How this channel uses the term In the material on AI for Science and Medicine, pragmatism (philosophy) names the practical orientation behind the shift the conversations describe: judging diagnostic categories, rules, and models by what they actually let practitioners predict and do, rather than by how neatly they match a theory of explanation. The three conversations — psychiatrist Awais Aftab on diagnosis, Edwin Chen on training frontier models, and Dr. Bradley Love on AI-driven discovery — are framed as one connected story: the same move from checklist-style rules to distributed, predictive representations is reshaping medicine, psychiatry, and science at once. Pragmatism, in this usage, is the lens for asking whether a classification works in practice — whether it predicts, guides treatment, and holds up under use — before asking whether it perfectly maps onto underlying causes. Why the term appears here The topic explores how prediction is starting to outrun explanation in medicine and science, and where the current evidence genuinely supports AI and where it does not. That framing is pragmatist in spirit: the value of a diagnostic or scientific representation is measured against its practical consequences — accuracy of prediction, usefulness in the clinic, honest acknowledgment of limits — instead of resting on an account of what the categories truly are. Read the term this way when it shows up alongside discussion of checklists versus intuition and prediction versus explanation. Pragmatism in this material means judging ideas and classifications by their practical consequences and predictive usefulness. It frames the move from checklist-style rules to learned, distributed representations across medicine, psychiatry, and science. It carries an honest-limiting caveat: usefulness is assessed against where the evidence genuinely supports AI, and where it does not.