While artificial intelligence can be used to detect many types of cancer, figuring out when to rely on experts versus the algorithms of AI is still tricky. It’s not simply a matter of who is “better” at making a diagnosis or prediction. Factors like how much time medical professionals have and their level of expertise also come into play.
To address this, researchers from MIT’s Computer Science and Artificial Intelligence Lab (CSAIL) developed a machine learning system that can decide to either make a prediction or defer to an expert. Most importantly, the system can adapt when and how often it defers to a human expert, based on that teammate’s availability, experience, and scope of practice.
For instance, in a busy hospital setting, the system may ask for human assistance only when it’s absolutely necessary. The researchers trained the system on multiple tasks, including looking at chest X-rays to diagnose conditions like a collapsed lung. When asked to diagnose cardiomegaly (an enlarged heart), the human-AI hybrid model performed eight percent better than either the AI or medical professionals could on their own.
Next, the researchers will test a system that works with and defer to several experts at once. For instance, the AI might collaborate with different radiologists who are more experienced with different patient populations.
