Center for Augmented Intelligence in Imaging Mayo Clinic Florida
Integrating and Deploying AI Models within Clinical-Imaging Workflows
Effective integrations of imaging-related (pixel- and nonpixel-based) Artificial Intelligence (AI) models into existing clinical Radiology workflows is critical since such additions can greatly impact (either positively or negatively) operational efficiencies or downstream decision making (e.g., surgery, pathology, interventions, and drug precautions) . In order to facilitate seamless integrations of imaging-AI capabilities, with minimal negative influence on existing Radiology workflows (Figure 1), the Center for Augmented Intelligence in Imaging (CAII) in Mayo Clinic Florida has developed infrastructure and modular software packages functionally compatible with MONAI  software packages (e.g., “MONAI Core” and “MONAI Deploy”).
AI-based infrastructure should be both indistinguishable from the existing IT environment and require, at most, minimal training of Radiology users (e.g., radiologists and technologists). Nevertheless, the introduction of such tools requires the fostering of trust among the users as well as beneficiaries (e.g., patients and referring clinicians).
As the leading discipline in utilizing AI in medicine, Radiology has already recognized the need for greater efficiencies in all aspects of imaging-AI application, including AI-model: development, deployment, and adaptation to real-world encounters. Unfortunately, these processes remain prohibitively time-consuming, laborious, and costly, often resulting in significant limitations to meaningful imaging-AI use (Figure 2).
Engineers, imaging scientists, and physicians working in the CAII have developed infrastructure and containerized software packages, enabling imaging-AI models to be seamlessly integrated into the existing IT environment of a busy Department of Radiology [3-9]. The necessary interfaces and packages (Figure 3) can be deployed on-prem, in-cloud, or in hybrid settings (Figure 4). The goal is to require minimal user training and IT support and foster confidence in users and beneficiaries.
CAII at Mayo Clinic Florida has developed various capabilities to streamline the integration of imaging AI models into Radiology workflows. These capabilities include:
Expert-in-the-loop AI-model deployment
On-demand model training in clinical settings
Real-time user inference-results adjudication with feedback in clinical settings
Monitoring of user satisfaction
Data collection for FDA approvals
Standards-based communication (DICOM, FHIR, HL7, IHE) between clinical systems
Standards-based data collection regarding system and model performances
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