Deployment process for artificial intelligence applications in radiology practice.
Authors
Affiliations (3)
Affiliations (3)
- HUS Diagnostic Center, Radiology, Helsinki University and Helsinki University Hospital, Haartmaninkatu 4, 00290 Helsinki, Finland. Electronic address: [email protected].
- HUS Diagnostic Center, Radiology, Helsinki University and Helsinki University Hospital, Haartmaninkatu 4, 00290 Helsinki, Finland.
- HUS Diagnostic Center, Radiology, Helsinki University and Helsinki University Hospital, Haartmaninkatu 4, 00290 Helsinki, Finland; Department of Physics, University of Helsinki, P.O. Box 64, 00014 Helsinki, Finland.
Abstract
Successful deployment of medical artificial intelligence (AI) systems should start with formulating clear goals and understanding organisational workflows. Comprehensive deployment planning is advised after identifying a suitable product, preliminary testing, and procurement. A structured approach supports effective adoption and improved efficiency. Planning involves evaluating the feasibility of integrating the AI into local clinical practice and creating a roadmap. Implementation is based on the specific use case and expected clinical outcomes. Defining stakeholder roles and establishing communication strategies facilitate collaboration between clinical, technical, and administrative teams. Key considerations include resource allocation, integration with hospital information systems, and compliance with regulatory requirements. Impact assessments, including Health Technology Assessment, Data Protection Impact Assessment, and AI risk evaluation, ensure patient safety and legal compliance. Planning involves outlining quality assurance (QA) protocols using clinically relevant key performance indicators (KPIs). The deployment phase focuses on preparing the AI system and clinical environment, including system installation, interoperability testing, security checks, cross-department communication, user training, workflow alignment, and initialization of QA monitoring. The rollout phase begins once preparations are completed and involves introducing the AI system into the workflow. A phased rollout and a small-scale pilot help to identify integration issues early with minimal workflow disruptions. In the follow-up phase, the performance of the AI system is monitored using QA parameters, user feedback, KPIs, and periodic effectiveness evaluations. A structured deployment of medical AI guided with technical, clinical, and operational preparation not only enhances patient safety and compliance but also ensures sustainable integration with measurable improvements in clinical practice.