Radiology Reimagined: Advancing Clinical Practice Through AI Innovation (2025)-"Practical Considerations to Build, Buy, and Implement AI "

"Practical Considerations to Build, Buy, and Implement AI "– Dr. Charles Kahn

Pdf Summary

This document by Dr. Charles E. Kahn, Jr. from the University of Pennsylvania provides practical guidance on building, purchasing, and implementing artificial intelligence (AI) within radiology practice. AI is rapidly transforming radiology workflows, diagnostics, and patient care, making it essential for radiologists to critically evaluate and integrate AI tools.<br /><br />Key ethical principles highlighted include beneficence, non-maleficence, autonomy, justice, transparency, explainability, and fairness in algorithm design. When purchasing AI tools, important considerations are intended clinical use, workflow integration, total cost of ownership, and vendor transparency.<br /><br />A recommended implementation strategy involves engaging stakeholders, mapping workflows, IT integration, and training radiologists. Assessing clinical accuracy entails reviewing model performance on local data, optimizing the Enhanced Detection Rate (EDR) to capture AI-driven improvements in sensitivity, and identifying “WOW” cases that significantly impact patient care or operations. Pitfalls such as false positives and negatives should be categorized to set expectations and mitigate bias. Continuous monitoring post-deployment is crucial to ensure ongoing safety, detect data drift, and revalidate models via registries, metadata tracking, and radiologist feedback.<br /><br />The ECLAIR guidelines support radiologists in evaluating commercial AI tools covering clinical relevance, regulatory compliance, financial aspects, and more. Ten critical questions radiologists should ask vendors address problem solved, benefits/risks, validation, workflow integration, IT needs, regulation, ROI, maintenance, training, and error management.<br /><br />RSNA offers a comprehensive AI Toolkit, accessible via EdCentral for members, including evaluation templates, implementation checklists, and curated resources. Additional opportunities include a Foundational Certificate in AI for Radiology to deepen understanding of AI principles and applications.<br /><br />The main takeaway is that radiologists must lead the evaluation, ethical deployment, and integration of AI technologies to improve patient care responsibly and effectively.

Keywords

Artificial Intelligence

Radiology

Ethical Principles

AI Implementation

Clinical Accuracy

Workflow Integration

ECLAIR Guidelines

AI Tool Evaluation

RSNA AI Toolkit

Radiologist Training