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.
Clinicians increasingly encounter patients using or requesting peptide-based therapies sourced through compounding pharmacies. The U.S. Food and Drug Administration has identified a subset of bulk drug substances, including certain peptides, that may present significant safety risks when used in compounded formulations. The clinical question is whether these regulatory signals reflect meaningful patient-level risk and how they should influence prescribing behavior. This matters because compounded peptides often sit outside traditional approval pathways, creating uncertainty around quality, dosing consistency, and safety. Understanding...
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