AI tools in systematic reviews: what they can and cannot reliably do

Q1. According to the systematic review, which task did AI methods appear to be used for most often in evidence synthesis workflows?
- Study screening
- Data extraction
- Risk-of-bias assessment
- Selective reporting detection
Answer: Study screening[[cite:1]]
Q2. What did the review say about the reported benefits of AI for data extraction?
- They were clearly established and fully reliable
- They were ambiguous, so reliability is not supported
- They were absent because data extraction was never attempted
- They were proven superior to human extraction in every review
Answer: They were ambiguous, so reliability is not supported[[cite:2]]
Q3. What does the review conclude about human involvement in AI-assisted systematic review workflows?
- Extensive human validation still appears required
- Human validation can usually be skipped once screening is automated
- Only one reviewer is needed for final publication
- Human input is unnecessary after training the model
Answer: Extensive human validation still appears required[[cite:3]]
Q4. Which statement best matches the review's overall caution about AI tools in systematic reviews?
- The source names a single AI tool that reliably performs all review tasks on its own
- The source supports reliable automation of screening, extraction, and bias assessment without oversight
- The source does not name any specific AI tool as reliably able to do all systematic-review tasks on its own
- The source concludes that AI has already replaced human reviewers
Answer: The source does not name any specific AI tool as reliably able to do all systematic-review tasks on its own[[cite:4]]
Q5. What did the review say about risk-of-bias assessment?
- It was the most common AI task and was shown to be fully reliable
- Only two methods were reported, and the source does not claim they can do this reliably without oversight
- It was not mentioned at all in the review
- It was shown to outperform human judgment in all cases
Answer: Only two methods were reported, and the source does not claim they can do this reliably without oversight[[cite:5]]
Q6. What is the best quiz answer about AI detection of selective reporting in the attached PMC source?
- The source reports a validated AI method that reliably detects selective reporting
- The source reports high accuracy for detecting selective reporting across all trials
- The attached PMC source was inaccessible because it returned reCAPTCHA/JavaScript content, so the available evidence does not substantiate a conclusion
- The source states that selective reporting detection is fully solved by AI
Answer: The attached PMC source was inaccessible because it returned reCAPTCHA/JavaScript content, so the available evidence does not substantiate a conclusion
Q7. What is the best quiz answer about AI detection of disagreement between evidence and clinical guidelines, and about fine-grained population-extraction failures, based on the attached PMC sources?
- The sources provide enough accessible text to conclude AI reliably detects guideline disagreements and fine-grained population extraction failures
- The sources show AI is unreliable only for guideline disagreements, but not for population extraction
- The attached PMC sources were inaccessible because they returned reCAPTCHA/JavaScript content, so the available evidence does not substantiate a conclusion
- The sources prove these failures occur only in manual review workflows
Answer: The attached PMC sources were inaccessible because they returned reCAPTCHA/JavaScript content, so the available evidence does not substantiate a conclusion

AI tools in systematic reviews: what they can and cannot reliably do

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