If data used to train artificial intelligence models for medical applications, such as hospitals across the Greater Toronto Area, differs from the real-world data, it could lead to patient harm. A new study published today in JAMA Network Open from York University found proactive, continual and transfer learning strategies for AI models to be key in mitigating data shifts and subsequent harms.
Specific learning strategies can enhance AI model effectiveness in hospitals
