Quiz połączony z przewodnikiem · Średni Poziom
Błąd automatyzacji w quizie dotyczącym sztucznej inteligencji klinicznej
Sprawdza błędy wynikające z zaniedbań w porównaniu z prowizją, dowody z badań mammograficznych i prób winietowych oraz nawyki, które zapewniają niezależność lekarzy.
Pytanie 1 z 8
Radiolog przegapia subtelną asymetrię, ponieważ AI nie umieściło tam oznaczenia, choć sam by ją zauważył. Jaki to rodzaj błędu automatyzacji?
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Odpowiedzi i wyjaśnienia pojawiają się w miarę rozwiązywania quizu.
- Radiolog przegapia subtelną asymetrię, ponieważ AI nie umieściło tam oznaczenia, choć sam by ją zauważył. Jaki to rodzaj błędu automatyzacji?
- An error of commission
- Algorithm aversion
- Alert fatigue
- An error of omission
- Co wykazało badanie z 2023 roku w czasopiśmie Radiology, gdy radiolodzy otrzymywali celowo błędne sugestie oznaczone przez AI na mammogramach?
- Accuracy was unchanged for every reader
- Accuracy fell, and less experienced readers were affected most
- Only very experienced readers were affected
- Readers ignored every suggestion
- According to the guide's summary of a 2023 JAMA vignette study, what happened when explanations were added to a systematically biased model?
- Explanations did little to offset the drop in diagnostic accuracy
- Explanations fully eliminated the bias
- Clinicians refused to read the explanations
- Accuracy exceeded the no-AI baseline
- After weeks of false sepsis alerts, a physician dismisses all of them, including a correct one. What does this illustrate?
- An error of commission caused by over-trust
- Sequential reading
- Under-trust, seen in hospitals as alert fatigue
- Well-calibrated reliance
- Which workflow design does the guide say best preserves independent judgment?
- Showing the AI's answer before the clinician looks at the case
- Hiding all confidence scores
- Disabling overrides
- Sequential reading, where the clinician forms a judgment before the AI output is revealed
- A department finds that clinicians accept nearly 100 percent of an AI tool's suggestions. How does the guide suggest interpreting this?
- It proves the tool is perfect
- It may signal rubber-stamping and warrants auditing a sample of accepted cases
- It means clinicians distrust the tool
- It has no safety meaning
- Why does the guide say that having a human in the loop does not guarantee safety?
- If the human reliably defers to the system, the loop adds little protection
- Humans are always less accurate than AI
- Regulations prohibit human review
- Humans cannot see AI outputs
- Which personal habit does the guide recommend for staying independent of AI suggestions?
- Treat the absence of an alert as proof that nothing is wrong
- Accept high-confidence outputs without review
- Write down your own hypothesis before looking at the AI output
- Rely on explanations to decide whether the output is right