| 1 |
An AI model for depression detection shows high accuracy in laboratory testing but performs poorly in community clinics. Based on the article, what is the most likely explanation?
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2. The model was trained on non-representative datasets |
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AI models often perform well in labs but poorly in real clinics because the training data do not represent real community populations. |
This is explained by generalizability theory. |
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| 2 |
Which scenario best illustrates the ethical risk of over-reliance on AI in mental health care?
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3. AI making autonomous decisions without human oversight |
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The article warns that using AI without doctors can cause ethical and safety problems. |
This follows the human-in-the-loop. which requires human control in mental health care. |
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| 3 |
If an AI mental health tool consistently underdiagnoses symptoms in minority populations, which corrective strategy aligns best with the article’s recommendations?
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2. Retraining the model using more diverse datasets |
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| 4 |
Why does the article argue that AI tools are more suitable for screening rather than standalone intervention in mental health?
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3. Mental health conditions are dynamic and context-dependent |
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Mental health changes over time and depends on personal context, which AI alone can’t understand. |
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| 5 |
Which AI feature would most increase clinician trust, according to the article?
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3. Explainable decision pathways |
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| 6 |
What unintended consequence may arise if AI mental health apps are widely adopted without regulation?
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2. Increased stigma due to data misuse |
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The article highlights that misuse of sensitive mental health data can increase stigma. |
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| 7 |
Which model of care best aligns with the authors’ proposed future of AI in mental health?
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3. Human–AI collaborative decision-making |
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The authors support AI as a tool that works together with doctors. |
This follows the human–AI collaboration model in healthcare. |
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| 8 |
Why might an AI system incorrectly label normal stress as a mental disorder?
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2. Absence of physiological data |
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| 9 |
Which research focus would most effectively improve clinical adoption of AI mental health tools?
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2. Enhancing data privacy frameworks |
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| 10 |
From a public health perspective, what is the strongest justification for integrating AI into mental health systems?
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3. Expanding access and early detection |
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AI can help reach underserved groups and detect mental health problems early. |
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| 11 |
Why are mRNA vaccines particularly suited for rapidly mutating respiratory viruses?
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3. Their sequences can be rapidly updated |
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The article explains that mRNA sequences can be quickly changed to match new virus variants. |
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| 12 |
What would most likely happen if lipid nanoparticles failed to protect mRNA during delivery?
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3. Rapid degradation of mRNA |
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| 13 |
Which limitation most restricts global deployment of current mRNA vaccines?
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2. Cold-chain storage requirements |
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The article states that strict cold storage limits global vaccine access. |
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| 14 |
Why does self-amplifying mRNA (saRNA) reduce required vaccine doses?
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2. It replicates within host cells |
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| 15 |
Which feature makes circular mRNA vaccines potentially transformative?
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3. Enhanced stability without a cold chain |
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| 16 |
Why might respiratory-route vaccine administration improve protection against respiratory viruses?
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2. It targets mucosal immunity at infection sites |
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| 17 |
What is the primary advantage of combination mRNA vaccines?
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3. Protection against multiple pathogens in one dose |
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Research : Multivalent Vaccine Strategies in Battling the Emergence of COVID-19 Variants |
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| 18 |
Why is post-market surveillance especially important for mRNA vaccines?
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2. Rare adverse events may emerge over time |
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Some side effects appear only after widespread use. |
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| 19 |
Which trend best reflects the future direction of mRNA vaccine research described in the article?
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3. Multivalent and broadly protective vaccines |
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| 20 |
From a global health perspective, which improvement would most enhance equity in mRNA vaccine access?
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4. Increased patent protection |
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