  Radiology AI models have made significant progress in near-human performance
or surpassing it. However, AI model's partnership with human radiologist
remains an unexplored challenge due to the lack of health information
standards, contextual and workflow differences, and data labeling variations.
To overcome these challenges, we integrated an AI model service that uses DICOM
standard SR annotations into the OHIF viewer in the open-source LibreHealth
Radiology Information Systems (RIS). In this paper, we describe the novel
Human-AI partnership capabilities of the platform, including few-shot learning
and swarm learning approaches to retrain the AI models continuously. Building
on the concept of machine teaching, we developed an active learning strategy
within the RIS, so that the human radiologist can enable/disable AI annotations
as well as "fix"/relabel the AI annotations. These annotations are then used to
retrain the models. This helps establish a partnership between the radiologist
user and a user-specific AI model. The weights of these user-specific models
are then finally shared between multiple models in a swarm learning approach.
