As the development of medicine accelerates in the age of COVID-19, the methods and processes through which we conduct the most basic of clinical activities – diagnosing and treating patients – must advance as well.
Although machine learning algorithms show great potential in optimizing patient care, imaginative or associative patterns of learning are necessary in order for such learning algorithms to replicate the cognitive abilities of physicians.
Semaglutide has moved beyond its original indication and now sits at the center of a widening set of clinical questions: cardiovascular risk, kidney disease progression, and even neurodegeneration. The question is no longer whether the drug lowers glucose or reduces weight—it does—but how far those effects extend across systems, and whether evidence from one population can be translated into another without distortion. Large, well-powered trials have produced consistent signals, yet those signals are now being applied in contexts that were...
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