Radiology Reimagined: Advancing Clinical Practice Through AI Innovation (2025)-"Data Orchestration - What Is It and Why Is It Important?"

"Data Orchestration - What Is It and Why Is It Important?" – Dr. Nina Kottler

Pdf Summary

The presentation by Nina Kottler, MD, focuses on the critical role of data orchestration in radiology AI workflows. Data orchestration refers to the precise, context-driven management and routing of imaging data to ensure AI models receive the correct inputs at the optimal time and clinical setting. Key lessons emphasize that the right study, series, images, and patient data must be sent to the appropriate AI model depending on the clinical question—highlighting that sending follow-up or irrelevant studies can waste resources and impact billing, especially in outpatient settings.<br /><br />Challenges arise because radiology imaging data is largely unstructured and metadata (e.g., DICOM tags) can be inaccurate or inconsistent, making rule-based orchestration insufficient. Series names and metadata cannot be fully trusted, requiring smarter orchestration systems that consider image content and clinical context. The process balances turnaround time (TAT) and image quality to optimize AI outputs without sacrificing workflow efficiency.<br /><br />Mistakes in data orchestration can lead to AI inaccuracies, delayed diagnosis, or screening errors. Dr. Kottler stresses that effective data orchestration is the “hidden engine” powering reliable AI integration in radiology and directly influences diagnostic quality. Radiology practices should inquire about vendors’ data orchestration strategies to ensure robust AI deployment.<br /><br />In summary, data orchestration involves moving the right imaging data of the right patient, to the right AI model, in the right clinical setting, at the right time. It is essential for improving AI reliability and thereby enhancing patient care quality in radiology. This context-aware approach to data handling is foundational to unlocking AI’s full potential in clinical radiology workflows.

Keywords

Data Orchestration

Radiology AI Workflows

Imaging Data Management

AI Model Inputs

Clinical Context

Unstructured Imaging Data

Metadata Challenges

Turnaround Time Optimization

Diagnostic Accuracy

AI Integration in Radiology