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Radiology AI models have made significant progress in near-human performanceor surpassing it. However, AI model's partnership with human radiologistremains an unexplored challenge due to the lack of health informationstandards, contextual and workflow differences, and data labeling variations.To overcome these challenges, we integrated an AI model service that uses DICOMstandard SR annotations into the OHIF viewer in the open-source LibreHealthRadiology Information Systems (RIS). In this paper, we describe the novelHuman-AI partnership capabilities of the platform, including few-shot learningand swarm learning approaches to retrain the AI models continuously. Buildingon the concept of machine teaching, we developed an active learning strategywithin the RIS, so that the human radiologist can enable/disable AI annotationsas well as "fix"/relabel the AI annotations. These annotations are then used toretrain the models. This helps establish a partnership between the radiologistuser and a user-specific AI model. The weights of these user-specific modelsare then finally shared between multiple models in a swarm learning approach.

A general-purpose AI assistant embedded in an open-source radiology information system