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/onyedikachi_onwurah_00ba3
Feedcoyote: https://feedcoyote.com/onyedikachi-ikenna-onwurah
Facebook: https://www.facebook.com/61587376550475/
LinkedIn: www.linkedin.com/in/onyedikachi-ikenna-onwurah-0a8523162
Open to remote healthcare AI collaborations.
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