Medicine as an Information Industry in the Age of Language Models

AbstractMedicine is fundamentally an information enterprise in which clinicians integrate patient data with prior knowledge and external sources to guide decisions. Over time, the delivery of medical knowledge has evolved from apprenticeship and print to digital search, improving access but not necessarily efficiency. The emergence of large language models (LLMs) marks a qualitative shift - from retrieval to synthesis - enabling rapid, contextually tailored responses to clinical questions. This transformation offers substantial gains in efficiency, but also introduces new epistemic risks. LLMs generate fluent, authoritative-seeming outputs based on statistical patterns rather than true understanding, and they have limitations in reasoning, calibration, and transparency. As a result, distinguishing evidence-based conclusions from plausible inferences becomes challenging. This shift redefines the role of clinicians and medical journals, which now function both as curators of validated knowledge and as upstream inputs to AI systems. Ensuring safe integration will require preserving critical appraisal, accountability, and standards of evidence in LLM-mediated clinical decision support.