When Correlation Misleads Healthcare AI

Modern healthcare AI models identify statistical relationships at scale. But medicine operates on causal reasoning. A variable that predicts deterioration does not necessarily cause deterioration. After 12 years in pharmacy practice, I learned that interventions must target modifiable drivers, not simply markers. With further training in public health and precision medicine data science, I now see the tension between predictive strength and intervention logic. Confusing correlation with causation can: • Increase cost without benefit • Divert attention from true drivers • Amplify inequity • Reinforce feedback loops Responsible healthcare AI requires: • Clinical interpretation of features • Modifiability assessment • Temporal awareness • Prospective impact evaluation Prediction improves foresight. Causal reasoning improves outcomes. Healthcare AI maturity lies in combining both. You can follow my broader work here: Medium: https://medium.com/@fora12.12am Dev.to: https://dev.to/onyedikach