| 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 model was trained on non representative datasets, leading to poor generalization in real world community clinics |
Statistical learning theory: AI models train on non representative data overfit and fail to generalize to real world settings |
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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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Risk rises when AI make autonomous mental health decisions without human oversight making it unsafe |
Human in the loop: AI in mental health must support not replace human judgement |
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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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To reduce bias and improve diagnostic equality |
Algorithmic fairness: correcting biased outcomes through diverse, representative training data to ensure good mental health care |
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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 conditions are dynamic and context dependent, requiring human judgment for interpretation |
Biopsychosocial model: mental health is a context dependent making it require human clinical reasoning beyond AI |
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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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Increase doctors trust by making AI outputs transparent and clinically interpretable |
XAI: transparent decision pathways increase clinician trusts by making AI outputs interpretable and useful |
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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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Without regulation, widespread AI mental health apps may increase stigma due to misuse or exposing personal data |
Data ethics: misuse of personal data can lead to harmful labeling, discrimination |
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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 author thinks that AI in mental health should be collaborative not replacing doctors decision making |
Augmented intelligence theory: AI functions as a clinical decision support tool |
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| 8 |
Why might an AI system incorrectly label normal stress as a mental disorder?
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3. Overfitting to training data patterns |
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Models apply learned pathological pay too broadly to everyday experiences |
Statistical learning theory: overfitting causes AI to misinterpret normal stress patterns as mental disorders because its limit construct validity |
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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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It improves Clinical adoption by building trusts and ensuring ethical use of AI in mental healthcare |
Trust: holds that data privacy and governance are essential for doctors and patients adapting of AI mental tools |
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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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Because it can scale screening to large populations, enable early identification to mental health risks, reach remote areas |
Public health prevention model: prioritizes expanding access and early detection to reduce mental health burden |
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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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mRNA sequence encoding the antigen can be quickly redesigned when new variants emerge which allows fast response to viral evolution and immune escape |
Platform technology: allows rapid updating of mRNA vaccines to keep pace with evolution |
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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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Without LNPs nanoparticle protection, mRNA will degrade, preventing antigen expression and vaccine effectiveness |
Drug delivery system: unstable molecules like mRNA require protective carriers to prevent degradation |
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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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mRNA vaccines require ultra cold and strict refrigeration, depending on on reliable cold chain infrastructure |
Health systems logistics theory: stringent cold chain requirements need equitable global distribution of mRNA vaccines |
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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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saRNA replicates itself making it produce more copies of antigen encoding RNA which leads to higher antigen expression from a small dose |
Molecular amplification theory: RNA replication produces high antigen expression from minimal initial RNA input |
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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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Their covalently closed structure lacks free ends which makes them more resistant to enzymatic degradation and may reduce or eliminate strict cold chain requirements |
RNA structural stability: eliminating free RNA ends enhances resistance to degradation and improves storage feasibility |
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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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Respiratory virus enters the body through the respiratory mucosa Stimulate local mucosal immunity, especially secretory IgA antibodies, provides first line defense |
mucosal immunity theory: targeting immune responses at respiratory entry sites enhances protection against respiratory viruses |
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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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mRNA vaccines are advantageous because they can Encode multiple antigens in a single formulation, Provide protection against several pathogens or multiple strains at once, Reduce the number of injections needed, Improve vaccination coverage and compliance |
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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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Post market surveillance is especially important for mRNA vaccines because rare or delayed adverse events may only become apparent after widespread, long term 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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mRNA vaccine research as focusing on:
• Multivalent vaccines that target multiple viral strains or viruses in a single formulation, Broadly protective designs to maintain effectiveness despite viral mutation and diversity, Leveraging the flexibility of the mRNA platform to respond to rapidly evolving respiratory viruses |
protective vaccine : multivalent and antigenic ally broad mRNA vaccines to counter viral diversity and evolution |
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| 20 |
From a global health perspective, which improvement would most enhance equity in mRNA vaccine access?
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3. Improved stability at room temperature |
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mRNA vaccines It’s limited by Logistical barriers which Reduce dependence on ultra-cold-chain infrastructure
Enable distribution in low and middle income countries, Lower costs related to storage and transport, Improve reach to remote and underserved populations |
global health equity : removing infrastructure barriers such as cold chain dependence to ensure fair access to vaccines worldwide |
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