mental health care, such as improving access and supporting doctors. However, ethical issues like privacy, bias, and safety must be carefully managed.
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| 11 |
What is the primary function of mRNA in respiratory virus mRNA vaccines?
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2. To instruct host cells to produce viral antigens |
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mRNA vaccines deliver instructions into host cells so that the cells produce antigens that trigger the immune system.
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| 12 |
Why is antigen design a critical step in mRNA vaccine development?
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2. It affects the immune system’s ability to recognize and respond to the virus |
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the immune system must recognize specific antigen structures to develop an effective protective response. The article describes how antigen design and optimization affect vaccine performance.
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| 13 |
Why are lipid nanoparticles essential in mRNA vaccine formulations?
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3. They protect mRNA and facilitate cellular uptake |
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The paper discusses how delivery systems like lipid nanoparticles are needed because mRNA is fragile and cannot easily enter cells by itself.
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| 14 |
Which immune response is directly activated when host cells express antigens encoded by mRNA vaccines?
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3. Adaptive immunity involving T and B cells |
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When cells produce viral proteins, the immune system activates T cells and B cells to fight the virus and build memory.
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| 15 |
What is a key objective of clinical studies for respiratory virus mRNA vaccines?
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2. To confirm immunogenicity and safety in humans |
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There is testing safety and effectiveness before wide use.
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| 16 |
Which challenge related to mRNA vaccines is highlighted in the article?
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2. Instability of mRNA requiring strict storage conditions |
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| 17 |
Compared with traditional vaccines, what advantage of mRNA vaccines is emphasized?
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3. Rapid design and manufacturing flexibility |
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| 18 |
Why is long-term safety monitoring important for mRNA vaccines, according to the article?
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2. Rare adverse effects may only appear over time |
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The review highlights that mRNA vaccines have shorter development cycles and simpler production.
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| 19 |
What future challenge in respiratory virus mRNA vaccine development is emphasized by the authors?
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2. Addressing viral variants and immune escape |
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The review states respiratory viruses mutate rapidly, and mRNA vaccines must be updated to maintain effectiveness. Designing vaccines that handle these variants is a major future challenge.
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| 20 |
Which statement best summarizes the authors’ perspective on respiratory virus mRNA vaccines?
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3. They represent a transformative platform with ongoing challenges |
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