High Stakes: Where Most Healthcare AI/ML Deployments Go Wrong
Read the white paper to explore three of the most common ways healthcare AI/ML models go wrong, and how you can ensure they go well.
More than ever before, artificial intelligence/machine learning (AI/ML) models have the potential to improve healthcare and decision-making; and the stakes are high. Actions taken or not taken based on predictions will have an impact on people’s health. Systemwide decision-making informed by tools working at a suboptimal level can result in missed opportunities to improve health outcomes, or even exacerbate health disparities. Considering what’s at stake, can data scientists accept a predictive model deployment rate of only 1 in 10?
Let’s explore three of the most common ways healthcare AI/ML models go wrong, and how you can ensure they go well.
- Data Quality
- Shifts in Underlying Data
- Ever-Changing Healthcare Terminologies