World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
35
Citations
5899
World Ranking
11613
National Ranking
4763

Overview

Charles E. Kahn is affiliated with the University of Pennsylvania in the United States. Their research predominantly focuses on Medicine, with particular expertise in the subfields of Radiology, Nuclear Medicine and Imaging, Health Informatics, Artificial Intelligence, Biomedical Engineering, and Molecular Biology.

Their work spans several specialized topics, including:

  • Artificial Intelligence in Healthcare and Education
  • Radiomics and Machine Learning in Medical Imaging
  • Radiology practices and education
  • AI in cancer detection
  • Machine Learning in Healthcare
  • Biomedical Text Mining and Ontologies
  • Medical Imaging and Analysis

Charles E. Kahn has contributed frequently to various publication venues, with a significant number of their papers appearing in:

  • Radiology Artificial Intelligence
  • arXiv (Cornell University)
  • Radiology
  • Journal of the American College of Radiology
  • Studies in health technology and informatics

Their recent papers include the following:

  • "Checklist for Artificial Intelligence in Medical Imaging (CLAIM): A Guide for Authors and Reviewers" (2020, Radiology Artificial Intelligence)
  • "Metrics reloaded: recommendations for image analysis validation" (2024, Nature Methods)
  • "Artificial intelligence and machine learning in cancer imaging" (2022, Communications Medicine)
  • "A quality assessment tool for artificial intelligence-centered diagnostic test accuracy studies: QUADAS-AI" (2021, Nature Medicine)
  • "Checklist for Artificial Intelligence in Medical Imaging (CLAIM): 2024 Update" (2024, Radiology Artificial Intelligence)

Frequent collaborators of Charles E. Kahn include:

  • Linda Moy
  • Tessa S. Cook
  • Hersh Sagreiya
  • Walter R. Witschey
  • James C. Gee

Best Publications

  • Checklist for Artificial Intelligence in Medical Imaging (CLAIM): A Guide for Authors and Reviewers.

    John Mongan;Linda Moy;Charles E. Kahn

  • Construction of a Bayesian network for mammographic diagnosis of breast cancer

    Charles E. Kahn;Linda M. Roberts;Katherine A. Shaffer;Peter Haddawy

  • Automatic segmentation of liver structure in CT images

    Kyongtae T. Bae;Maryellen L. Giger;Chin-Tu Chen;Charles E. Kahn

  • A quality assessment tool for artificial intelligence-centered diagnostic test accuracy studies : QUADAS-AI

    Viknesh Sounderajah;Hutan Ashrafian;Sherri Rose;Nigam H. Shah

  • Informatics in radiology: comparison of logistic regression and artificial neural network models in breast cancer risk estimation.

    Turgay Ayer;Jagpreet Chhatwal;Oguzhan Alagoz;Charles E. Kahn

  • Breast cancer risk estimation with artificial neural networks revisited: discrimination and calibration.

    Turgay Ayer;Oguzhan Alagoz;Jagpreet Chhatwal;Jude W. Shavlik

  • Overview of the CLEF 2009 medical image retrieval track

    Henning Müller;Jayashree Kalpathy-Cramer;Ivan Eggel;Steven Bedrick

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

    Patrick Omoumi;Alexis Ducarouge;Antoine Tournier;Hugh Harvey

  • Artificial intelligence in radiology: decision support systems.

    Kahn Ce

  • Hepatic helical CT: contrast material injection protocol.

    W D Foley;R G Hoffmann;F A Quiroz;C E Kahn

  • Common Data Elements in Radiology.

    Daniel L. Rubin;Charles E. Kahn

  • DICOM and radiology: past, present, and future.

    Charles E. Kahn;John A. Carrino;Michael J. Flynn;Donald J. Peck

  • Probabilistic Computer Model Developed from Clinical Data in National Mammography Database Format to Classify Mammographic Findings

    Elizabeth S. Burnside;Jesse Davis;Jesse Davis;Jagpreet Chhatwal;Oguzhan Alagoz

  • GoldMiner: A Radiology Image Search Engine

    Charles E. Kahn;Cheng Thao

  • Actionable Findings and the Role of IT Support: Report of the ACR Actionable Reporting Work Group

    Paul A. Larson;Lincoln L. Berland;Brent Griffith;Charles E. Kahn

  • A logistic regression model based on the national mammography database format to aid breast cancer diagnosis

    Jagpreet Chhatwal;Oguzhan Alagoz;Mary J. Lindstrom;Charles E. Kahn

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

    John H. Holmes;James Beinlich;Mary R. Boland;Kathryn H. Bowles

  • Overview of the CLEF 2010 medical image retrieval track

    Henning Müller;Henning Müller;Jayashree Kalpathy-Cramer;Ivan Eggel;Steven Bedrick

  • From Images to Actions: Opportunities for Artificial Intelligence in Radiology.

    Charles E Kahn

  • Overview of the ImageCLEFmed 2008 medical image retrieval task

    Henning Müller;Jayashree Kalpathy-Cramer;Charles E. Kahn;William Hatt

  • How users search and what they search for in the medical domain

    João Palotti;Allan Hanbury;Henning Müller;Charles E. Kahn

  • Health status assessment via the World Wide Web.

    D. S. Bell;C. E. Kahn

Frequent Co-Authors

Henning Müller
Henning Müller University of Applied Sciences and Arts Western Switzerland
Peter Haddawy
Peter Haddawy Mahidol University
Daniel L. Rubin
Daniel L. Rubin Stanford University
Jayashree Kalpathy-Cramer
Jayashree Kalpathy-Cramer Harvard University
William R. Hersh
William R. Hersh Oregon Health & Science University
Ben Glocker
Ben Glocker Imperial College London
Ronald M. Summers
Ronald M. Summers National Institutes of Health
Lena Maier-Hein
Lena Maier-Hein German Cancer Research Center
Pamela S. Douglas
Pamela S. Douglas Duke University
Klaus H. Maier-Hein
Klaus H. Maier-Hein German Cancer Research Center

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Studying Computer Science in the USA offers a wide range of online degree options to suit different needs and backgrounds. For those starting their academic journey, online associate degree programs provide a flexible and cost-effective introduction to computer science concepts. These programs can help students quickly build foundational skills and transition into a bachelor’s degree or entry-level tech roles.

Affordability is often a major concern for students. Exploring the cheapest online degrees can help lower the financial barrier while still providing quality education. Additionally, some students may worry about past academic performance. Fortunately, there are college that accepts low gpa, making higher education accessible to a wider range of applicants.

Computer Science skills pave the way for versatile careers—not only in tech but in interdisciplinary fields. For example, if your interests also lie in sustainability, consider what you can achieve with an environmental science degree. Combining expertise opens doors in emerging industries and broadens your career opportunities.

Best Scientists Citing Charles E. Kahn

Trending Scientists

Recently Published Articles