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Charles E Kahn

Charles E Kahn

University of Pennsylvania · Rehabilitation Medicine

Active 1964–2026

h-index38
Citations6.0k
Papers45670 last 5y
Funding

Academic metrics are sourced from OpenAlex and public funding records; values may differ from Google Scholar.

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About

Charles E Kahn Jr., MD, MS, FACR, is a Professor of Radiology at the Hospital of the University of Pennsylvania. He is an active member of the medical staff in the Department of Radiology at Chester County Hospital, Penn Presbyterian Medical Center, and Pennsylvania Hospital. Dr. Kahn serves as the Vice Chair of the Department of Radiology at the University of Pennsylvania. His professional focus includes radiology and medical imaging, with involvement in academic and clinical activities at the University of Pennsylvania.

Research topics

  • Political Science
  • Medicine
  • Medical emergency

Selected publications

  • Metrics reloaded: recommendations for image analysis validation

    Nature Methods · 2024-02-01 · 392 citations

    reviewOpen access
  • Artificial intelligence and machine learning in cancer imaging

    Communications Medicine · 2022-10-27 · 301 citations

    reviewOpen access

    An increasing array of tools is being developed using artificial intelligence (AI) and machine learning (ML) for cancer imaging. The development of an optimal tool requires multidisciplinary engagement to ensure that the appropriate use case is met, as well as to undertake robust development and testing prior to its adoption into healthcare systems. This multidisciplinary review highlights key developments in the field. We discuss the challenges and opportunities of AI and ML in cancer imaging;…

  • To buy or not to buy—evaluating commercial AI solutions in radiology (the ECLAIR guidelines)

    European Radiology · 2021-03-05 · 160 citations

    articleOpen access

    Artificial intelligence (AI) has made impressive progress over the past few years, including many applications in medical imaging. Numerous commercial solutions based on AI techniques are now available for sale, forcing radiology practices to learn how to properly assess these tools. While several guidelines describing good practices for conducting and reporting AI-based research in medicine and radiology have been published, fewer efforts have focused on recommendations addressing the key quest…

  • Why Is the Electronic Health Record So Challenging for Research and Clinical Care?

    Methods of Information in Medicine · 2021 · 110 citations

    BACKGROUND: The electronic health record (EHR) has become increasingly ubiquitous. At the same time, health professionals have been turning to this resource for access to data that is needed for the delivery of health care and for clinical research. There is little doubt that the EHR has made both of these functions easier than earlier days when we relied on paper-based clinical records. Coupled with modern database and data warehouse systems, high-speed networks, and the ability to share clinic…

  • Automated Integration of AI Results into Radiology Reports Using Common Data Elements

    Journal of Imaging Informatics in Medicine · 2025-01-27 · 13 citations

    articleOpen accessSenior author

    Integration of artificial intelligence (AI) into radiology practice can create opportunities to improve diagnostic accuracy, workflow efficiency, and patient outcomes. Integration demands the ability to seamlessly incorporate AI-derived measurements into radiology reports. Common data elements (CDEs) define standardized, interoperable units of information. This article describes the application of CDEs as a standardized framework to embed AI-derived results into radiology reports. The authors de…

Frequent coauthors

  • Kahn Ce

    186 shared
  • Carl T. Wittwer

    University of Utah

    64 shared
  • Gregory Tsongalis

    64 shared
  • Greg Miller

    West Virginia University

    64 shared
  • David E. Bruns

    64 shared
  • Yi‐Ju Li

    64 shared
  • Rossa W. K. Chiu

    Prince of Wales Hospital

    64 shared
  • Edwin Ullman

    Virginia Commonwealth University

    64 shared

Labs

  • Charles E Kahn LabPI

Education

  • MS, Computer Sciences

    University of Wisconsin Madison

    2003
  • MD, College of Medicine

    University of Illinois at Chicago

    1985
  • BA, Mathematics

    University of Wisconsin Madison

    1981

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