Kediska Baarayaasha Isdhexgalka Dawada AI

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

1. Sidee baa is dhexgalka xeer-dhaqameedku u go'aansadaa inuu rido digniinta?
   • It predicts the next likely word in a sentence
   • It matches drug pairs against entries curated by clinical editors
   • It asks the patient to rate their symptoms
   • It searches social media for complaints
2. Muxuu haguhu u sheegay in chatbot-ka guud uu si kalsooni leh u qeexi karo isdhexgalka aan jirin?
   • It is always connected to Micromedex
   • It generates statistically likely text rather than looking up a curated database
   • It deliberately exaggerates for safety
   • Interaction data is secret
3. Clarithromycin kor u qaadista heerarka simvastatin waa tusaale noocee isdhexgalka?
   • Pharmacodynamic
   • Pharmacokinetic
   • Food allergy
   • Placebo effect
4. SSRI oo ay weheliso tramadol oo kor u qaadaysa halista xanuunka serotonin waa maxay tusaale ahaan?
   • A pharmacokinetic interaction only
   • A food interaction
   • A pharmacodynamic interaction where effects add together
   • An interaction databases never include
5. Waa maxay doorka tusaalaha cilmi-baarista sida DeepDDI marka loo eego hagaha?
   • It replaces pharmacy alert systems
   • It predicts possible interactions from drug features as hypotheses for study
   • It prints patient medication labels
   • It checks insurance coverage
6. Which known weakness of rule-based checkers leads clinicians to ignore alerts?
   • Aad bay u gaabiyaan inay ordaan
   • Alert fatigue from many alerts that get overridden
   • They cannot store drug names
   • Waxay ku shaqeeyaan oo kaliya offline
7. Why did the chatbot in the St. John's wort example miss the interaction?
   • St. John's wort has no interactions
   • The supplement was never included in the medication list it was given
   • Chatbots cannot read the letter S
   • The pharmacist hid the information
8. What does a retrieval-augmented design do differently from a plain chatbot?
   • It removes all citations
   • It first queries a curated database, then uses the language model to explain the retrieved entries
   • It trains a new model for every question
   • It relies only on the model's memory