Against Disease Classification: Toward Continuous Disease Assessment in Precision Medicine

AbstractMedical machine learning predominantly relies on disease classification systems that reduce complex, continuous disease processes to discrete pathologies. We argue that this paradigm oversimplifies disease reality, perpetuates existing biases, and limits therapeutic insights. We propose a shift toward fine-grained disease assessment through outcome prediction, individualized treatment modeling, and multidimensional disease characterization to better serve the goals of precision medicine.