Deployment process for artificial intelligence applications in radiology practice

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.

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Publication Details

Journal
Physica Medica
Published
2026-07-17
DOI
https://doi.org/10.1016/j.ejmp.2026.105890
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
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article

Deployment process for artificial intelligence applications in radiology practice

Teemu Mäkelä, Suvi Syväranta, Satu I. Inkinen, Mika Kortesniemi et al.
Physica Medica
Artificial Intelligence in Healthcare and Education
article

Deployment process for artificial intelligence applications in radiology practice

Teemu Mäkelä, Suvi Syväranta, Satu I. Inkinen, Mika Kortesniemi, Juuso H. Ketola, Markus Sormaala
article en

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.

Physica MedicaVol. 148
University of Helsinki (FI), Helsinki University Hospital (FI), Hospital District of Helsinki and Uusimaa (FI)
Openalex Percentile: Top 11%
Artificial Intelligence in Healthcare and Education
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