Radiology Reimagined: Advancing Clinical Practice Through AI Innovation (2025)-"How Should We Guide AI? How will AI Change Us?"

"How Should We Guide AI? How will AI Change Us?" – Dr. Charles Kahn

Pdf Summary

This article by Charles E. Kahn, Jr., MD, MS from the University of Pennsylvania discusses guiding principles and considerations for the design, deployment, and future of AI in radiology and healthcare. Radiology is at the forefront of AI use with many FDA-cleared tools, promising increased efficiency, accuracy, and reduced clinician burnout. However, successful integration requires more than good algorithms; it demands addressing clinical, cultural, computational, and regulatory factors.<br /><br />Key guiding principles for trustworthy AI include fairness, universality, traceability, usability, robustness, and explainability. Clinically, strategies like federated learning can overcome data limitations while maintaining privacy. Annotation remains costly but can be aided by active and self-supervised learning. Bias arises from underrepresented data, so continuous performance monitoring is vital. AI tools should be well integrated into clinical workflows (e.g., PACS) to reduce distractions and improve safety.<br /><br />Culturally, AI changes traditional practices by enabling volumetric analysis, risk prediction, and integrating radiology with other medical data like genomics. Radiologists retain accountability and trust requires explainable AI and local validation. Computationally, cloud computing benefits resource-limited settings, and clinical accuracy outweighs speed. Data transfer from scanners often limits throughput.<br /><br />Regulatory landscapes currently favor radiology AI but lag for generative AI and often miss robust post-market surveillance. Solutions include phantom datasets and automated retraining. Collaborative initiatives, such as RSNA's ROADMAP, bring together professional societies to unify goals, ethics, and education, fostering progress through hackathons and challenges.<br /><br />Looking forward, emerging foundation models and multimodal AI should focus on solving real clinical problems with radiologists leading AI development. The article encourages ongoing involvement and learning through RSNA resources.

Keywords

AI in radiology

FDA-cleared AI tools

trustworthy AI principles

federated learning

active learning

bias in AI

clinical workflow integration

explainable AI

cloud computing in healthcare

RSNA ROADMAP initiative