By Alireza Minagar, MD, MBA, MS (Software Engineering), MS (Bioinformatics)
A machine-learning model can perform well in validation and still fail inside a hospital.
The problem may not be the algorithm. It may be incomplete production data, poor integration, alert fatigue, model drift, or a prediction reaching the wrong clinician at the wrong time.
Healthcare has become increasingly capable of building AI models. It remains much less effective at making them work safely in actual clinical environments.
That is why medicine needs forward-deployed AI engineers.
