Overall, the MTCNNs outperformed all other AI models. Based on retrospective analysis, the hard parameter model (59.04%) and cross-stitch model (57.93%) correctly classified a higher percentage of pathology reports than the other models, which ranged from 36.75% to 53.68%. A prospective analysis of the two MTCNNs also resulted in a superior performance (60.11% for the hard parameter model, 58.13% for the cross-stitch model) compared to the other models.
So what’s next for these researchers?
“The next step is to launch a large-scale user study where the technology will be deployed across cancer registries to identify the most effective ways of integration in the registries’ workflows,” Tourassi said in the same ORNL statement. “The goal is not to replace the human but rather augment the human.”
