AI in medical physics: guidelines for publication.

Journal: Medical physics
Published Date: Sep 1, 2021

Abstract

The Abstract is intended to provide a concise summary of the study and its scientific findings. For AI/ML applications in medical physics, a problem statement and rationale for utilizing these algorithms are necessary while highlighting the novelty of the approach. A brief numerical description of how the data are partitioned into subsets for training of the AI/ML algorithm, validation (including tuning of parameters), and independent testing of algorithm performance is required. This is to be followed by a summary of the results and statistical metrics that quantify the performance of the AI/ML algorithm.

Authors

  • Issam El Naqa
    Department of Machine Learning, Moffitt Cancer Center, Tampa, Florida.
  • John M Boone
    Department of Radiology and Biomedical Engineering, University of California Davis Health, 4860 "Y" street, suite 3100 Ellison building, Sacramento, CA, 95817, USA.
  • Stanley H Benedict
    Department of Radiation Oncology, University of California Davis Medical Center, Sacramento, CA, United States.
  • Mitchell M Goodsitt
    Department of Radiology, University Michigan, 1500 E Medical Center Dr, Ann Arbor, MI, 48109, USA.
  • Heang-Ping Chan
    Department of Radiology, University of Michigan, Ann Arbor, Michigan.
  • Karen Drukker
    Department of Radiology, University of Chicago, Chicago, IL, 60637, USA.
  • Lubomir Hadjiiski
    Department of Radiology, The University of Michigan, Ann Arbor, Michigan 48109-0904.
  • Dan Ruan
    Departments of Radiation Oncology, Biomedical Physics and Bioengineering, UCLA, Los Angeles, CA, 90095, USA.
  • Berkman Sahiner
    Food and Drug Administration/CDRH, Silver Spring, USA.

Keywords

External Resources

Popular Topics
Recent Journals