🤖
Machine Learning in Healthcare: A Developer's Perspective
Lessons from medical imaging work: data quality, model selection, and evaluation.
Data quality first
Label consistency, de-identification, and bias checks matter more than fancy models. Spend time on robust pipelines.
Right metric, right task
Use clinically meaningful metrics. AUC isn’t always enough—think sensitivity/specificity trade-offs and calibration.
Deployment considerations
Latency, explainability, and monitoring: ship models with dashboards and fallback paths.