Clinical AI and Precision Medicine: Philosophical Questions About Labels, Disease, and Evidence

Precision medicine is often framed as a move from “average-patient” care toward prevention, diagnosis, and treatment tailored to individual variability. This aspiration has been articulated in major policy and scientific calls for a new evidence-generating health system and a new taxonomy of disease [ 1 , 2 ]. Yet, the limiting factors are not only technical; in practice, clinicians repeatedly operationalize contested notions, such as disease, risk, diagnosis, and evidence, as if they were stable clinical elements. As clinical care is increasingly captured in electronic data systems, including electronic health records, imaging platforms, wearable devices, and large language model outputs, these data elements increasingly define the targets used to train and evaluate clinical AI models [ 2 ].