Charles Kahn

Charles Kahn is a Professor of Radiology with expertise in Artificial Intelligence in Radiology, Medical Imaging Informatics, and Clinical Decision Support Systems. His work focuses on integrating AI into clinical workflows, standardizing medical imaging practices, and developing ontologies to enhance healthcare interoperability.

His research spans:

• AI-driven radiology reporting systems
• Automated imaging analysis using deep learning
• Language model applications for clinical guidelines
• Standardization frameworks for medical AI
• Ontology-based diagnostics and data integration

Key trends in his recent work include the development of vision-language models for 3D CT representations, automated integration of AI results into radiology reports, and strategies to reduce bias in healthcare AI systems. His publications emphasize rigorous validation, reproducibility, and clinical adoption of AI tools.

Scientific contributions and awards include:

• Fellow of the American College of Radiology (FACR)
• Fellow of the American College of Medical Informatics (FACMI)
• CHECKLIST FOR ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING (CLAIM)
• PRISMA AI reporting guidelines
• QUADAS-AI quality assessment tool

He has mentored radiologists and imaging scientists, contributed to AI education, and addressed challenges in clinical AI deployment through collaborative panels with RSNA and MICCAI experts.